Assessing the sustainability of a manufacturing process using life cycle assessment technique—a case of an Indian pharmaceutical company

Clean Technologies and Environmental Policy - Manufacturing organizations are under continuous pressure to implement sustainability in their activities. There is a need to identify the...     

Digital Connectivity and Extended Well-Composed Sets for Gray Images

In this paper, we propose a new definition of digital connectivity for gray images on a 2D space for arbitrary grid systems. We extend a digital version of the Jordan curve theorem and its converse proved earlier by Rosenfeld for the rectangular grid system. We also extend in two directions the concept of well-composed sets introduced by Lateckiet al.(1995,Comput. Vision Image Understanding61,70–83). First, we extend the definition of well-composed sets from the quadratic grid system to an arbitrary grid system. Then, by using the concept of parameter-dependent connected components introduced by us in a previous work, we allow the pixels in a connected component of a well-composed set to have different gray values so that the connectivity of connected components accommodates a wider meaning.     

An algebraic method for processing colored images

An 8-valued commutative ring is developed to process colored images. Algebraic operators are defined to perform a number of operations such as the extraction of the primary colors and contours of different colors.     

Studies on jute–asphalt composites

The tensile properties of jute make it a suitable raw material for asphalt overlay (A/O) fabric. In this study, the thermal stability of jute under conditions of asphalt overlaying process was investigated and the compatibility of jute with asphalt was assessed through experimentation on jute–asphalt composites under mechanical and hygral loads. Fourier transform infra red (FTIR) spectroscopic study revealed probable chemical bonding between jute and asphalt. The test for ascertaining the capability of asphalt encasement for protecting jute against biodegradation under enzymatic attack was found positive. A 6‐month hygral treatment, of the jute–asphalt composite, showed significant increase in modulus of the material. The results indicate that although the strength of jute deteriorates by about 10% under asphalt overlaying condition, the overall tensile behavior of jute fabric–asphalt composite material is considerably superior to that of the pure jute fabric, even under biological and extended hygral loading conditions. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci 2008     

Interaction of CO with hydrous ruthenium oxide and development of a chemoresistive ambient CO sensor

Hydrated ruthenium oxide (RuO_x(OH)_y), the material of interest in this study was prepared by reaction of an aqueous solution of ruthenium chloride with base. This material was amorphous, made up of 20-50 nm particles and contains Ru(III) and Ru(IV), as determined by X-ray photoelectron spectroscopy. The conductivity of thick films of RuO_x(OH)_y decreased in the presence of CO in a background of air and this change was reversible. Infrared spectroscopy showed the formation of carbonates in the presence of CO, which disappeared upon replacement of CO with O₂. Upon heating RuO_x(OH)_y, there was a gradual conversion to crystalline RuO₂ beyond 200 °C. With these heated materials, the resistance change in the presence of CO at room temperature also gradually diminished. We propose that oxidation of CO on RuO_x(OH)_y leads to reduction of the ruthenium and a decrease in conductivity. With the conversion to crystalline RuO₂ upon heating, the material becomes metallic and conductivity changes are diminished. The change in conductivity of RuO_x(OH)_y with CO provides a convenient platform for an ambient CO sensor. Such a device also does not show interference from hydrocarbons (2000 ppm), ammonia (150 ppm), CO₂ (2000 ppm), NO (15 ppm) and NO₂ (15 ppm).     

Examination of Au/SnO2 core-shell architecture nanoparticle for low temperature gas sensing applications

Au/SnO₂ core-shell structure nanoparticles (NPs) were synthesized using two methods, microwave and conventional precipitation. In both cases, the size of the Au core was 12-18 nm and the thickness of the SnO₂ shell was 8-12 nm. The particle size of SnO₂ synthesized using the microwave and precipitation method was 3-5 nm and 1-2 nm, respectively. Upon heating to 400-600 °C, both particles maintained their core-shell morphology but the smaller SnO₂ particles prepared using the precipitation method were more sintered. The resistance changes in films of these particles were measured as a function of CO concentration. The Au/SnO₂ particles prepared using the microwave method showed higher sensor response than those prepared using the precipitation method, even providing a significant signal at testing temperatures approaching ambient conditions, thereby affording a new class of material for gas sensing. Both sets of core-shell particles showed higher sensor response than the SnO₂ nanoparticles. The role of the Au core as a catalyst in improving the adsorption and oxidation of CO gas is important for improving the low temperature response. In addition, the maintenance of the size of SnO₂ in the microwave method during sintering contributed to the higher response towards CO sensing.     

Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs

We present algorithms for iris segmentation, feature extraction and selection, and iris pattern matching. To segment the inner boundary from a nonideal iris image, we apply a level set based curve evolution approach using the edge stopping function, and to detect the outer boundary, we employ the curve evolution approach using the regularized Mumford-Shah segmentation model with an energy minimization algorithm. Daubechies wavelet transform (DBWT) is used to extract the textural features, and genetic algorithms (GAs) are deployed to select the subset of informative features by combining the valuable outcomes from the multiple feature selection criteria without compromising the recognition accuracy. To speed up the matching process and to control the misclassification error, we apply a combined approach called the adaptive asymmetrical support vector machines (AASVMs). The parameter values of SVMs are also optimized in order to improve the overall generalization performance. The verification and identification performance of the proposed scheme is validated using the UBIRIS Version 2, the ICE 2005, and the WVU datasets.     

Delineation of Groundwater Potential Zones in Arid Region of India—A Remote Sensing and GIS Approach

The present research is an attempt to find out the groundwater potential zones within an arid region of India supported by the scientific investigation of lithology, geomorphology, geohydrological characterization of geological formations and their interrelationship. Thematic layers of drainage, lithology, geomorphology, lineaments, slope, soil, digital elevation model, rainfall, landuse/land cover and well inventory have been generated by using ancillary data, digital satellite image, water level data of 90 observation wells for last 11?years (2000?C2010), litholog data along with ground truthing. The groundwater potential zones have been classified into five categories like very poor, poor, moderate, good and excellent. The potential zones were obtained by weighted overlay combination using the spatial analyst tool in ArcGIS 9.2. During weighted overlay analysis, the ranking was given for each individual parameter of each thematic map and weights were assigned according to their influence such as lithology (20?%), geomorphology (15?%), lineament density (15?%), drainage density (15?%), soil (10?%), slope (10?%), rainfall (5?%), land use and land cover (5?%) and digital elevation model (DEM) (5?%) and it was found that the potential zones in terms of very poor, poor, moderate, good and excellent zones covered 13.7?%, 42.8?%, 27.3?%, 10.8% and 5.4% respectively of the total area. The result also has been validated by yield data collected from existing sources and it confirms that the higher yield categories are falling within excellent groundwater potential zones where yield ranges from 23 to 40.3?l/s and lower values ranging from 8.1 to 10.6?l/s are falling within poor groundwater potential zones.