Birla Institute of Technology, Mesra

Abstract: Flavonoids, a group of natural substances with variable phenolic structures, are found in fruits, vegetables, grains, bark, roots, stems, flowers, tea and wine. These natural products are well known for their beneficial effects on health and efforts are being made to isolate the ingredients so called flavonoids. Flavonoids are now considered as an indispensable component in a variety of nutraceutical, pharmaceutical, medicinal and cosmetic applications. This is attributed to their anti-oxidative, anti-inflammatory, anti-mutagenic and anti-carcinogenic properties coupled with their capacity to modulate key cellular enzyme function. Research on flavonoids received an added impulse with the discovery of the low cardiovascular mortality rate and also prevention of CHD. Information on the working mechanisms of flavonoids is still not understood properly. However, it has widely been known for centuries that derivatives of plant origin possess a broad spectrum of biological activity. Current trends of research and development activities on flavonoids relate to isolation, identification, characterisation and functions of flavonoids and finally their applications on health benefits. Molecular docking and knowledge of bioinformatics are also being used to predict potential applications and manufacturing by industry. In the present review, attempts have been made to discuss the current trends of research and development on flavonoids, working mechanisms of flavonoids, flavonoid functions and applications, prediction of flavonoids as potential drugs in preventing chronic diseases and future research directions.

Abstract: High-energy consumption in our day-to-day life can be balanced not only by harvesting pollution-free renewable energy sources, but also requires proper storage and distribution of energy. In this regard, lithium ion batteries are currently considered as effective energy storage devices and involve the most active research. There exist several review articles dealing with various sections of LIBs, such as the anode, the cathode, electrolytes, electrode–electrolyte interface etc. However, the anode is considered to be a crucial component affecting the performance of LIBs as evident from the tremendous amount of current research work carried out in this area. In the last few years, advancements have been focused more on the fabrication of the nanostructured anode owing to its special properties, such as high surface area, short Li+ ion diffusion path length, high electron transportation rate etc. As the work in this area is growing very fast, the present review paper deliberates the recent developments of anode materials on the nanoscale. Different types of anode materials, such as carbon-based materials, alloys, Si-based materials, transition metal oxides, and transition metal chalcogenides, with their unique physical and electrochemical properties, are discussed. Various approaches to designing materials in the form of 0, 1 and 2D nanostructures and their effect of size and morphology on their performance as anode materials in LIBs are reviewed. Moreover, the article emphasizes smart approaches for making core–shell particles, nanoheterostructures, nanocomposites or nanohybrids with the combination of electrochemically active materials and conductive carbonaceous or electrochemically inactive materials to achieve LIBs with high capacity, high rate capability, and excellent cycling stability. We believe the review paper will provide an update for the reader regarding recent progress on nanostructured anode materials for LIBs.

Abstract: A Random Forest (RF) classifier was applied to spectral as well as mono- and multi-seasonal textural features extracted from Landsat TM imagery to increase the accuracy of land cover classification over a complex Mediterranean landscape, with a large number of land cover categories and low inter-class separability. Five different types of geostatistical textural measure for three different window sizes and three different lags were applied creating a total of 972 potential input variables. Madograms, rodograms and direct variograms for the univariate case and cross- and pseudo-cross variograms for the multivariate case, together with multi-seasonal spectral information, were used in a RF classifier to map the land cover types. The pseudo-cross and cross variograms were used specifically to incorporate important seasonal/temporal information. Incorporating multi-scale textural features into the RF models led to an increase in the overall index of 10.71% and, for the most accurate classification, the increase was up to 30% in some classes. The differences in the kappa coefficient for the textural classification models were evaluated statistically using a pairwise Z-test, revealing a significant increase in per-class classification accuracy compared to GLCM-based texture measures. The pseudo-cross variogram between the visible and near-infrared bands was the most important textural features for general classification, and the multi-seasonal pseudo-cross variogram had an outstanding importance for agricultural classes. Overall, the RF classifier applied to a reduced subset of input variables composed of the most informative textural features led to the highest accuracy. Highly reliable classification results were obtained when the 16 most important textural features calculated at single scales (window sizes) were selected and used in the classification. The proposed methodology significantly increased the classification accuracy achieved with a spectral maximum likelihood classifier (ML). The kappa values for the textural RF and ML classifications were equal to 0.92 and 0.83, respectively.

Abstract: Lean and Six Sigma are two widely acknowledged business process improvement strategies available to organisations today for achieving dramatic results in cost, quality and time by focusing on process performance. Lately, Lean and Six Sigma practitioners are integrating the two strategies into a more powerful and effective hybrid, addressing many of the weaknesses and retaining most of the strengths of each strategy. Lean Sigma combines the variability reduction tools and techniques from Six Sigma with the waste and non-value added elimination tools and techniques from Lean Manufacturing, to generate savings to the bottom-line of an organisation. This paper proposes a Lean Sigma framework to reduce the defect occurring in the final product (automobile accessories) manufactured by a die-casting process. The proposed framework integrates Lean tools (current state map, 5S System, and Total Productive Maintenance (TPM)) within Six Sigma DMAIC methodology to enhance the bottom-line results and win customer loyalty. Implementation of the proposed framework shows dramatic improvement in the key metrics (defect per unit (DPU), process capability index, mean and standard deviation of casting density, yield, and overall equipment effectiveness (OEE)) and a substantial financial savings is generated by the organisation.

Abstract: This paper presents an overview of the significance of the use of molecular biomarkers as diagnostic and prognostic tools for marine pollution monitoring. In order to assess the impact of highly persistent pollutants such as polychlorinated biphenyls (PCB), polychlorinated dibenzo–dioxins (PCDD), polychlorinated dibenzo–furans (PCDF), polynuclear aromatic hydrocarbons (PAH), tributyltin (TBT) and other toxic metals on the marine ecosystem a suite of biomarkers are being extensively used worldwide. Among the various types of biomarkers, the following have received special attention: cytochrome P4501A induction, DNA integrity, acetylcholinesterase activity and metallothionein induction. These biomarkers are being used to evaluate exposure of various species of sentinel marine organisms (e.g. mussels, clams, oysters, snails, fishes, etc.) to and the effect of various contaminants (organic xenobiotics and metals) using different molecular approaches [biochemical assays, enzyme linked immuno-sorbent assays (ELISA), spectrophotometric, fluorometric measurement, differential pulsed polarography, liquid chromatography, atomic absorption spectrometry]. The induction of the biotransformation enzyme, cytochrome P4501A in fishes (Callionymus lyra, Limanda limanda, Serranus sp., Mullus barbatus) and mussels (Dreissena polymorpha) by various xenobiotic contaminants such as PCBs, PAHs, PCDs is used as a biomarker of exposure to such organic pollutants. The induction of cytochrome P4501A is involved in chemical carcinogenesis through catalysis of the covalent bonding of organic contaminants to a DNA strand leading to formation of DNA adduct. Measurement of the induction of cytochrome P4501A in terms of EROD (7-ethoxy resorufin O-deethylase) activity is successfully used as a potential biomarker of exposure to xenobiotic contaminants in marine pollution monitoring.