On the Asaithambi-Zuhe-Moore algorithm for computing the range of values

Asaithambi, Zuhe, and Moore (Computing 28, 225–237, 1982) presented an algorithm to find good upper and lower bounds on the range of values of a function of several variables on an n- dimensional rectangle. Unfortunately, the algorithm presented there contains some errors. In this paper, we point out these errors, show how they may be corrected, and suggest improvements of the algorithm.     

Optical detection of CA 15.3 breast cancer antigen using CdS quantum dot

The present study focus on optical sensing of breast cancer antigen 15.3 (CA 15.3) using cadmium sulphide quantum dot (CdS‐QD) in saline and serum samples spiked with antigen. The surface of CdS‐QD was modified by cysteamine capping followed by tagging of CA 15.3 antibody. The samples were characterised using UV‐visible absorption spectroscopy (UV‐VIS Spectroscopy), Fourier transform infrared spectroscopy (FTIR), high‐resolution transmission electron microscopy (HRTEM) attached with energy‐dispersive X‐ray spectroscopy, phase contrast inverted epi‐fluorescence microscopy and photoluminescence (PL) spectrophotometry (EDS). The CdS‐QD showed a mean diameter of 3.02 ± 0.6 nm. The complex formed after antigen‐antibody interaction resulted in distinguishable optical and fluorescence intensity with respect to varying concentration of antigen. The PL study revealed that CA 15.3 antibody labelled CdS QD can detect CA 15.3 tumour marker even at very low concentration of 0.002 KU/L with a constant response time of 15 min. This study clearly indicates that detection of CA 15.3 at low concentration is possible using surface modified CdS QD in serum samples and can find immense applications in biosensor development for detection of breast cancer marker similar to various automated detection kits available in market.Inspec keywords: semiconductor quantum dots, cadmium compounds, II‐VI semiconductors, wide band gap semiconductors, cancer, tumours, optical sensors, biosensors, biomedical equipment, visible spectra, ultraviolet spectra, Fourier transform infrared spectra, transmission electron microscopy, X‐ray chemical analysis, fluorescence, optical microscopy, photoluminescence, proteins, molecular biophysics, nanosensors, nanomedicine, nanoparticlesOther keywords: optical detection, CA 15.3 breast cancer antigen, optical sensing, cadmium sulphide quantum dot, saline samples, serum samples, cysteamine capping, CA 15.3 antibody, UV‐visible absorption spectroscopy, Fourier transform infrared spectroscopy, high‐resolution transmission electron microscopy, energy dispersive X‐ray spectroscopy, phase contrast inverted epifluorescence microscopy, photoluminescence spectrophotometry, antigen‐antibody interaction, fluorescence intensity, optical intensity, CA 15.3 tumour marker, surface modified CdS QD, biosensor development, time 15 min, CdS     

Simultaneous automated design of structured QFT controller and prefilter using nonlinear programming

This paper describes a nonlinear programming‐based robust design methodology for controllers and prefilters of a predefined structure for the linear time‐invariant systems involved in the quantitative feedback theory. This controller and prefilter synthesis problem is formulated as a single optimization problem with a given performance optimization objective and constraints enforcing stability and various specifications usually enforced in the quantitative feedback theory. The focus is set on providing constraints expression that can be used in standard nonlinear programming solvers. The nonlinear solver then computes in a single‐step controller and prefilter design parameters that satisfy the prescribed constraints and maximizes the performance optimization objective. The effectiveness of the proposed approach is demonstrated through a variety of difficult design cases like resonant plants, open‐loop unstable plants, and plants with variation in the time delay. Copyright © 2016 John Wiley & Sons, Ltd.     

On-line voltage stability assessment using radial basis function network model with reduced input features

In recent years, voltage instability has become a major threat for the operation of many power systems. This paper presents an artificial neural network (ANN)-based approach for on-line voltage security assessment. The proposed approach uses radial basis function (RBF) networks to estimate the voltage stability level of the system under contingency state. Maximum L-index of the load buses in the system is taken as the indicator of voltage stability. Pre-contingency state power flows are taken as the input to the neural network. The key feature of the proposed method is the use of dimensionality reduction techniques to improve the performance of the developed network. Mutual information based technique for feature selection is proposed to enhance overall design of neural network. The effectiveness of the proposed approach is demonstrated through voltage security assessment in IEEE 30-bus system and Indian practical 76 bus system under various operating conditions considering single and double line contingencies and is found to predict voltage stability index more accurate than feedforward neural networks trained by back propagation algorithm and AC load flow. Experimental results show that the proposed method reduces the training time and improves the generalization capability of the network than the multilayer perceptron networks.     

α-Glucosidase, α-amylase inhibition kinetics,in vitro gastro-intestinal digestion,and apoptosis inducing abilities of Ficus microcarpa L. f. and Ficus racemosa L. fruit polysaccharides

Food Science and Biotechnology - A rich source of nutrients, figs have a number of clinically validated benefits. This study aimed to evaluate the in vitro simulated gastrointestinal digestion, and...     

Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm

Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal rule set and membership function tuning. Advanced and problem specific genetic operators are proposed to improve the convergence of GSA and classification accuracy. The performance of the proposed approach is evaluated using six gene expression data sets. From the simulation study it is found that the proposed approach generated a compact fuzzy system with high classification accuracy for all the data sets when compared with other approaches.     

An energy flow analysis in a paper-based industry

Industrial sector is the largest user of energy in India and many parts of the world. More than half of the total energy is used in industries to operate various energy-using machineries. Energy conservation is a cost-effective approach in any energy optimization program that can be implemented in industrial activities. Energy-intensive industries in developing economies offer significant energy-saving potential owing to the technology gap as well as lower operating efficiency. A detailed energy flow analysis was conducted in a paper carton manufacturing unit in India to quantify the energy-saving potential. The study revealed that the compressed air leakage is responsible for about 50% of the total energy loss. The specific electrical and thermal energy consumption was estimated and found to be 91.85?kWh/ton and 1,619?MJ/ton of biomass, respectively. Annual energy saving potential was found to be 5.9% of the total annual energy consumption. The cost of DG power is found to be 389% higher than the grid power. The share of self-generated DG power is estimated to be 41% of the total power consumed. Electrical energy intensity of the plant could be reduced by 6.36% implementing energy conservation measures proposed in this study.     

Lymphatic Transport of α-Linolenic Acid and Its Conversion to Long Chain n-3 Fatty Acids in Rats Fed Microemulsions of Linseed Oil

In the present study we evaluated the uptake of ALA and its conversion to EPA + DHA in rats given linseed oil (LSO) in native form or as a microemulsion in whey protein or in lipoid. In a single oral dose study in which rats maintained on rodent pellets deficient in ω-3 fatty acids were intubated with 0.35 g LSO in lipoid, the amount of ALA present in lymph was increased reaching a maximum concentration of 16.23 mg/ml at 2.5 h. The amount of ALA present in lymph was increased to a maximum level of 10.95 mg/ml at 4 h in rats given LSO as a microemulsion in whey protein. When LSO was given without emulsification, the amount of ALA present in lymph was found to reach a maximum level of 7.08 mg/ml at 6 h. A similar result was observed when weaning rats were intubated with 0.15 g of LSO per day for a period of 60 days. Higher levels of ALA by 41 and 103 % were observed in lymph lipids of rats given microemulsions of LSO in whey protein and in lipoid respectively as compared to rats given LSO without pre-emulsification. Very little conversion of ALA to EPA and DHA was observed in lymph lipids but higher amounts of EPA + DHA was observed in liver and serum of rats given LSO in microemulsion form. This study indicated that ALA concentration in lymph lipids was increased when LSO was given in microemulsion form in lipoid and further conversion to EPA and DHA was facilitated in liver and serum.     

Novel Role of Ghrelin Receptor in Gut Dysbiosis and Experimental Colitis in Aging

Chronic low-grade inflammation is a hallmark of aging, which is now coined as inflamm-aging. Inflamm-aging contributes to many age-associated diseases such as obesity, type 2 diabetes, cardiovascular disease, and inflammatory bowel disease (IBD). We have shown that gut hormone ghrelin, via its receptor growth hormone secretagogue receptor (GHS-R), regulates energy metabolism and inflammation in aging. Emerging evidence suggests that gut microbiome has a critical role in intestinal immunity of the host. To determine whether microbiome is an integral driving force of GHS-R mediated immune-metabolic homeostasis in aging, we assessed the gut microbiome profiles of young and old GHS-R global knockout (KO) mice. While young GHS-R KO mice showed marginal changes in Bacteroidetes and Firmicutes, aged GHS-R KO mice exhibited reduced Bacteroidetes and increased Firmicutes, featuring a disease-susceptible microbiome profile. To further study the role of GHS-R in intestinal inflammation in aging, we induced acute colitis in young and aged GHS-R KO mice using dextran sulfate sodium (DSS). The GHS-R KO mice showed more severe disease activity scores, higher proinflammatory cytokine expression, and decreased expression of tight junction markers. These results suggest that GHS-R plays an important role in microbiome homeostasis and gut inflammation during aging; GHS-R suppression exacerbates intestinal inflammation in aging and increases vulnerability to colitis. Collectively, our finding reveals for the first time that GHS-R is an important regulator of intestinal health in aging; targeting GHS-R may present a novel therapeutic strategy for prevention/treatment of aging leaky gut and inflammatory bowel disease.