Optimization of Physiological Growth Conditions for Maximal Gamma-linolenic Acid Production by Cunninghamella blakesleeana-JSK2

Effects of various cultural conditions on biomass, lipid and Gamma-linolenic acid (GLA) production were investigated in the oleaginous fungus Cunninghamella blakesleeana-JSK2 isolated from soil. The GLA production was influenced by various factors such as growth condition (static and shaken), incubation time, pH, temperature, carbon and nitrogen sources. The results indicated that optimum GLA production (21 %) was obtained when the fungus was grown under shaken condition at 120 rpm for 6 days with optimum pH and temperature of 6 and 28 °C ,respectively. Glucose and potassium nitrate enhanced the GLA production. Urea and sucrose were poor substances for biomass, lipid and GLA production.     

Status of dietary fiber contents in pigmented and non-pigmented rice varieties before and after parboiling

Five different varieties of paddy (four pigmented and one non-pigmented) were shelled and milled in pre and post parboiled form, their dietary fiber contents were estimated. Under similar conditions of milling, raw rice showed a high degree of polish (DOP), 9–12 g/100 g and parboiled rice showed low DOP, 4.6–6.6 g/100 g. Dietary fiber content was high in pigmented rice, 9–10 g/100 g compared to non-pigmented, ∼6 g/100 g. Soluble fiber content in pigmented head rice (dehusked) varied from 1 to 1.5 g/100 g and in its brokens varied from 0.45 to 1.45 g/100 g. Dietary fiber content was low by about 1% in parboiled rice. In the parboiled rice of pigmented varieties, the total fiber content varied from 7.95 ± 0.15 to 9.05 ± 0.25 g/100 g and the soluble fiber content varied from 0.7 to 0.9 g/100 g. In milled parboiled rice the respective values were 5 ± 0.4 to 6 ± 0.1 g/100 g and 0.85 ± 0.05 to 1.25 ± 0.05 g/100 g. However, the soluble fiber content in the non-pigmented brown rice, IR-64 remained same after parboiling, 0.75 ± 0.5 g/100 g. Milled parboiled rice showed higher soluble dietary fiber compared to milled raw rice. In conclusion, dietary fiber was high in pigmented rice varieties when compared with non-pigmented rice.     

Copolymers of aniline with o‐ and m‐toluidine: synthesis and characterization

Copolymers of aniline and toluidine were synthesized by oxidative chemical polymerization using different ratios of the monomers in the feed, and characterized by a number of techniques including UV–visible, IR, Raman, ¹H NMR and EPR spectroscopies, as well as by thermogravimetric analysis and conductivity measurements. The properties of the copolymers are influenced by the amount of toluidine in the copolymer. Poly(o‐toluidine) and poly(m‐toluidine) are noticeably different in their solubility and conductivity. The copolymers show better solubilities than polyaniline but have lower conductivities. Differences in the properties of the salt and base forms of the copolymers are pointed out. Copyright © 2003 Society of Chemical Industry     

Web-log-driven business activity monitoring

Business process transformation defines a new level of business optimization that manifests as a range of industry-specific initiatives that bring processes, people, and information together to optimize efficiency. This new optimization level is possible because the Web has assumed the role of a common infrastructure. To examine how BPT can optimize an organization's processes, we describe a corporate initiative that was developed within IBM's supply chain organization to transform the import compliance process that supports the company's global logistics. The initiative sought to give IBM greater awareness of regulatory compliance exceptions - information critical to the corporation and its importing partners.     

Impact of nickel doping on structural,optical, electrical,and third-order NLO behavior of piperazinium L-tartrate single crystals

Slow evaporation technique was used to grow single crystals of pure and nickel (Ni)-doped piperazinium L-tartrate (PPLT). Powder crystal X-ray diffraction was used to characterize the structural properties of the grown crystals. Fourier transform infrared spectroscopy was used to determine the functional groups. In the visible band, both pure and Ni-doped PPLT crystals have low absorbance, indicating their utility in nonlinear optical (NLO) applications. PPLT crystals, both pure and Ni doped, have bandgap energies that indicate their insulating nature, indicating their utility in electronic applications. The growth pattern and dislocation density of the crystal are revealed by etching analysis. The electric field response of generated single crystals was investigated in terms of dielectric constant and dielectric loss as a function of the frequency and temperature, with the findings described. The efficiency of nickel-doped PPLT crystal is 2.86 times larger than potassium dihydrogen phosphate (KDP), whereas the efficiency of pure PPLT is 1.38 times greater than KDP, according to NLO testing measured by Kurtz powder method. Because metal ions were incorporated into the crystal lattice, the SHG efficiency of nickel-doped PPLT was somewhat improved.

     

Galactomannan esters—A simple, cost-effective method of preparation and characterization

Acetate, succinate and octenylsuccinate derivatives of galactomannans were prepared in anhydrous reaction conditions carried out at slightly elevated temperature (40–60 °C) using solid NaHCO₃ as a mild base catalyst. Prior surface wetting of the reactants with 5% absolute ethyl alcohol gave derivatives with a higher degree of substitution, although it decreased the slurry viscosity. Use of NaHCO₃ significantly minimized high pH-induced degradative reactions, and its quantitative removal later was easily accomplished by repeated washings with aqueous ethyl alcohol. FT-IR, ¹³C-NMR, HPSEC and SEM data provided additional structural information. These derivatives are useful as functional food ingredients.     

Critical behaviour and magnetocaloric effect of nano crystalline La0.67Ca0.33Mn1−xFexO3 (x = 0.05, 0.2) synthesized by nebulized spray pyrolysis

The structure, critical exponents and magnetocaloric effect (MCE) of Nebulized Spray Pyrolysis (NSP) synthesized nano crystalline La_0.67Ca_0.33Mn_1−xFe_xO₃ (x = 0.05, 0.2) were investigated. The Reitveld refinement of XRD patterns show that the samples adopt an orthorhombic structure with Pnma space group. TEM inspection reveals that the average particle size is about 15 nm and 42 nm for NSP synthesized LCMFe_0.05 and LCMFe_0.2 samples respectively. The temperature and field dependent magnetization studies reveal the superparamagnetic state of La_0.67Ca_0.33Mn_0.95Fe_0.05O₃ and spin-glass-like state of La_0.67Ca_0.33Mn_0.8Fe_0.2O₃. The critical behaviour at the transition region studied using modified Arrott plot provides a second order nature of phase transition for both samples. The magnetocaloric studies show the maximum value of magnetic entropy change (ΔS_max) is in the range 2.3 J kg⁻¹ K⁻¹ at 158 K for LCMFe_0.05 and 0.3 J kg⁻¹ K at 92 K for LCMFe_0.2 respectively at 5 T field. The field dependence of the magnetic entropy changes are also analysed, which show a power law dependence (ΔS_M ∞ Hⁿ, n = 0.72 (2)) at transition temperature, T_C = 162 K for LCMFe_0.05 and n = 1.11(3) at 92 K for LCMFe_0.2.     

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space–time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBL-McRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multi-modal Human Action Database (MHAD).     

Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems

In this paper, we present a fast learning fully complex-valued extreme learning machine classifier, referred to as ‘Circular Complex-valued Extreme Learning Machine (CC-ELM)’ for handling real-valued classification problems. CC-ELM is a single hidden layer network with non-linear input and hidden layers and a linear output layer. A circular transformation with a translational/rotational bias term that performs a one-to-one transformation of real-valued features to the complex plane is used as an activation function for the input neurons. The neurons in the hidden layer employ a fully complex-valued Gaussian-like (‘sech’) activation function. The input parameters of CC-ELM are chosen randomly and the output weights are computed analytically. This paper also presents an analytical proof to show that the decision boundaries of a single complex-valued neuron at the hidden and output layers of CC-ELM consist of two hyper-surfaces that intersect orthogonally. These orthogonal boundaries and the input circular transformation help CC-ELM to perform real-valued classification tasks efficiently.Performance of CC-ELM is evaluated using a set of benchmark real-valued classification problems from the University of California, Irvine machine learning repository. Finally, the performance of CC-ELM is compared with existing methods on two practical problems, viz., the acoustic emission signal classification problem and a mammogram classification problem. These study results show that CC-ELM performs better than other existing (both) real-valued and complex-valued classifiers, especially when the data sets are highly unbalanced.