111 research outputs found
Asymmetric Second Order Rotatable Designs (AsymmetricSORDs)
Get PDFResponse surface designs (RSDs) are widely used for Response Surface Methodology (RSM) based optimization studies, which aid in exploring the relationship between a group of explanatory variables and one or more response variable(s) (G.E.P. Box and K.B. Wilson (1951), ``On the experimental attainment of optimum conditions'' ; M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) .``Theoretical developments in response surface designs: an informative review and further thoughts''.). Second order rotatable designs are the most prominent and popular class of designs used for process and product optimization trials but it is suitable for situations when all the number of levels for each factor is the same. In many practical situations, RSDs with asymmetric levels (J.S. Mehta and M.N. Das (1968). ``Asymmetric rotatable designs and orthogonal transformations'' ; M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2020) . ``Sequential asymmetric third order rotatable designs (SATORDs)'' .) are more suitable as these designs explore more regions in the design space.This package contains functions named Asords() ,CCD_coded(), CCD_original(), SORD_coded() and SORD_original() for generating asymmetric/symmetric RSDs along with the randomized layout. It also contains another function named Pred.var() for generating the variance of predicted response as well as the moment matrix based on a second order model
Lipid profile of different body components of selected Indian marine fishes with reference to their fatty acid composition
No full textThis Dissertation / Report is the outcome of investigation carried out by the creator(s) / author(s) at the department/division of Central Food Technological Research Institute (CFTRI), Mysore mentioned below in this page
Rational computational design for the development of andrographolide molecularly imprinted polymer
No full textBiochemical analysis of enhanced tolerance in transgenic potato plants overexpressing d-galacturonic acid reductase gene in response to various abiotic stresses
No full textUpregulation of the antioxidant enzyme system in plants provides protection against various abiotic stresses. Transgenic potato plants overexpressing the strawberry D-galacturonic acid reductase (GalUR) gene with enhanced accumulation of ascorbate (AsA) were used to study the antioxidant system involving the ascorbate-glutathione cycle in order to understand the tolerance mechanism in plants in response to various abiotic stresses under in vitro conditions. Transgenic potato tubers subjected to various abiotic stresses induced by methyl viologen, sodium chloride and zinc chloride showed enhanced activities of superoxide dismutase (SOD, EC 1.15.1.1), catalase (CAT, EC 1.1.1.1.6) and enzymes of the ascorbate-glutathione cycle such as ascorbate peroxidase (APX, EC 1.11.1.11), dehydroascorbate reductase (DHAR, EC 1.8.5.1) and glutathione reductase (GR, EC 1.8.1.7), as well as increased levels of ascorbate, glutathione (GSH) and proline when compared to untransformed tubers. The increased enzyme activities correlated with the mRNA transcript levels in the stressed transgenic tubers. Significant differences in redox status of AsA and GSH were also observed in stressed transgenic potato tubers that showed increased tolerance to abiotic stresses compared to untransformed tubers. This study suggests that the increased accumulation of AsA could upregulate the antioxidant system which imparts improved tolerance against various abiotic stresses in transgenic tubers compared to untransformed tubers.open
Modeling and Estimation of Lithium-ion Battery State of Charge Using Intelligent Techniques
No full textBiotechnological Approaches for the Downstream Processing of Selected Enzymes
No full textThe present study mainly covers the downstream processing of selected
enzymes using reverse micellar extraction, aqueous two phase extraction and
membrane processes. Experiments were carried out to evaluate the effect of
various process parameters of these processes on recovery and purification
of target enzyme from natural sources which are often complex mixture.
Reverse micellar extraction, aqueous two phase extraction and membrane
processes are operated individually as well as in an integrated mode for the
purification of selected enzymes such as bromelain, β-galactosidase and β-
glucosidase. Major emphasis is given to develop a systematic approach for
the extraction and purification of enzymes from natural systems. Reverse
micellar extraction of β-galactosidase and β-glucosidase has given a new
insight into successful extraction of large molecular weight enzymes when
attempted by injection mode. Similarly mixed reversed micellar system is used
for the first time for extraction and purification of enzyme from real system. A
new approach for simultaneous fractionation and purification of mixture of β-
glycosidases namely, β-galactosidase and β-glucosidase, from Hordeum
vulgare using aqueous two phase system was provided. The conventional
methods of enzyme purification were also carried out and compared with the
present methods.
The present protocols, although studied for particular enzymes, can be
applied in general to any other biomolecules. The thesis contributes to applied
science in terms of process development for the extraction, isolation,
purification and concentration selected enzymes from natural complex
systems
Lithium-ion battery state of health estimation using intelligent methods
No full textIn electric vehicle applications, detecting Li-ion battery degradation is essential to ensure safety and reliability. A key approach to assessing battery health is monitoring the internal impedance and capacity over the battery's lifetime, which provides insight into the State of Health (SOH) and indicates whether the battery has reached its End of Life (EOL). This study proposes an intelligent SOH estimation algorithm utilizing Feed-forward and Recurrent Neural Networks, trained with the Levenberg-Marquardt function, to predict battery SOH under various aging conditions. The methodology begins with life cycle and Electrochemical Impedance Spectroscopy (EIS) tests to establish the charge-discharge characteristics and create an Equivalent Circuit Model that represents the dynamic properties and degradation indicators of an 18650 Li-ion battery. Key model parameters, such as internal resistance, are extracted per cycle to track aging progression. Finally, the SOH estimation models, developed in SIMULINK, utilize internal impedance and capacity metrics to predict SOH under various aging scenarios. Results in SIMULINK demonstrate that both networks provide accurate SOH estimations; however, the Recurrent Neural Network achieves faster convergence, reaching accurate predictions within 10 epochs. This improved convergence speed, along with high measurement accuracy and reliability, underscores the Recurrent Neural Network's suitability for real-time SOH monitoring in electric vehicle applications
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