Open Educational Resources (OER) Issues and Problems Experienced by Social Scientists of Select Higher Educational Institutions in India

Abstract: Purpose: The purpose of the study is to explore the OER issues and problems experienced by social scientists at select higher educational institutions in India. In addition, the study also focuses on to create awareness about the concept and recommends some points to improve the OER practice. Design/Methodology/Approach: The relevant data for the study was obtained through a well-structured questionnaire followed by personal interview wherever it was required. The well-structured & precise questionnaire was distributed to 300 social scientists (research scholars and faculty) of different higher educational institutes such as University of Delhi (DU), Mizoram University (MZU), Jawaharlal Nehru University (JNU) and Jamia Milia Islamia (JMI), selected through purposive random sampling. In addition to this, the bibliographical data were recorded in MS-Excel sheet 2007 for tabulation, analysis and interpretation purpose. Further, the chi square test was applied using the application software i.e. SPSS to draw the results. Findings: The response rate was 86.67% in total that include 91.33% male and 82% female respondents. The respondents were found very enthusiastic in responding the questionnaire. Further, the study also finds that majority of the social scientist are aware about the concept open educational resources (OER). Research Limitations/Implications: The scope of the study is confined to the social scientists (research scholars and faculty) of University of Delhi (DU), Mizoram University (MZU), Jawaharlal Nehru University (JNU) and Jamia Milia Islamia (JMI) and research results are limited to this only. Originality/Value: The study stress upon the concept of open educational resources with regard to its issues and problems experienced by social scientists. The area of the study is still untouched here in India that makes the study unique in itself and opens the path to make the concept of OER to get the momentum here in India also as in developed countries the OER movement has already reached at advanced level. Paper Type: Research Pape

Artificial neural network to determine dynamic effect in capillary pressure relationship for two-phase flow in porous media with micro-heterogeneities

Open access articleAn artificial neural network (ANN) is presented for computing a parameter of dynamic two-phase flow in porous media with water as wetting phase, namely, dynamic coefficient (τ), by considering micro-heterogeneity in porous media as a key parameter. τ quantifies the dependence of time derivative of water saturation on the capillary pressures and indicates the rates at which a two-phase flow system may reach flow equilibrium. Therefore, τ is of importance in the study of dynamic two-phase flow in porous media. An attempt has been made in this work to reduce computational and experimental effort by developing and applying an ANN which can predict the dynamic coefficient through the “learning” from available data. The data employed for testing and training the ANN have been obtained from computational flow physics-based studies. Six input parameters have been used for the training, performance testing and validation of the ANN which include water saturation, intensity of heterogeneity, average permeability depending on this intensity, fluid density ratio, fluid viscosity ratio and temperature. It is found that a 15 neuron, single hidden layer ANN can characterize the relationship between media heterogeneity and dynamic coefficient and it ensures a reliable prediction of the dynamic coefficient as a function of water saturation

Editorial

Phenomenal advances in computer technology together with progress in the areas of mathematical modelling in recent decades have made simulation procedures very powerful tools for the analysis and design of almost all types of industrial and economic processes. Accurate and reliable predictions about the outcome of complex natural transport processes and performance of novel designs for industrial equipments are routinely made using modern simulation methodologies. Annually held international Industrial Simulation Conferences (ISC), organised and run by the EUROSIS in conjunction with various organisations, provides an important forum for the presentation and exchange of new ideas related to the development and application of computer simulation techniques in a very diverse and wide ranging area of industrial relevance

Artificial neural network to determine dynamic effect in capillary pressure relationship for two-phase flow in porous media with micro-heterogeneities

An artificial neural network (ANN) is presented for computing a parameter of dynamic two-phase flow in porous media with water as wetting phase, namely, dynamic coefficient (τ), by considering micro-heterogeneity in porous media as a key parameter. τ quantifies the dependence of time derivative of water saturation on the capillary pressures and indicates the rates at which a two-phase flow system may reach flow equilibrium. Therefore, τ is of importance in the study of dynamic two-phase flow in porous media. An attempt has been made in this work to reduce computational and experimental effort by developing and applying an ANN which can predict the dynamic coefficient through the “learning” from available data. The data employed for testing and training the ANN have been obtained from computational flow physics-based studies. Six input parameters have been used for the training, performance testing and validation of the ANN which include water saturation, intensity of heterogeneity, average permeability depending on this intensity, fluid density ratio, fluid viscosity ratio and temperature. It is found that a 15 neuron, single hidden layer ANN can characterize the relationship between media heterogeneity and dynamic coefficient and it ensures a reliable prediction of the dynamic coefficient as a function of water saturation

(E)-(4-Methoxyphenyl)-N-(4H-1,2,4-triazol-4-yl) methanimine: Solvent driven single molecule triple fluorescent “on” sensor for Cu2+, Cd2+ and Hg2+

213-217A single molecule, (E)-(4-methoxyphenyl)-N-(4H-1,2,4-triazol-4-yl) methanimine (Metho-tria-imine), can detect Cu2+, Cd2+ or Hg2+ depending on whether the solvent is H2O, CH3CN or C2H5OH respectively by fluorescence “on” mode. The enhancement in fluorescence intensity is found to be ca. 13 times for Cu2+, 70 times for Cd2+ and 57 times for Hg2+. The metal ions - Al3+, Co2+, K+, Li+, Mg2+, Mn2+, Na+, Ni2+, Pb2+, Zn2+ (along with two metal ions out of Cu2+, Cd2+ and Hg2+ for which the sensor is not effective) do not interfere. The plot of absorbance versus metal ion concentration was sigmoidal for Cu2+ and Cd2+ and linear for Hg2+ which indicates formation of dimeric complexes in solution for Cu2+ and Cd2+. DFT studies showed metal-metal bonding in case of Metho-tria-imine forming complexes with Cu2+ and Cd2+ and hence dimeric complexes with highest binding energy for Cu2+ in H2O, Cd2+ in CH3CN, Hg2+ in C2H5OH. The detection limits are found to be 1.9×10-8 M, 7.0×10-7 M and 6.9×10-8 M respectively and Metho-tria-imine is reversible with respect to EDTA2- for all the three metal ion

Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media

The dynamic effect in two-phase flow in porous media indicated by a dynamic coefficient τ depends on a number of factors (e.g. medium and fluid properties). Varying these parameters parametrically in mathematical models to compute τ incurs significant time and computational costs. To circumvent this issue, we present an artificial neural network (ANN)-based technique for predicting τ over a range of physical parameters of porous media and fluid that affect the flow. The data employed for training the ANN algorithm have been acquired from previous modeling studies. It is observed that ANN modeling can appropriately characterize the relationship between the changes in the media and fluid properties, thereby ensuring a reliable prediction of the dynamic coefficient as a function of water saturation. Our results indicate that a double-hidden-layer ANN network performs better in comparison to the single-hidden-layer ANN models for the majority of the performance tests carried out. While single-hidden-layer ANN models can reliably predict complex dynamic coefficients (e.g. water saturation relationships) at high water saturation content, the double-hidden-layer neural network model outperforms at low water saturation content. In all the cases, the single- and double-hidden-layer ANN models are better predictors in comparison to the regression models attempted in this work

Schiff base modified Pt electrode as sensor for detecting Al(III) and Pb(II)

A platinum electrode with its surface modified with the condensation product of p-phenylenediamine and acetylferrocene (PPDA-AcFc) has been fabricated by cyclic voltammetry. The square wave voltammogram of the modified electrode, PPDA-AcFc/Pt, in aqueous medium gradually shifts by 0.440 V and 0.090 V in the positive direction on interaction with Al3+ and Pb2+ respectively. Electrochemical impedance spectroscopy shows the charge transfer resistance value of PPDA-AcFc/Pt electrode increases in the case of Al3+ while it decreases in the case of Pb2+. The linear range of detection is 0-12 mM and 0-6 mM for Al3+ and Pb2+ respectively

Voltammetric distinction between dopamine and ascorbic acid using bare platinum disc electrode catalysed by bisethylenediaminecopper(II)

Bare platinum disc electrode in presence of bisethylenediaminecopper(II), [Cu(en)2]2+ where en = H2NCH2CH2NH2, in the electrolytic medium excellently distinguishes between dopamine (DA) and ascorbic acid (AA) in their aqueous mixture by cyclic voltammetry and square wave voltammetry. Both DA and AA independently changes the irreversible cyclic voltammogram of [Cu(en)2]2+ into a quasi reversible ones with redox potential value +0.045 V and +0.150 V respectively. The redox currents are linear function of DA and AA concentration. Interaction of [Cu(en)2]2+ with DA and AA mixture resulted quasi reversible cyclic voltammogram with a pair of redox couples with potential +0.220 V and +0.530 V due to DA and AA respectively

Voltammetric distinction between dopamine and ascorbic acid using bare platinum disc electrode catalysed by bisethylenediaminecopper(II)

102-106Bare platinum disc electrode in presence of bisethylenediaminecopper(II), [Cu(en)2]2+ where en = H2NCH2CH2NH2, in the electrolytic medium excellently distinguishes between dopamine (DA) and ascorbic acid (AA) in their aqueous mixture by cyclic voltammetry and square wave voltammetry. Both DA and AA independently changes the irreversible cyclic voltammogram of [Cu(en)2]2+ into a quasi reversible ones with redox potential value +0.045 V and +0.150 V respectively. The redox currents are linear function of DA and AA concentration. Interaction of [Cu(en)2]2+ with DA and AA mixture resulted quasi reversible cyclic voltammogram with a pair of redox couples with potential +0.220 V and +0.530 V due to DA and AA respectively

Schiff base modified Pt electrode as sensor for detecting Al(III) and Pb(II)

832-837A platinum electrode with its surface modified with the condensation product of p-phenylenediamine and acetylferrocene (PPDA-AcFc) has been fabricated by cyclic voltammetry. The square wave voltammogram of the modified electrode, PPDA-AcFc/Pt, in aqueous medium gradually shifts by 0.440 V and 0.090 V in the positive direction on interaction with Al3+ and Pb2+ respectively. Electrochemical impedance spectroscopy shows the charge transfer resistance value of PPDA-AcFc/Pt electrode increases in the case of Al3+ while it decreases in the case of Pb2+. The linear range of detection is 0-12 µM and 0-6 µM for Al3+ and Pb2+ respectively