ABSTRACT
The study examines the role of quality education in access to justice, using a panel data of 21 diversified countries for the period of 1990-2015. The findings show that there is a positive relationship between the presence of scientific and technical journals (STJ) articles and crime rates. The R&D expenditures does not substantially reduce crime rate while per capita income, trademark applications, and technical cooperation grants significantly reduce crime rates across countries. The panel fixed effect (FE) model confirmed the inverted U-shaped relationship between per capita income (GDPpc) and crime rate in the presence of STJ, while this result is changed in the case of GMM estimator. The results of panel causality confirmed the unidirectional causality running from crime rate to STJ and R&D expenditures, while there is bidirectional causality between i) GDPpc and technical cooperation grants, and between ii) energy efficiency and refugee population by country. The variance decomposition analysis (VDA) shows that R&D expenditures have a greater share to influence crime rate, while technical cooperation grants will affect STJ for the next 10â¯years time. This finding bolsters the conversation on the relationship between education and a reduction in crime rates.
ABSTRACT
The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance ((1)H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all proton bearing compounds, and consequently all relevant sample classes. Since the spectra obtained were too complex to be analyzed visually by the naked eyes, the classification of spectra was carried out.The multivariate calibration of partial least square (PLS) regression was used for modelling the relationship between actual value of lard and predicted value. The model yielded a highest regression coefficient (R(2)) of 0.998 and the lowest root mean square error calibration (RMSEC) of 0.0091% and root mean square error prediction (RMSEP) of 0.0090, respectively. Cross validation testing evaluates the predictive power of the model. PLS model was shown as good models as the intercept of R(2)Y and Q(2)Y were 0.0853 and -0.309, respectively.
