Biostratigraphy of the Surgah formation in Kuh-e-Surgah and correlation with Tang-e-Gerab (West of Iran)

In biostratigraphic studies of the Surgah formation in the Kuh-e-Surgah section, 145 samples were collected. The thickness of the Surgah formation is about 175 m, and it consists mainly of limestone and shale. The lower and upper boundaries of the Surgah formation are Sarvak and Ilam formations, respectively, and they are conformable with sharp lithology. Seventeen species which belong to six genera of planktonic foraminifera and four biozones have been identified in this study. Based on foraminifera assemblages and biozone determinations, the age of the Surgah formation is Late Turonian to Early Late Santonian. This section is correlated with the Tang-e-Gerab section.     

Factors encouraging sustainability integration into institutions of higher education

In view of the mountainous evidence on destruction of environmental quality and societal well-being as a consequence of rapid economic development, sustainability has gained vast attention from the community and industrial players. Tertiary education is a platform through which sustainability can be inculcated within the society as it imparts knowledge and provides various trainings. There has been extensive research on factors that encourage sustainability integration into Institutions of Higher Education in the last decade. However, majority of the previous publications only discuss one or two factors exclusively and there is no literature that summarizes and discusses such factors in a collective manner. This paper provides an overview of the main factors that encourage sustainability integration into Institutions of Higher Education in the last decade. It aims at providing a one-stop reference for future researchers who need a reference on factors that encourage sustainability integration into Institutions of Higher Education, especially those who are interested in conducting a progressive research in this context. Accordingly, a review of relevant publications from year 2000 and above was conducted and it was found that there are generally eight main factors, which encourage sustainability integration into Institutions of Higher Education, which are: (1) integration into curricula; (2) suitable pedagogy; (3) campus management; (4) research; (5) opportunities provision; (6) availability of social capital; (7) awareness level; and (8) community outreach. There is no indicator on the impact level of these factors, and thus, it is suggested that relevant research can be conducted in future.     

Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network

The longitudinal dispersion coefficient is a key element in determining the distribution and transmission of pollution, especially when cross-sectional mixing is completed. However, the existing predictive techniques for this purpose exhibit great amounts of uncertainty. The main objective of this study is to present a more accurate model for predicting longitudinal dispersion coefficient in natural rivers and streams. Bayesian network (BN) approach was considered in the modeling procedure. Two forms of input variables including dimensional and dimensionless parameters were examined to find the best model structure. In order to increase the performance of the model, the clustering method as a preprocessing data technique was applied to categorize the data in separate groups with similar characteristics. An expansive data set consisting of 149 field measurements was used for training and testing steps of the developed models. Three performance evaluation criteria were adopted for comparison of the results of the different models. Comparison of the present results with the artificial neural network (ANN) model and also well-known existing equations showed the efficiency of the present model. The performance of dimensionless BN model 30% is more than dimensional ones in terms of the root mean square error. The accuracy criterion was increased from 70 to 83% by performing clustering analysis on the BN model. The BN-cluster model 43% is more accurate than ANN model in terms of the accuracy criterion. The results indicate that the BN-cluster model give 16% better results than the best available considered model in terms of the accuracy criterion. The developed model provides a suitable approach for predicting pollutant transport in natural rivers.