Pyrite is a common mineral in sedimentary rocks and is widely distributed in a variety of different morphologies and sizes. Pyrite is also widely distributed in the Es3x shale of the Eocene Shahejie Formation in the Zhanhua Sag, Bohai Bay Basin. A combination of geochemical and petrographic studies has been applied to address the formation and distribution of pyrite in the Es3x shale. The methods include thin section analysis to identify the representative samples of the shale-containing pyrite, total organic carbon (TOC) content analysis, X-ray fluorescence, X-ray diffraction, electron probe micro-analysis, and field emission scanning electron microscopy (FE-SEM) coupled with the energy dispersive spectrometer, to observe the characteristics, morphology, and distribution of pyrite in the lacustrine shale. The content of pyrite in the Es3x shale ranges from 1.4 to 11.2% with an average content of 3.42%. The average contents of TOC and total organic sulfur (TS) are 3.48 and 2.53 wt %, respectively. Various types of pyrites are observed during the detailed FE-SEM investigations including pyrite framboids, euhedral pyrite, welded pyrite, pyrite microcrystals, and framework pyrite. Pyrite framboids are densely packed sphere-shaped masses of submicron-scale pyrite crystals with subordinate large-sized euhedral crystals of pyrite. Welded pyrite forms due to the overgrowth and alteration of pyrite crystals within the larger pyrite framboids. Pyrite microcrystals are the euhedral-shaped microcrystals of pyrite. The framework pyrite is also observed and is formed due to the pyritization of plant/algal tissues. Based on the growth mechanism, the pyrites can be divided into syngenetic pyrites, early diagenetic pyrites, and late diagenetic pyrites. The presence of pyrite, especially the abundance of pyrite framboids, suggests that the environment during the Es3x shale deposition in the lacustrine basin was anoxic. Their dominant smaller size suggests the presence of an euxinic water column, which is consistent with the lack of in-place biota and high TOC contents. This research work not only helps to understand the pyrite mineralization, role of organic matter, and reactive iron in pyrite formation in the shale but also helps to interpret the paleoredox conditions during the deposition of shale. This research work can also be helpful to other researchers who can apply these methods and conclusions to studying shale in other similar basins worldwide.

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An experiment on hundred wheat genotypes under different levels of osmotic stress was carried out during 2014 to select the genotype(s) tolerant to drought at germination and early seedling stage. Different levels of osmotic stress were imposed by using polyethylene glycol (PEG). Three osmotic stress levels viz. control (distilled water), 15% PEG solution and 25% PEG solution were used. Among the 100 genotypes the rate of germination percentage, final germination (%), root and shoot dry weight, amount of respiration and vigour index under PEG treatment was found significantly lower than that of control condition. Compared to control condition relative decrease in rate of germination, final germination, amount of respiration and vigour index among the wheat genotypes were found more at 25% PEG than that of 15% PEG treatment. However, the seed metabolic efficiency was significantly higher in wheat genotypes under both 15% PEG and 25% PEG treatment compared to the control condition. A significant positive correlation exists between the important growth parameters like rate of germination (%), final germination (%), shoot dry weight, root dry weight and vigour index. On the basis of these physiological traits against osmotic stress, nine genotypes of wheat such as BD-480, BD-498, BD-501, BD-513, BD-514, BD-519, BD-592, BD-618 and BD-633 were selected as drought tolerant.

The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are used routinely to solve the problem from a causal modeling perspective. However, a transparent process in the form of Decision Trees is used to shed more light on the structure of input variables such as climate and land use in predicting the stream water quality in the current paper. The Decision Tree algorithms such as classification and regression tree (CART), iterative dichotomiser (ID3), random forest (RF), and ensemble methods such as bagging and boosting are applied to predict and classify the unknown stream water quality behavior from the input variables. The variants of bagging and boosting have also been looked at for more effective modeling results. Although the Random Forest, Gradient Boosting, and Extremely Randomized Tree models have been found to yield consistent classification results, DTs with Adaptive Boosting and Bagging gave the best testing accuracies out of all the attempted modeling approaches for the classification of Fecal Coliforms in the Upper Green River watershed, Kentucky, USA. Separately, a discussion of the Decision Support System (DSS) that uses Decision Tree Classifier (DTC) is provided.