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The word salinity comes from the Latin word salinium which means "salt cellar" and it means "position or quality of being". Sodicity indicates the amount of sodium converted to calcium and magnesium in soil. High sodicity suppresses plant growth due to sodium toxicity and nutrient imbalance in plants, as well as low availability of mineral nutrients in the soil. Salt stress is the cause of the slow growth and growth of plants and leads to changes in yield and quality in a variety of crops. Plants provide a complex response to salt and changes in the morphology, physiology and metabolism of plants are observed. The effect of salt on various vegetable plants namely beetroot, cabbage, capsicum, kabuli chana, coriander, fenugreek, lettuce, onion, tomato, potato was reviewed. Salinity was adversely damaged as a result of salinity: Seed formation, survival percent, phonological attribute, growth and yield, its components, dry and fresh weight were affected. Photosynthesis and respiratory rates of plants were reduced. Salinity reduced total carbohydrate, fatty acid, and protein content but notably increased amino acid levels. The growth of asparagus and tomatoes was more concentrated in sodic soils than saline soils, indicating that asparagus and tomatoes are sensitive to sodicity. Beans also die in sodic soils, indicating that the beans are very sensitive to sodicity. The negative effects of alkaline water on the addition of gypsum and FYM have shown a significant increase in plant growth and yield limits.

Plasma is the ionized state of matter and is of interest as it has found applications in diverse fields. In all practical applications of plasma, it interacts with the material surface via non-neutral region that is formed between bulk plasma and surface known as the sheath, which plays a vital role in overall plasma properties. In this work, the characteristics of electronegative magnetized plasma sheath have been presented employing the kinetic trajectory simulation method based on kinetic theory. It is found that magnetic field and volumetric composition of negatively charged particles have significantly affected the characteristics of electronegative plasma sheath. Although the particle densities deplete towards the wall, the decreasing rate of negative charged particles is steeper than that of positive ions. The magnitude of electric field slowly increases close to the sheath entrance, whereas it sharply increases close to the wall. The positive ion density decreases in both cases when the concentration of negative ion is increased or when the magnetic field is increased. On increasing the magnetic field from 0 to 250 mT, the ion density reaching the wall decreases from 0.331 to 0.305 n ps. The results are similar and agree with similar works following different models and our model provides a satisfactory basis for the study of electronegative plasma sheath.

In this work, quantum chemical study on a natural product ergoline has been presented using density functional theory (DFT) employing 6-311++G(d,p) basis set. A complete vibrational assignment has been performed for the theoretical FT-IR and Raman wavenumbers along with the potential energy distribution (PED) with the result of quantum chemical calculations. The structure–activity relationship has been interpreted by mapping electrostatic potential surface (MEP). Graphical representation of frontier molecular orbitals with their energy gap have been analyzed theoretically for both the gaseous and solvent environment employing time dependent density functional theory (TDDFT) employing 6-31G basis set.

In this work, plasma is produced by arc discharge between two copper electrodes and is characterized by a movable single probe and a double Langmuir probe. The movable Langmuir single-probe techniq...

Today, due to globalization of the world the size of data set is increasing, it is necessary to discover the knowledge. The discovery of knowledge can be typically in the form of association rules, classification rules, clustering, discovery of frequent episodes and deviation detection. Fast and accurate classifiers for large databases are an important task in data mining. There is growing evidence that integrating classification and association rules mining, classification approaches based on heuristic, greedy search like decision tree induction. Emerging associative classification algorithms have shown good promises on producing accurate classifiers. In this paper we focus on performance of associative classification and present a parallel model for classifier building. For classifier building some parallel-distributed algorithms have been proposed for decision tree induction but so far no such work has been reported for associative classification.