THE PEDOLOGICAL AND AGROCHEMICAL FEATURES OF AN ENTIANTROSOIL FROM THE GANGUE DUMP ROSIA QUARRY, GORJ COUNTY

Our country has important inferior coal reserves that, by their features, can by extracted from the soil surface. After the extraction processes large surface of land can not be cultivated; they are represented by the excavation and depositing surfaces as sterile dumps. These surfaces must be cropped again. In this respect there is need to know their main pedological and agrochemical characteristics in order to establish the most suitable measures for cropping.The paper presents the main agropedological features of the sterile dump from Rosia, District Gorj, in order to set up the most suitable strategy of its recovery

ORGANIC AND MINERAL FERTILIZATION OF TEMPORARY GRASLANDS – ECOLOGICAL MEAUSE AND THE EFFICIENCY ON ENHANCING FODDER PRODUCTION SCENARIO IN THE HILLS`AREA OF OLTENIA REGION

It is commonly known manure is a complex organic fertilizer. Manure contributes to the fertility of the soil by adding organic matter and nutrients such as: nitrogen or phosphorus wich can be found goth as an organic and mineral matter in equal parts and potassium wich cand be found especially in soluble form in water (Gooke, 1971, quoted by Moga. I, and colab. 1996)

ADAPTING PERENNIAL GRAMINACEE PRODUCTION FOR HILLS`AREA OF OLTENIA REGION

The present paper aims at presenting the importance of perennial graminacee introduction for temporary grasslands, which were adapted to the ecological conditions. Moreover here are some species of graminacee which were adapted to the present conditions

Parallel programming paradigms and frameworks in big data era

With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. We have entered the Era of Big Data. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines-ranging from science and engineering to biology and business. One major challenge is how to take advantage of the unprecedented scale of data-typically of heterogeneous nature-in order to acquire further insights and knowledge for improving the quality of the offered services. To exploit this new resource, we need to scale up and scale out both our infrastructures and standard techniques. Our society is already data-rich, but the question remains whether or not we have the conceptual tools to handle it. In this paper we discuss and analyze opportunities and challenges for efficient parallel data processing. Big Data is the next frontier for innovation, competition, and productivity, and many solutions continue to appear, partly supported by the considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. We review various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigms and frameworks. To better support practitioners interesting in this domain, we end with an analysis of on-going research challenges towards the truly fourth generation data-intensive science.Peer ReviewedPostprint (author's final draft