Nature And Properties Of Soils

Facebook recently deployed Facebook Messages, its first ever user-facing application built on the Apache Hadoop platform. Apache HBase is a database-like layer built on Hadoop designed to support billions of messages per day. This paper describes the reasons why Facebook chose Hadoop and HBase over other systems such as Apache Cassandra and Voldemort and discusses the applicationBs requirements for consistency, availability, partition tolerance, data model and scalability. I explore the enhancements made to Hadoop to make it a more effective realtime system, the tradeoffs we made while configuring the system, and how this solution has significant advantages over the sharded MySQL database scheme used in other applications at Facebook and many other web-scalecompanies. I discuss the motivations behind my design choices, the challenges that we face in day-to-day operations, and future capabilities and improvements still under development.I offer these observations on the deployment as a model for other companies who are contemplating a Hadoop-based solution over traditional sharded RDBMS deployments.

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Apache Hadoop Goes Realtime at Facebook

This study aims at analyzing the performance results of the European policies in energy investment within an integrated multidimensional quality measurement approach. For this purpose, the quality function deployment is adopted to the multidimensional performance measurement based on the balanced scorecard method. The dimensions and criteria of customer and technical requirements, process design, and performance measurement are defined to measure each process of QFD (Quality Function Deployment) for the energy investment policies with the supported literature. Within this context, the customer and technical requirements for energy investment policies are evaluated by considering the fuzzy decision matrix in the first process of the QFD and then, the technical requirements are used to rank the new service/product process of the energy investments in the second process of QFD. The balanced scorecard perspectives and the key factors are defined to rank the European energy investment policies respectively. Accordingly, the possible priorities of the European energy investment policies are discussed to conduct the most remarkable and efficient energy operations and the relevant results to contribute the improvement of energy sector are highlighted.

Balanced scorecard-based analysis about European energy investment policies: A hybrid hesitant fuzzy decision-making approach with Quality Function Deployment

Effects of inoculation with lignocellulose-degrading microorganisms on antibiotic resistance genes and the bacterial community during co-composting of swine manure with spent mushroom substrate.

Spent mushroom substrate and cattle manure amendments enhance the transformation of garden waste into vermicomposts using the earthworm Eisenia fetida

Implementation of context-aware workflows with multi-agent systems

Production of mushrooms and wine has a very significant economic impact on the Region of La Rioja (Spain). A direct consequence of these activities is the production of large volumes of agricultural wastes. Since composting may be a viable alternative means to reduce the environmental problems currently associated with the management of these residues, this article reports the results that have been obtained from the composting of two types of spent mushroom substrate and winery sludge. The characteristics of the final product indicated that this combination of residues results in an organic material that is suitable for application to crops.

Composting of Spent Mushroom Substrate and Winery Sludge

At present, the European Higher Education Area (EHEA) emphasizes the importance of collaborative learning in University education Previous studies have found evidence to support collaborative activities; however, none have analyzed the influence of contact networks among students on their engagement and performance This paper reports an intervention with nursing degree students aimed at facilitating an interdependent network to promote engagement and improve academic performance The intervention was based on recording a video during a case study The variables analyzed were network centrality, engagement and performance The UCINET program was used to analyse social networks The results showed a more cohesive network after the intervention and a high level of academic performance The use of contact networks among students could be used as an academic strategy to build bridges between students in the classroom and even between these and students in other classrooms or centres

Co-operative Networks and their Influence on Engagement: A Study with Students of a Degree in Nursing

Public health in developed countries is heavily affected by pollution specially in highly populated areas. Amongst the pollutants with greatest impact in health, ozone is particularly addressed in this paper due to importance of its effect on cardiovascular and respiratory problems and their prevalence on developed societies. Local authorities are compelled to provide satisfactory predictions of ozone levels and thus the need of proper estimation tools rises. A data driven approach to prediction demands high quality data but those observations collected by weather stations usually fail to meet this requirement. This paper reports a new approach to robust ozone levels prediction by using an outlier detection technique in an innovative way. The aim is to assess the feasibility of using raw data without preprocessing in order to obtain similar or better results than with traditional outlier removal techniques. An experimental dataset from a location in Spain, Ponferrada, is used through an experimental stage in which such approach provides satisfactory results in a difficult case.

Data Mining Techniques for the Estimation of Variables in Health-Related Noisy Data

This paper explores the benefit of using the PAELLA algorithm in an innovative way. The PAELLA algorithm was originally developed in the context of outlier detection and data cleaning. As a consequence, it is usually seen as a discriminant tool that categorizes observations into two groups: core observations and outliers. A new look at the information contained in its output provides ample opportunity in the context of data driven predictive models. The information contained in the occurrence vector is used through the experiments reported in a quest for finding how to take advantage of that information. The results obtained in each successive experiment guide the researcher to a sensible use case in which this information proves extremely useful: probabilistic sampling regression.

Javier Alfonso-Cendón

Papers

Implementation of context-aware workflows with multi-agent systems

Composting of Spent Mushroom Substrate and Winery Sludge

Co-operative Networks and their Influence on Engagement: A Study with Students of a Degree in Nursing

Data Mining Techniques for the Estimation of Variables in Health-Related Noisy Data

Coupling the PAELLA Algorithm to Predictive Models