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Abstract: Studying the impacts of climate change requires looking at a multitude of variables across a broad range of sectors [1,2]. Information on the variables involved is often unevenly available or offers different uncertainties [3,4], and a lack of uniform terminology and methods further complicates the process of analysis, resulting in communication gaps when research enterprises span different sectors. For example, models designed by experts in one given discipline might assume conventions in language or oversimplify cross-disciplinary links in a way that is unfamiliar for scientists in another discipline. Geospatial Semantic Array Programming (GeoSemAP) offers the potential to move toward overcoming these challenges by promoting a uniform approach to data collection and sharing [5]. The Joint Research Centre of the European Commission has been exploring the use of geospatial semantics through a module in the PESETA II project (Projection of economic impacts of climate change in sectors of the European Union based on bottom-up analysis).

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recent years, there has been a population increase, and with it, an increased density in our environment. The massive building in residential areas, various technologies which have become more available for individual use (cars, varied music players, TV sets). In this article, we will try to introduce a number of functions that eliminate noise.

Università degli studi di Napoli Federico II Scuola Politecnica e delle Scienze di Base Corso di Laurea Magistrale in Ingegneria Informatica Tesi di Laurea Magistrale in Big Data Analytics and Business Intelligence Artificial Bee Colony Influence Maximization

Document describing basic economic and statistical analysis and how to apply them to machine learning

"(Infinite) series are the invention of the devil, by using them, on may draw any conclusion he pleases, and that is why these series have produced so many fallacies and so many paradoxes." -Neils Hendrik Abel

Based on the paper Sometimes Newton's Method Cycles, we first asked ourselves if there were any Newtonian Method Cycle functions which have non-trivial guesses. We encountered a way to create functions that cycle between a set number of points with any initial, non-trivial guesses when Newton's Method is applied. We exercised these possibilities through the methods of 2-cycles, 3-cycles and 4-cycles. We then generalized these cycles into k-cycles. After generalizing Newton's Method, we found the conditions that skew the cycles into a spiral pattern which will either converge, diverge or become a near-cycle. Once we obtained all this information, we explored additional questions that rose up from our initial exploration of Newton's Method.

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