Models for Human Resource Allocation Profiles in Different It Projects

A gestão de projetos vem sendo aplicada nas mais diversas áreas em face da aceleração do ritmo de mudanças impostas pela globalização em qualquer ambiente organizacional. Em especial, a gestão de projetos vem sendo utilizada com muita frequência na área de tecnologia da informação. Em uma instituição que executa projetos de TI, periodicamente existem demandas de alocação de recursos humanos. Cabe à organização fazer a alocação dos profissionais nas atividades de forma a atender as necessidades do projeto. Este artigo tem como objetivo propor e avaliar modelos estruturados em programação matemática, objetivando otimizar o uso dos recursos humanos em projetos de TI.The project management has been applied in many areas in special Information Technology (IT) companies have increasingly using project management methodologies. Institutions that performs IT projects periodically have demands for human resources allocation. This task is normally done manually, which makes it expensive and sensitive to under allocations. It is needed allocate all of the resources in the most several project activities, always paying attention to the different affinities that the professionals have according to their profile. In this scenario, the aim of this study is to evaluate and develop structured mathematical programming models to optimize the allocation of human resources from different profiles into activities of IT projects. The research developed was of type applied and explanatory, regarding to the nature and the objectives; experimental, regarding to the procedure; and quantitative, regarding to the approach. The experiments were done using real data projects extracted from a software factory, which in general lines the results revealed a cost saving of 15% in labor hours and higher agility in adopting mathematical models to define the allocations. This results contribute to IT project be completed better plans in budget and schedule

PROBLEMA E ALGORITMOS DE COLORAÇÃO EM GRAFOS - EXATOS E HEURÍSTICOS

Context: Given a graph and an integer the graph coloring problem consists to use a set of colors to assign to each node of a certain color, so that adjacent vertices have distinct color. Despite the existence of accurate methods capable of determining the staining solution to the problem, it is observed that these techniques have an exponential complexity in some cases. The limitations presented by exact methods when applied to instances of greater complexity motivated the development of several heuristics, able to indicate satisfactory solutions for graphs in general. This paper describes the problem of coloring graphs, presents some of the techniques that can be used in their determination, besides presenting some real problems that can be modeled as a graph and solved based on the result achieved by applying a coloring on. Contexto: Dados um grafo G e um inteiro k > 0 , o problema da coloração em grafos consiste em utilizar um conjunto de n <= k cores para atribuir a cada vértice de G uma determinada cor, de modo que vértices adjacentes tenham cores distintas. A despeito da existência de métodos de exatos, capazes de determinar a solução do problema de coloração, observa-se que estas técnicas possuem em alguns casos complexidade exponencial. A limitação apresentada pelos métodos exatos quando aplicados a instâncias de maior complexidade motivou o desenvolvimento de diversas heurísticas, aptas a indicar soluções de maneira satisfatória para grafos em geral. Este trabalho descreve o problema da coloração em grafos, apresenta algumas das técnicas que podem ser utilizadas na sua determinação, além de enumerar alguns problemas reais que podem ser modelados a partir de um grafo G e solucionados com base no resultado alcançado pela aplicação de uma coloração sobre G.   

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

O ensino da leitura em ambiente virtual: o uso da plataforma “Afiando Palavras” em escolas públicas cearenses

Resumo: O artigo relata não apenas a construção de um ambiente virtual de aprendizagem (AVA), voltado para o desenvolvimento e ensino da leitura em escolas públicas do estado do Ceará, mas também a construção de uma proposta metodológica própria e a sua utilização em três escolas públicas estaduais por professores e alunos. Inicialmente, idealizou-se a organização dos conteúdos numa proposta de sequência didática. No segundo momento, criou-se um AVA, estruturado de acordo com as necessidades da proposta idealizada. Por fim, fez-se uso do ambiente em três escolas da rede pública do estado do Ceará. Após esse período, os sujeitos da pesquisa preencheram um questionário avaliativo, a respeito de aspectos pertinentes ao ambiente e à proposta metodológica. Concluída a análise das respostas, constatou-se uma significativa contribuição do ambiente e da proposta metodológica para o desenvolvimento do ensino da leitura nas escolas

A New Hierarchical Procedure for Natural Groups Identification in Euclidean Space

In this work we discuss the Natural Group Problem (NP-Complete), where we evaluate some non hierarchical methods by a parallel implementation of a categorization process that uses the index of Calinski & Harabasz in conjunction with algorithms of Means (H-Means). After having observed that this method did not obtain good results, we were stimulated to develop a new methodology for natural groups identification based on hierarchical techniques. We found and show important properties in the hierarchical identification of natural groups. Starting from these properties, we built a new polynomial algorithm- O(n 2 log n), based on simple criteria we call NGI (Natural Group Identification Procedure). The procedure returns better than the first we evaluate, and outperforms some other methods from the literature. We show in our tests that it reaches exact solutions for a number of Euclidean examples (ℜ 2) tested, using data sets from the literature. All the results were investigated using the SCLUSTER system, built to facilitate multivariate analysis tasks