Improving selective collection of urban waste using a business intelligence system

Today recovering urban waste requires effective management services, which usually imply sophisticated monitoring and analysis mechanisms. This is essential for the smooth running of the entire recycling process as well as for planning and control urban waste recovering. In this paper we present a business intelligence system especially designed and im- plemented to support regular decision-making tasks on urban waste management processes. The system provides a set of domain-oriented analytical tools for studying and characterizing poten- tial scenarios of collection processes of urban waste, as well as for supporting waste manage- ment in urban areas, allowing for the organization and optimization of collection services. In or- der to clarify the way the system was developed and the how it operates, particularly in process visualization and data analysis, we also present the organization model of the system, the ser- vices it disposes, and the interface platforms for exploring data.

Ex situ collection of Limonium spp.: cytogenetic and reproductive characterization for the conservation of rare species

Dissertação mestrado em Biologia Molecular, Biotecnologia e Bioempreendedorismo em Plantas

A survey of distributed data aggregation algorithms

Distributed data aggregation is an important task, allowing the de- centralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting val- ues result from the distributed computation of functions like count, sum and average. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many di erent approaches have been pro- posed, with di erent trade-o s in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of ag- gregation algorithms, it can be di cult and time consuming to determine which techniques will be more appropriate to use in speci c settings, jus- tifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally de nes the concept of aggrega- tion, characterizing the di erent types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

Spatio-temporal modelling of environmental data

Programa Doutoral em Matemática e Aplicações.

A systematic study of the structural and magnetic properties of Mn-, Co-, and Ni-Doped Colloidal Fe3O4 nanoparticles

A series of colloidal MxFe3-xO4 (M = Mn, Co, Ni; x = 0–1) nanoparticles with diameters ranging from 6.8 to 11.6 nm was synthesized by hydrothermal reaction in aqueous medium at low temperature (200 °C). Energy-dispersive X-ray microa-nalysis and inductively coupled plasma spectrometry confirms that the actual elemental compositions agree well with the nominal ones. The structural properties of obtained nanoparticles were investigated by using powder X-ray diffraction, Raman scattering, Mössbauer spectroscopy, and electron microscopy. The results demonstrate that our synthesis technique leads to the formation of chemically uniform single-phase solid solution nanoparticles with cubic spinel structure, confirming the intrinsic doping. Magnetic studies showed that, in comparison to Fe3O4, the saturation magnetization of MxFe3-xO4 (M = Mn, Ni) decreases with increasing dopant concentration, while Co-doped samples showed similar saturation magnetizations. On other hand, whereas Mn- and Ni-doped nanoparticles exhibits superparamagnetic behavior at room temperature, ferromagnetism emerges for CoxFe3-xO4 nanoparticles, which can be tuned by the level of Co doping.

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.