Characterization of pesticide consumption in the county of Santarém, Pará, Brazil

Many potentially harmful pesticides for both human health and the environment are used in Brazilian Amazon. However, no scientific datum on pesticide usage is presently available for this region. Consequently, it is difficult to assess which substances arc used and in which quantities. As an important premise for future work on pesticide contamination in the county of Santarém (State of Pará, Brazil), a survey was conducted in order to qualify and quantify the use of some pesticides in this region. This investigation was made between January and March 1997 and August and October 1998 and revealed use of several organophosphates, synthetic pyrethroids and carbamates insecticides. Furthermore, many herbicides and fungicides were listed. These pesticides are used for agriculture, domestic, and sanitary programs. This paper also provides a first estimation of quantities of some insecticides commonly used in agriculture (chlorpyrifos, malathion, metamidophos and methyl-parathion). The annual consumption for these four compounds is estimated at ca. 1 910 kg. Organophosphate insecticide consumption in the county of Santarém seems to be lower than the Brazilian average in terms of «per capita» and «per agricultural area» consumptions. Nevertheless, this county uses toxic substances on sensitive environments such as floodplains (várzeas), making relevant a thorough study on the potential contamination of this environment and its biota.

Comparative environmental life-cycle analysis of concretes using biomass and coal fly ashes as partial cement replacement material

Nowadays, the concrete production sector is challenged by attempts to minimize the usage of raw materials and energy consumption, as well as by environmental concerns. Therefore, it is necessary to choose better options, e.g. new technologies or materials with improved life-cycle performance. One solution for using resources in an efficient manner is to close the materials' loop through the recycling of materials that result either from the end-of-life of products or from being the by-product of an industrial process. It is well known that the production of Portland cement, one of the materials most used in the construction sector, has a significant contribution to the environmental impacts, mainly related with carbon dioxide emission. Therefore, the study and utilization of by-products or wastes usable as cement replacement in concrete can supply more sustainable options, provided that these type of concrete produced has same durability and equivalent quality properties as standard concrete. This work studied the environmental benefits of incorporating different percentages of two types of fly ashes that can be used in concrete as cement replacement. These ashes are waste products of power and heat production sectors using coal or biomass as fuels. The results showed that both ashes provide a benefit for the concrete production both in terms of environmental impact minimization and a better environmental performance through an increase in cement replacement. It is possible to verify that the incorporation of fly ashes is a sustainable option for cement substitution and a possible path to improve the environmental performance of the concrete industry.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Modern optimization in earthwork construction

Earthworks tasks are often regarded in transportation projects as some of the most demanding processes. In fact, sequential tasks such as excavation, transportation, spreading and compaction are strongly based on heavy mechanical equipment and repetitive processes, thus becoming as economically demanding as they are time-consuming. Moreover, actual construction requirements originate higher demands for productivity and safety in earthwork constructions. Given the percentual weight of costs and duration of earthworks in infrastructure construction, the optimal usage of every resource in these tasks is paramount. Considering the characteristics of an earthwork construction, it can be looked at as a production line based on resources (mechanical equipment) and dependency relations between sequential tasks, hence being susceptible to optimization. Up to the present, the steady development of Information Technology areas, such as databases, artificial intelligence and operations research, has resulted in the emergence of several technologies with potential application bearing that purpose in mind. Among these, modern optimization methods (also known as metaheuristics), such as evolutionary computation, have the potential to find high quality optimal solutions with a reasonable use of computational resources. In this context, this work describes an optimization algorithm for earthworks equipment allocation based on a modern optimization approach, which takes advantage of the concept that an earthwork construction can be regarded as a production line.

A trust model for cloud computing environment

This paper presents a proposal for a management model based on reliability requirements concerning Cloud Computing (CC). The proposal was based on a literature review focused on the problems, challenges and underway studies related to the safety and reliability of Information Systems (IS) in this technological environment. This literature review examined the existing obstacles and challenges from the point of view of respected authors on the subject. The main issues are addressed and structured as a model, called "Trust Model for Cloud Computing environment". This is a proactive proposal that purposes to organize and discuss management solutions for the CC environment, aiming improved reliability of the IS applications operation, for both providers and their customers. On the other hand and central to trust, one of the CC challenges is the development of models for mutual audit management agreements, so that a formal relationship can be established involving the relevant legal responsibilities. To establish and control the appropriate contractual requirements, it is necessary to adopt technologies that can collect the data needed to inform risk decisions, such as access usage, security controls, location and other references related to the use of the service. In this process, the cloud service providers and consumers themselves must have metrics and controls to support cloud-use management in compliance with the SLAs agreed between the parties. The organization of these studies and its dissemination in the market as a conceptual model that is able to establish parameters to regulate a reliable relation between provider and user of IT services in CC environment is an interesting instrument to guide providers, developers and users in order to provide services and secure and reliable applications.

Strategic planning of electricity systems: Integrating renewable energies

Tese de Doutoramento - Programa Doutoral em Engenharia Industrial e Sistemas (PDEIS)

A relational algebra approach to ETL modeling

The MAP-i Doctoral Programme in Informatics, of the Universities of Minho, Aveiro and Porto

Degradation of all-inkjet-printed organic thin-film transistors with TIPS-pentacene under processes applied in textile manufacturing

Printed electronics represent an alternative solution for the manufacturing of low-temperature and large area flexible electronics. The use of inkjet printing is showing major advantages when compared to other established printing technologies such as, gravure, screen or offset printing, allowing the reduction of manufacturing costs due to its efficient material usage and the direct-writing approach without requirement of any masks. However, several technological restrictions for printed electronics can hinder its application potential, e.g. the device stability under atmospheric or even more stringent conditions. Here, we study the influence of specific mechanical, chemical, and temperature treatments usually appearing in manufacturing processes for textiles on the electrical performance of all-inkjet-printed organic thin-film transistors (OTFTs). Therefore, OTFTs where manufactured with silver electrodes, a UV curable dielectric, and 6,13-bis(triisopropylsilylethynyl) pentance (TIPS-pentacene) as the active semiconductor layer. All the layers were deposited using inkjet printing. After electrical characterization of the printed OTFTs, a simple encapsulation method was applied followed by the degradation study allowing a comparison of the electrical performance of treated and not treated OTFTs. Industrial calendering, dyeing, washing and stentering were selected as typical textile processes and treatment methods for the printed OTFTs. It is shown that the all-inkjet-printed OTFTs fabricated in this work are functional after their submission to the textiles processes but with degradation in the electrical performance, exhibiting higher degradation in the OTFTs with shorter channel lengths (L=10 μm).

Da modelação matemática à simulação computacional: a experimentação matemática no ensino

Tese de Doutoramento em Ciências (área de especialização em Matemática).

Da modelação matemática à simulação computacional: a experimentação matemática no ensino

Tese de Doutoramento em Ciências (área de especialização em Matemática).

A vegetação na envolvente exterior dos edifícios: impactos, condicionantes e estratégias de intervenção ecoeficientes

Dissertação de mestrado integrado em Arquitectura

Aplicação de celulases e xilanases na reciclagem de fibras de papel

Tese de Doutoramento em Engenharia Química e Biológica (área de conhecimento em Engenharia Enzimática e das Fermentações)

A radiofrequency identification system for the 60 GHz ISM band

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)

Estudo, projeto e desenvolvimento de uma máquina para montar e vincar sacos de papel

Dissertação de mestrado integrado em Engenharia Mecânica

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.