A Bibliometric study on Industry 4.0

Understanding the scientific production  evolution of a certain area of knowledge is fundamental to capture its development and facilitate its dissemination. Thus, this paper presents a bibliometric study on Industry 4.0. For that, the metadata of 1382 publications, which were published, between the years of 2013 and 2017, in vehicles indexed to the Web of Science are analyzed. The results found that the research on Industry 4.0 came from several countries and that Germany leads the ranking of country publications by having published 462 of the 1382 publications on Industry 4.0. It should be noted that currently the most recurring words in scientific publications on Industry 4.0 are: "big data"; "it was"; "Review"; "Opportunity" and "smart manufacturing", that is, they represent the trends and the main objects of interest on the topic today

Incorporating managed preferences in the evaluation of public organizations efficiency: a DEA approach

Classical Data Envelopment Analysis (DEA) models have often been used to evaluate the public organizations effectiveness. However, such models, by ignoring the managers preferences, can classify as efficient organizations that, in fact, are not. Based on this motivation, the objective of this paper was to evaluate, through a DEA model that incorporates managerial preferences, the efficiency of the 25 superintendencies of the National Department of Mineral Production (DNPM), an autarchy linked to the Ministry of Mines and Energy. For this purpose, the number of servers in the middle and end areas and, as outputs, the number of mining titles granted in 2016 was considered as input. Manager preferences regarding outputs were incorporated into classical DEA models using the assurance region method. The results showed that when management preferences were incorporated into classic DEA models, the DNPM superintendencies number that showed maximum operational efficiency was reduced from eight to five. For superintendencies classified as inefficient, the benchmarks and performance targets were identified, which is useful, since they can support the action planning aimed at reducing the high liabilities pending processes for analysis by the municipality. This would reduce DNPM's slowness in granting mining bonds, which would stimulate investments in the mineral sector, which is of paramount importance to the Brazilian economy. It should be emphasized that the methods used in this research can be applied in the evaluation of the organizations efficiency whose managers have different preferences on inputs and outputs

Condition-based maintenance in hydroelectric plants: A systematic literature review

Industrial maintenance has become an essential strategic factor for profit and productivity in industrial systems. In the modern industrial context, condition-based maintenance guides the interventions and repairs according to the machine’s health status, calculated from monitoring variables and using statistical and computational techniques. Although several literature reviews address condition-based maintenance, no study discusses the application of these techniques in the hydroelectric sector, a fundamental source of renewable energy. We conducted a systematic literature review of articles published in the area of condition-based maintenance in the last 10 years. This was followed by quantitative and thematic analyses of the most relevant categories that compose the phases of condition-based maintenance. We identified a research trend in the application of machine learning techniques, both in the diagnosis and the prognosis of the generating unit’s assets, being vibration the most frequently discussed monitoring variable. Finally, there is a vast field to be explored regarding the application of statistical models to estimate the useful life, and hybrid models based on physical models and specialists’ knowledge, of turbine-generators

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

Potencial de geração de energia eólica no Brasil: análise de municípios na região Sul e Nordeste do Brasil

Exportado OPUSMade available in DSpace on 2019-08-13T02:00:15Z (GMT). No. of bitstreams: 1 disserta__o_tiago_silveira_gontijo.pdf: 5193910 bytes, checksum: eee6b5570a981f09b7ca9ddf80402856 (MD5) Previous issue date: 19A crescente demanda por energia, bem como a busca por alternativas energéticas ao uso dos combustíveis fósseis, ampliaram o debate acerca das fontes renováveis de energia. Destaca-se, neste cenário, a energia eólica. A geração eólica depende da velocidade do vento, uma variável que não pode ser controlada e que apresenta grande variabilidade, em função do terreno, relevo, altitude e da sua natureza aleatória. Ela também não pode garantir um montante fixo de energia ao sistema elétrico. Para contornar esta deficiência, o operador da rede elétrica deve manter uma capacidade de reserva na programação de despacho de forma a garantir o equilíbrio entre a carga e a geração de energia. Dessa forma, trabalhos relativos ao impacto da anemometria no potencial de geração da energia eólica, bem como nos custos de geração estimados são atualmente muito discutidos na literatura (KAIGUI et al., 2012; OYEDEPO, 2012; YETER et al., 2012; CALLAWAY, 2010; SILVA et al., 2010). Este fato se torna mais evidente, principalmente quando se procura fomentar o desenvolvimento de fontes limpas de geração de energia. O objetivo principal da pesquisa é determinar a forma pela qual o perfil de vento e a densidade do ar afetam a geração, bem como o potencial de geração por regiões brasileiras.In order to reduce the dependence on fossil fuels, the growing demand for energy as well as the pursuit for energy alternatives, the debate about renewable energy has expanded. It is noteworthy, in this scenario, the wind power. The wind generation depends on wind speed, a variable that cant be controlled and which is characterized by variability, since it depends on the topography in addition to its intrinsic random nature. As a consequence, a wind energy generation facility cant guarantee a fixed amount of energy to the electrical system. To overcome this deficiency, the electric grid operator must maintain a reserve capacity in order to ensure the balance between load and generation of energy. There are many studies focusing on the impact of anemometry on the potential for wind energy generation as well as on the estimated generation costs (KAIGUI et al., 2012; OYEDEPO, 2012; YETER et al., 2012; CALLAWAY, 2010; SILVA et al., 2010). This fact becomes more evident, especially when it encourages the development of clean energy sources. The main objective of the research is to determine how the wind profile and the air desnsity affects the Geration potential in two Brazilian regions

Forecasting Hierarchical Time Series in Power Generation

Academic attention is being paid to the study of hierarchical time series. Especially in the electrical sector, there are several applications in which information can be organized into a hierarchical structure. The present study analyzed hourly power generation in Brazil (2018–2020), grouped according to each of the electrical subsystems and their respective sources of generating energy. The objective was to calculate the accuracy of the main measures of aggregating and disaggregating the forecasts of the Autoregressive Integrated Moving Average (ARIMA) and Error, Trend, Seasonal (ETS) models. Specifically, the following hierarchical approaches were analyzed: (i) bottom-up (BU), (ii) top-down (TD), and (iii) optimal reconciliation. The optimal reconciliation models showed the best mean performance, considering the primary predictive windows. It was also found that energy forecasts in the South subsystem presented greater inaccuracy compared to the others, which signals the need for individualized models for this subsystem

Análise da Volatilidade do Retorno da Commodity Dendê: 1980-2008

The agricultural prices variation analysis is essential on the formulation of public policies and business decisions. Considering the strategic importance of the biofuels for the Brazilian economy and the social objectives of the biofuel programs for familiar agriculture, this study aims to quantify the volatility of palm oil prices and the reaction towards clash under ARCH and GARCH models. The results for palm oil quotes show that clashes of volatility last for long periods of time, increasing the risk for farmers