Development of a web application for the optimization of administrative processes: application of the lean methodology for priority classification

This study is part of the context of the Research Project signed between the Ministry of Agriculture, Livestock and Supply (MAPA) and the Federal Institute of Education, Science and Technology Goiano (IF Goiano) for the development of methodologies with a view to implement and operate an agreement monitoring center. Its objective is to present the tool (software) developed via web to organize the structure of the chain of value of the Ministry of Agriculture's system of agreements, map performance indicators and classify process optimization priorities, based on parameters of efficiency and effectiveness of the reengineering processes. This is applied research in the empirical scenario of the Agreements Sector of MAPA, which uses the case study procedure to achieve its objective. The software development is based on the theoretical framework of Business Process Management (BPM) and Lean Six Sigma, with the application of the GUT Matrix tool and the Eisenhower Matrix for decision making. The primary data were collected through unstructured interviews with nine key informants working in the macro-processes of formalization, execution and monitoring and rendering of accounts of agreements. The results contain the characterization of the MAPA agreement area, highlighting the main activities carried out in the three macroprocesses, the description of the modelling characteristics and functionalities of the developed software and the discussion of benefits arising from the application of information technologies to the Business Process Management (BPM).  It can be inferred that the Lean methodology is plausible as a logical “production line”, since it is adaptable to a structured algorithm, which provides an orderly solution of problems focused on continuous improvement

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

<b>Controle ótimo em reator batelada alimentada para a hidrólise enzimática da celobiose</b> - DOI: 10.4025/actascitechnol.v25i1.2236

A glicose pode ser obtida através da hidrólise enzimática pelas enzimas do complexo celulase. a hidrólise da celobiose pela celobiase exibe inibição pelo substrato e pelo produto, que reduzem a performance da reação. os trabalhos sobre esta hidrólise enzimática disponíveis na literatura foram conduzidos em reatores batelada, portanto um estudo sobre a possibilidade de utilização de outro tipo de reator ainda não foi realizado para esta hidrólise. o objetivo deste trabalho é analisar o uso de um reator batelada alimentada para esta reação enzimática. a política de alimentação foi determinada utilizando a teoria de controle ótimo, onde a conversão do substrato e a concentração do produto final foram maximizadas. os resultados simulados foram comparados com dados experimentais obtidos por calsavara <em>et al</em>. (1999) em um reator batelada e indicaram que a inibição pelo substrato se sobrepõe a inibição pelo produto. a operação em batelada alimentada mostrou-se vantajosa em algumas situações

Controlled release of microencapsulated citronella essential oil on cotton and polyester matrices

Microencapsulated finishes are an important element in the development of new textiles. In this context, a large area to be explored is microencapsulation of essential oils in textiles. This technique offers the possibility of developing new products with many advantages over traditional fabrics, as traditional finishing may be ineffective for reasons related to uncontrolled release of the active principle while microencapsulation aims to achieve increased duration of the finishing effect. However, many studies present only the application of microcapsules in a textile but do not report how the release of the encapsulated material occurs or the influence of the textile matrix. This paper reports the mechanism and kinetics of controlled release of microencapsulated citronella oil in cotton and polyester. The microencapsulation was done by complex coacervation with gelatin and gum Arabic as shell materials. The resulting microcapsules were analyzed by optical microscopy, scanning electron microscopy, thermogravimetric analysis, and dynamic light scattering. They were then applied in cotton and polyester and evaluated by attenuated total reflection Fourier-transform infrared spectroscopy. Finally, the controlled release of citronella from the microcapsules deposited on the fabrics was studied in vitro. It was found that the release was directly influenced by the type of fiber: the microcapsules in polyester showed diffusion by a Fickian mechanism, while a non-Fickian kinetic model fit for the modified cotton. Comprehension of such controlled release processes is fundamental for achieving and developing more durable finishing effectsPostprint (author's final draft

Controlled release of microencapsulated citronella essential oil on cotton and polyester matrices

Microencapsulated finishes are an important element in the development of new textiles. In this context, a large area to be explored is microencapsulation of essential oils in textiles. This technique offers the possibility of developing new products with many advantages over traditional fabrics, as traditional finishing may be ineffective for reasons related to uncontrolled release of the active principle while microencapsulation aims to achieve increased duration of the finishing effect. However, many studies present only the application of microcapsules in a textile but do not report how the release of the encapsulated material occurs or the influence of the textile matrix. This paper reports the mechanism and kinetics of controlled release of microencapsulated citronella oil in cotton and polyester. The microencapsulation was done by complex coacervation with gelatin and gum Arabic as shell materials. The resulting microcapsules were analyzed by optical microscopy, scanning electron microscopy, thermogravimetric analysis, and dynamic light scattering. They were then applied in cotton and polyester and evaluated by attenuated total reflection Fourier-transform infrared spectroscopy. Finally, the controlled release of citronella from the microcapsules deposited on the fabrics was studied in vitro. It was found that the release was directly influenced by the type of fiber: the microcapsules in polyester showed diffusion by a Fickian mechanism, while a non-Fickian kinetic model fit for the modified cotton. Comprehension of such controlled release processes is fundamental for achieving and developing more durable finishing effect