Melhoria e automatização da ferramenta ErgoSafeCI

A realização desta Tese tem por base a melhoria de uma ferramenta (ErgoSafeCI) que contém várias medidas de caracter operacional, de métodos e ferramentas Lean combinadas com a segurança e a ergonomia nos locais de “criação de valor”, como por exemplo uma linha de produção. Como o objetivo da ferramenta passa por apoiar a melhoria de um posto de trabalho considerando os aspetos ergonómicos e de segurança, tornou-se então também necessário abordar a ferramenta com o objetivo de a tornar mais user friendly e mais intuitiva, de maneira a que qualquer pessoa menos instruída a possa utilizar. A melhoria da ferramenta passa por: tornar o processo de realização do questionário mais simples e intuitivo através da utilização de userforms e de código VBA; tornar a apresentação da ferramenta mais clean e também automatizar o processo de obtenção da avaliação final e das áreas a trabalhar. A busca incessante pela melhoria da eficiência dos processos e pela melhoria do bemestar dos trabalhadores, fez com que os princípios e os métodos Lean, assim como a segurança e a ergonomia fossem exaustivamente estudados e trabalhados para atingir esse objetivo. Isto consegue-se recorrendo a um processo interativo entre a teoria e os conhecimentos práticos. O significado da obtenção de uma alta pontuação através desta ferramenta significa que existe uma boa relação entre a metodologia Lean, a segurança e a ergonomia. Para que a implementação ocorra com sucesso, os responsáveis pela implementação devem começar com uma avaliação Lean e acompanhá-la regularmente. Existe a possibilidade de realizar uma avaliação com recurso a várias ferramentas Lean existentes, no entanto com recurso à ferramenta apresentada neste trabalho, é possível proceder a uma avaliação que avalie a parte Lean, a segurança no trabalho e a ergonomia em simultâneo. Este trabalho teve ainda uma contribuição científica através da escrita de um artigo que se encontra aprovado para publicação. A elaboração deste artigo permitiu desenvolver competências, como capacidade de trabalhar sob pressão e resolução de problemas. Permitiu ainda desenvolver o espírito de trabalho em equipa, visto que foi desenvolvido com outros colegas.The execution of this thesis has the improvement of the tool (ErgoSafeCI) as a basis, which contains several measures of an operational character, of methods and Lean tools combined with security and ergonomics in the locations of "value creation", for example on a production line. As the objective of the tool is to improve a workplace by improving processes, improving the ergonomics of the station, reducing waste and among other possibilities, it also became necessary to approach the tool with the aim of making it more user friendly and more intuitive, so that anyone less educated can use it. The improvement of the tool involves: making the process of conducting the questionnaire simpler and more intuitive, through the use of userforms and VBA code; make the presentation of the tool cleaner and also automate the process of obtaining the final assessment and the fields to be worked on. The ceaseless search for an improvement of the tactic’s efficiency and the well-being of the workers, has made the principles and Lean methods, as well as the security and ergonomics to be exhaustively studied and worked on to achieve this goal. This can happen using an interactive procedure between the theory and the practical knowledge. The meaning of obtaining a high score through this tool means that there is a good relation between Lean methodology, security and ergonomics. For the implementation to be successful, the responsible people for the implementation should start a Lean evaluation and accompany her regularly. There is the possibility to carry out an evaluation using several existing Lean tools, however using the tool presented in this work, it is possible to carry out an evaluation that evaluates the Lean part, safety at work and ergonomics simultaneously. This work also had a scientific contribution through the writing of an article that is approved for publication. The preparation of this article allowed to develop skills, such as the ability to work under pressure and problem solving. It also allowed to develop the spirit of teamwork, since it was developed with other colleagues

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