Application of Predicted Models in Debt Management: Developing a Machine Learning Algorithm to Predict Customer Risk at EDP Comercial

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis report is a result of a nine-month internship at EDP Comercial where the main project of research was the application of artificial intelligence tools in the field of debt management. Debt management involves a set of strategies and processes aimed at reducing or eliminating debt and the use of artificial intelligence has shown great potential to optimize these processes and minimize the risk of debt for individuals and organizations. In terms of monitoring and controlling the creditworthiness and quality of clients, debt management has mainly been responsive and reactive, attempting to recover losses after a client has become delinquent. There is a gap in the knowledge of how to proactively identify at-risk accounts before they fall behind on payments. To avoid the constant reactive response in the field, it was developed a machine-learning algorithm that predicts the risk of a client becoming in debt by analyzing their scorecard, which measures the quality of a client based on their infringement history. After preprocessing the data, XGBoost was implemented to a dataset of 3M customers with at least one active contract on EDP, on electricity or gas. Hyperparameter tuning was performed on the model to reach an F1 score of 0.7850 on the training set and 0.7835 on the test set. The results were discussed and based on those, recommendations and improvements were also identified

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

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

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Redução da agitação nas pessoas idosas com demência durante os cuidados de higiene: Contributo da Metodologia de Cuidar Humanitude

Introdução: O envelhecimento revela-se um desafio para os cuidadores devido ao aumento de pessoas cuidadas com um nível de dependência elevado e com comportamentos, associados a patologias neurodegenerativas, como a demência. Neste sentido, é crucial capacitar os enfermeiros com metodologias de cuidar inovadoras com técnicas relacionais que operacionalizem e sistematizem a relação. Objetivos: Pretende-se identificar e analisar as evidências científicas, atualmente existentes, relacionadas com a agitação durante os cuidados de higiene, bem como compreender qual o contributo da Metodologia Cuidar Humanitude na redução da agitação das pessoas cuidadas durante os cuidados de higiene. Métodos: Foi realizada uma Revisão Integrativa da Literatura, cuja finalidade consistiu em responder à questão de investigação: “Qual o contributo da Metodologia de Cuidar Humanitude na redução da agitação durante os cuidados de higiene, nas pessoas idosas dependentes com demência?”. Principais resultados: De um total de 70 artigos após a aplicação dos critérios de inclusão e exclusão obteve-se um total de 5 artigos. Conclusões: Os estudos evidenciaram que com a implementação da Metodologia de Cuidar Humanitude® há uma redução dos comportamentos de agitação nas pessoas com demência, durante os cuidados de higiene, devido à intencionalidade na relação e a uma maior compreensão da pessoa cuidada, levando a uma maior aceitação e participação nos cuidados

Redução da agitação nas pessoas idosas com demência durante os cuidados de higiene: Contributo da Metodologia de Cuidar Humanitude

Introdução: O envelhecimento revela-se um desafio para os cuidadores devido ao aumento de pessoas cuidadas com um nível de dependência elevado e com comportamentos, associados a patologias neurodegenerativas, como a demência. Neste sentido, é crucial capacitar os enfermeiros com metodologias de cuidar inovadoras com técnicas relacionais que operacionalizem e sistematizem a relação. Objetivos: Pretende-se identificar e analisar as evidências científicas, atualmente existentes, relacionadas com a agitação durante os cuidados de higiene, bem como compreender qual o contributo da Metodologia Cuidar Humanitude na redução da agitação das pessoas cuidadas durante os cuidados de higiene. Métodos: Foi realizada uma Revisão Integrativa da Literatura, cuja finalidade consistiu em responder à questão de investigação: “Qual o contributo da Metodologia de Cuidar Humanitude na redução da agitação durante os cuidados de higiene, nas pessoas idosas dependentes com demência?”. Principais resultados: De um total de 70 artigos após a aplicação dos critérios de inclusão e exclusão obteve-se um total de 5 artigos. Conclusões: Os estudos evidenciaram que com a implementação da Metodologia de Cuidar Humanitude® há uma redução dos comportamentos de agitação nas pessoas com demência, durante os cuidados de higiene, devido à intencionalidade na relação e a uma maior compreensão da pessoa cuidada, levando a uma maior aceitação e participação nos cuidados