The inherent and handled risks in tourism marketing: the role of sustainability as a risk reduction strategy in the intenational traveñ

[Abstract] The perceived risk has been studied in marketing since its introduction in 1960, especially about its influence in the consumer behavior. However, the theory of perceived risk still has some gaps, even being the subject of several studies in the area for more than four decades. In the tourism area, the risk is present in consumer decisions, especially due to the characteristics of the activity, as inseparability, intangibility, variability and perishability. This study presents a contribution in this field, analyzing tourism marketing, a not complete explored area, the perceived risk in international travel and the sustainability as a risk reduction strategy. Since 1990, when sustainability became a key concept for a number of areas, the need for multidisciplinarity has grown. Studies that provide knowledge on the subject have received emphasis, given the importance of research on the construct, especially on the consumer's perspective. The study is descriptive, cross sectional study. The sample was selected in Brazil, using students of tourism and administration of a Federal University. The results of this study may contribute for an improved understanding of the image provided by the 'green label' in the tourism industry. These data also provide important information that may help defining new incentive policies, contributing to the improvement of offered services and, consequently, the image of the region.[Resumo] Os Riscos Inerentes e Manipulados no Marketing Turístico: o Papel da Sustentabilidade como uma Estratégia de Redução do Risco Percebido em Viagens Internacionais. O risco percebido tem sido estudado na área de marketing desde sua introdução, em 1960, em especial quanto a sua influência no comportamento do consumidor. Todavia, a teoria do risco percebido ainda apresenta questões não respondidas, mesmo sendo objeto de várias pesquisas na área há mais de quatro décadas. Na área de turismo, o construto risco está presente nas decisões dos consumidores, especialmente pelas próprias características da atividade, como inseparabilidade, intangibilidade, variabilidade e perecibilidade. Esta pesquisa apresenta então uma contribuição neste campo do conhecimento, ao analisar, dentro do marketing turístico, uma área pouco explorada, que é a questão do risco percebido em viagens internacionais e as estratégias de redução do risco percebido com a sustentabilidade. Desde 1990, quando a sustentabilidade se tornou um conceito chave para uma série de áreas, a necessidade de multidisciplinaridade tem crescido. Estudos que propiciem conhecimentos sobre o tema têm recebido ênfase, visto a importância de investigações sobre o construto, especialmente sobre a ótica do consumidor. O estudo é um corte transversal, com caráter descritivo. A amostra foi composta indivíduos de nacionalidade brasileira, estudantes do curso de turismo e administração de uma Universidade Federal. Os resultados encontrados neste estudo podem contribuir para um maior conhecimento sobre a imagem proporcionada pelos ‘rótulos verdes’ na indústria do turismo. Esses dados também trazem informações que poderão contribuir na definição de novas políticas de incentivo, para que contribuam com a melhoria dos serviços ofertados e, consequentemente, com a imagem da região

PUFFIN: A Path-Unifying Feed-Forward Interfaced Network for Vapor Pressure Prediction

Accurately predicting vapor pressure is vital for various industrial and environmental applications. However, obtaining accurate measurements for all compounds of interest is not possible due to the resource and labor intensity of experiments. The demand for resources and labor further multiplies when a temperature-dependent relationship for predicting vapor pressure is desired. In this paper, we propose PUFFIN (Path-Unifying Feed-Forward Interfaced Network), a machine learning framework that combines transfer learning with a new inductive bias node inspired by domain knowledge (the Antoine equation) to improve vapor pressure prediction. By leveraging inductive bias and transfer learning using graph embeddings, PUFFIN outperforms alternative strategies that do not use inductive bias or that use generic descriptors of compounds. The framework's incorporation of domain-specific knowledge to overcome the limitation of poor data availability shows its potential for broader applications in chemical compound analysis, including the prediction of other physicochemical properties. Importantly, our proposed machine learning framework is partially interpretable, because the inductive Antoine node yields network-derived Antoine equation coefficients. It would then be possible to directly incorporate the obtained analytical expression in process design software for better prediction and control of processes occurring in industry and the environment

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