Herramientas para la implementación del sistema integrado de gestión para el sector secundario dedicado a la producción de materiales de construcción

Con el presente documento, se propone herramientas para la implementación de un sistema HSEQ, es decir, la implementación de las normas ISO 9001, 14001 Y 45001, en una empresa del sector secundario dedicada a la producción de materiales de construcción. La empresa auditada tiene varios procesos de producción, pero el estudio se enfoca en la línea de producción de pegante cerámico y boquilla. Así entonces, en un primer plano se investiga y describe la empresa, sus características, riesgos, stake holders y se propone un modelo de ciclo PHVA al proceso de integración de los sistemas de gestión dentro de la organización, seguidamente se implementa un sistema de bioseguridad, se formula el plan de integración y finalmente, se realizan ciertas recomendaciones.With this document, tools are proposed for the implementation of an HSEQ system, that is, the implementation of the ISO 9001, 14001 and 45001 standards, in a secondary sector company dedicated to the production of construction materials. The audited company has several production processes, but the study focuses on the ceramic glue and nozzle production line. Thus, in the foreground, the company is investigated and described, its characteristics, risks, stake holders and a PDCA cycle model is proposed to the process of integration of management systems within the organization, then a biosafety system is implemented , the integration plan is formulated and finally, certain recommendations are made

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