The OBAA Standard for Developing Repositories of Learning Objects: the Case of Ocean Literacy in Azores

This paper describes the existing web resources of learning objects to promote ocean literacy. The several projects and sites are explored, and the shortcomings revealed. The limitations identified include insufficient metadata about registered learning objects and lack of support for intelligent applications. As solution, we promote the seaThings project that relies on a multi-disciplinary approach to promote literacy in the marine environment by implementing a specific Learning Objects repositories (LOR) and a federation of repositories (FED), supported by a OBAA, a versatile and innovative standard that will provide the necessary support for intelligent applications for education purposes, to be used in schools and other educational institutions.info:eu-repo/semantics/publishedVersio

The COVID-19 pandemic and professional nursing practice in the context of hospitals

The COVID-19 pandemic has imposed challenges to health systems and institutions, which had to quickly create conditions to meet the growing health needs of the population. Thus, this study aimed to assess the impact of COVID-19 on professional nursing practice environments and to identify the variables that affected their quality. Quantitative, observational study, conducted in 16 Portuguese hospitals, with 1575 nurses. Data were collected using a questionnaire and participants responded to two different moments in time: the pre-pandemic period and after the fourth critical period of COVID-19. The pandemic had a positive impact on the Structure and Outcome components, and a negative trend in the Process component. The variables associated with the qualification of the components and their dimensions were predominantly: work context, the exercise of functions in areas of assistance to COVID-19 patients, length of professional experience and length of experience in the service. The investment in professional practice environments impacted the improvement of organizational factors, supporting the development of nurses’ work towards the quality of care. However, it is necessary to invest in nurses’ participation, involvement and professional qualifications, which are aspects strongly dependent on the institutions’ management strategies.info:eu-repo/semantics/publishedVersio

The COVID-19 Pandemic and Professional Nursing Practice in the Context of Hospitals

The COVID-19 pandemic has imposed challenges to health systems and institutions, which had to quickly create conditions to meet the growing health needs of the population. Thus, this study aimed to assess the impact of COVID-19 on professional nursing practice environments and to identify the variables that affected their quality. Quantitative, observational study, conducted in 16 Portuguese hospitals, with 1575 nurses. Data were collected using a questionnaire and participants responded to two different moments in time: the pre-pandemic period and after the fourth critical period of COVID-19. The pandemic had a positive impact on the Structure and Outcome components, and a negative trend in the Process component. The variables associated with the qualification of the components and their dimensions were predominantly: work context, the exercise of functions in areas of assistance to COVID-19 patients, length of professional experience and length of experience in the service. The investment in professional practice environments impacted the improvement of organizational factors, supporting the development of nurses’ work towards the quality of care. However, it is necessary to invest in nurses’ participation, involvement and professional qualifications, which are aspects strongly dependent on the institutions’ management strategiesinfo: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 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

Probabilistic-based assessment of existing steel-concrete composite bridges – Application to Sousa River Bridge

This paper presents a framework to assess the safety of existing structures, combining deterministic model identification and reliability assessment techniques, considering both load-test and complementary laboratory test results. Firstly, the proposed framework, as well as the most significant uncertainty sources are presented. Then, the developed model identification procedure is described. Reliability methods are then used to compute structural safety, considering the updated model from model identification. Data acquisition, such as that collected by monitoring, non-destructive or material characterization tests, is a standard procedure during safety assessment analysis. Hence, Bayesian inference is introduced into the developed framework, in order to update and reduce the statistical uncertainty. Lastly, the application of this framework to a case study is presented. The example analyzed is a steel and concrete composite bridge. The load test, the developed numerical model and the obtained results are discussed in detail. The use of model identification allows the development of more reliable structural models, while Bayesian updating leads to a significant reduction in uncertainty. The combination of both methods allows for a more accurate assessment of structural safety(undefined)info:eu-repo/semantics/publishedVersio

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved