Environmental sustainability in superior education: case study of Lisbon College of Health Technology

Over the centuries there has been a growing trend of societies and it is possible to verify their economic growth. This growth has provided an increased pressure on natural resources, often over-reaching the boundaries of each country, which has called into question the level of environmental sustainability in different countries. Sustainability is understood as a complex concept involving ecological, social, economic dimensions and temporal urban processes. Therefore, Firmino (2009) suggests that the ecological footprint (EF) allows people to establish dependency relations between human activities and the natural resources required for such activities and for the absorption of waste generated. According to Bergh & Verbruggen (1999) the EF is an objective, impartial and one-dimensional indicator that enables people to assess the sustainability. The Superior Schools have a crucial role in building the vision of a sustainable future as a reality, because in transmitting values and environmental principles to his students, are providing that they, in exercising his professional activity, make decisions weighing the environmental values. This ensures improved quality of life. The present study aims to determine the level of environmental sustainability of the Academic Community of Lisbon College of Health Technology (ESTeSL), by calculating the EF, and describe whether a relation between Footprint and various socio-demographic characteristics of the subjects.

Ecological footprint as an indicator of sustainability at Lisbon School of Health Technology, Portugal

Higher education institutions, has an active role in the development of a sustainable future and for this reason, it is essential that they became environmentally sustainable institutions, applying methods such as the Ecological Footprint analysis. This study intent is to strengthen the potential of the ecological footprint as an indicator of the sustainability of students of Lisbon School of Health Technology, and identify the relationship between the ecological footprint and the different socio-demographic variables.

Assessment of the quality of brain regions and neuroimaging metrics as biomarkers of Alzheimer’s disease

Alzheimer Disease (AD) is characterized by progressive cognitive decline and dementia. Earlier diagnosis and classification of different stages of the disease are currently the main challenges and can be assessed by neuroimaging. With this work we aim to evaluate the quality of brain regions and neuroimaging metrics as biomarkers of AD. Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox functionalities were used to study AD by T1weighted, Diffusion Tensor Imaging and 18FAV45 PET, with data obtained from the AD Neuroimaging Initiative database, specifically 12 healthy controls (CTRL) and 33 patients with early mild cognitive impairment (EMCI), late MCI (LMCI) and AD (11 patients/group). The metrics evaluated were gray-matter volume (GMV), cortical thickness (CThk), mean diffusivity (MD), fractional anisotropy (FA), fiber count (FiberConn), node degree (Deg), cluster coefficient (ClusC) and relative standard-uptake-values (rSUV). Receiver Operating Characteristic (ROC) curves were used to evaluate and compare the diagnostic accuracy of the most significant metrics and brain regions and expressed as area under the curve (AUC). Comparisons were performed between groups. The RH-Accumbens/Deg demonstrated the highest AUC when differentiating between CTRLEMCI (82%), whether rSUV presented it in several brain regions when distinguishing CTRL-LMCI (99%). Regarding CTRL-AD, highest AUC were found with LH-STG/FiberConn and RH-FP/FiberConn (~100%). A larger number of neuroimaging metrics related with cortical atrophy with AUC>70% was found in CTRL-AD in both hemispheres, while in earlier stages, cortical metrics showed in more confined areas of the temporal region and mainly in LH, indicating an increasing of the spread of cortical atrophy that is characteristic of disease progression. In CTRL-EMCI several brain regions and neuroimaging metrics presented AUC>70% with a worst result in later stages suggesting these indicators as biomarkers for an earlier stage of MCI, although further research is necessary.

The role of teaching decision analysis for sustainability in engeneering schools

This paper addresses the role that decision analysis plays in helping engineers to gain a greater understanding of the problems they face. The need of structured decision analysis is highlighted as well as the use of multiple criteria decision analysis to tackle sustainability issues with emphasis in the use of MACBETH approach. Some insights from a Portuguese Summer Course on engineering for sustainable development are presented namely the students 'and teacher perceptions about the module of decision analysis for sustainability.