Theoretical foundations in support of small and medium towns

This theoretical review aims to create a comprehensive and systematic analysis based on previously published literature explaining how contemporary technological developments may promote new paths for small and medium-sized towns (SMTs) and their networking systems. Much has been said concerning the capacity of towns to absorb strategic knowledge, which is highly dependent on local governance systems. In this paper, five levels of multidisciplinary approaches will be addressed so as to pinpoint the theoretical grounds for the promotion and advocacy of small and medium-sized towns (SMTs) as major drivers of regional sustainability: agglomeration advantages and networking efficiencies-representing strict economic accounting of cost and benefits; clustering in a context of online environments, and its extension to open networking systems; sustainable innovation processes for SMTs, technology, and knowledge transfer in open innovation systems-both settings for discussions within the framing of new technological developments and artificial intelligence; knowledge and new technological developments with local spillovers-to be enhanced employing new educational programs and learning diffusion at advanced levels; the social functions of small and medium-sized towns-to be addressed in the areas of sociology, architecture, and planning.info:eu-repo/semantics/publishedVersio

How corporations deal with reporting sustainability: Assessment using the multicriteria logistic biplot approach

This paper suggests a new methodology capable of accessing in detail the contribution of companies to countries' sustainability related to economic performance. The concept of sustainability has been brought up in several debates, leading to a clearer understanding of its progress in recent decades. The most adequate indicators to achieve a unique value to define sustainability have been identified. However, specific behaviors of economic agents such as exist in particularly large organizations, have rarely been exposed and evaluated regarding their positive or negative contribution to the increase of sustainability throughout the world. This paper proposes an integrated approach incorporating an evaluation of the positive and negative contributions to sustainability by means of a logistic biplot application. This allows the creation of a summarized index that combines all single sustainability indicators. These synthetic indices allow the positioning of each of the companies in a geometric representation for an original exploration of the sustainability paradigm. The supplied method permits accessing and evaluating information concerning specific behaviors of economic agents such as big companies. In our paper, we have followed the engagements towards sustainability of big corporations, individually or as groups, across the different activity sectors in Portugal and Spain

New methods for resilient societies: The geographical analysis of injury data

In this paper an empirical assessment of injury patterns is supplied as an example of social endurance -resilient societies can be built by means of geographical analysis of injury data, providing better support for decision makers regarding urban safety. Preventing road traffic collisions with vulnerable road users, such as pedestrians, could help mitigate significant loses and improve infrastructure planning. In this sense, the geographical aspects of injury prevention are of clear spatial analog, and should be tested regarding the carrying capacity of urban areas as well as vulnerability for growing urban regions. The application of open source development tool for spatial analysis research in health studies is addressed. The study aims to create a framework of available open source tools through Python that enable better decision making through a systematic review of existing tools for spatial analysis. Methodologically, spatial autocorrelation indices are tested as well as influential variables are brought forward to establish a better understanding of the incremental concern of injuries in rural areas, in general, and in the Greater Toronto Area, in particular. By using Python Library for Spatial Analysis (PySAL), an integrative vision of assessing a growing epidemiological concern of injuries in Toronto, one of North America's fastest growing economic metropolises is offered. In this sense, this study promotes the use of PySAL and open source toolsets for integrating spatial analysis and geographical analysis for health practitioners. The novelty and capabilities of open source tools through methods such as PySAL allow for a cost efficiency as well as give planning an easier methodological toolbox for advances spatial modelling techniques.info:eu-repo/semantics/publishedVersio

Innovation for Sustainability and Networking

Throughout human history, innovation has been the main factor in adapting humanity to its settings. On the basis of earlier practice, human creativity allows the finding of new, permanent ways to do things. their applications encourage new spaces, new necessities and new lifestyles. Innovation has been an element of human capacities from its earlier stages, but it has been recognized only recently as a clear device of social and economic change

Sessão V - Ensino Superior

A Sessão V concentrou-se no sistema de ensino a jusante do secundário, nomeadamente o politécnico e universitário. Pretendeu-se aqui também suscitar uma ampla participação de todas as forças vivas que compõem estes sistemas, focalizada nas consequências para os seus projectos educativos do grau de maturidade do desenvolvimento dos três valores nos seus estudantes

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