Dispersion force for materials relevant for micro and nanodevices fabrication

The dispersion (van der Waals and Casimir) force between two semi-spaces are calculated using the Lifshitz theory for different materials relevant for micro and nanodevices fabrication, namely, gold, silicon, gallium arsenide, diamond and two types of diamond-like carbon (DLC), silicon carbide, silicon nitride and silicon dioxide. The calculations were performed using recent experimental optical data available in the literature, usually ranging from the far infrared up to the extreme ultraviolet bands of the electromagnetic spectrum. The results are presented in the form of a correction factor to the Casimir force predicted between perfect conductors, for the separation between the semi-spaces varying from 1 nanometre up to 1 micrometre. The relative importance of the contributions to the dispersion force of the optical properties in different spectral ranges is analyzed. The role of the temperature for semiconductors and insulators is also addressed. The results are meant to be useful for the estimation of the impact of the Casimir and van der Waals forces on the operational parameters of micro and nanodevices

Making use of fuzzy cognitive maps in agent-based modeling

One of the main challenges in Agent-Based Modeling (ABM) is to model agents’ preferences and behavioral rules such that the knowledge and decision-making processes of real-life stakeholders will be reflected. To tackle this challenge, we demonstrate the potential use of a participatory method, Fuzzy Cognitive Mapping (FCM), that aggregates agents’ qualitative knowledge (i.e., knowledge co-production). In our proposed approach, the outcome of FCM would be a basis for designing agents’ preferences and behavioral rules in ABM. We apply this method to a social-ecological system of a farming community facing water scarcity

Physical constraints of cultural evolution of dialects in killer whales

Data collection was supported by a variety of organizations, including the Russian Fund for the Fundamental Research (Grant No. 15-04-05540), the Rufford Small Grants Fund, Whale and Dolphin Conservation, the Fundação para a Ciência e a Tecnologia (Grant No. SFRH/BD/30303/2006), Russell Trust Award of the University of St. Andrews, the Office of Naval Research, the Icelandic Research Fund (i. Rannsóknasjóður), the National Geographic Society Science and Exploration Europe (Grant No. GEFNE65-12), Vancouver Aquarium Marine Science Centre, the Canadian Ministry of Fisheries and Oceans, and the North Gulf Oceanic Society.Odontocete sounds are produced by two pairs of phonic lips situated in soft nares below the blowhole; the right pair is larger and is more likely to produce clicks, while the left pair is more likely to produce whistles. This has important implications for the cultural evolution of delphinid sounds: the greater the physical constraints, the greater the probability of random convergence. In this paper the authors examine the call structure of eight killer whale populations to identify structural constraints and to determine if they are consistent among all populations. Constraints were especially pronounced in two-voiced calls. In the calls of all eight populations, the lower component of two-voiced (biphonic) calls was typically centered below 4 kHz, while the upper component was typically above that value. The lower component of two-voiced calls had a narrower frequency range than single-voiced calls in all populations. This may be because some single-voiced calls are homologous to the lower component, while others are homologous to the higher component of two-voiced calls. Physical constraints on the call structure reduce the possible variation and increase the probability of random convergence, producing similar calls in different populations.PostprintPeer reviewe

Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.</jats:p

Agent‐based modeling to integrate elements from different disciplines for ambitious climate policy

Ambitious climate mitigation policies face social and political resistance. One reason is that existing policies insufficiently capture the diversity of relevant insights from the social sciences about potential policy outcomes. We argue that agent-based models can serve as a powerful tool for integration of elements from different disciplines. Having such a common platform will enable a more complete assessment of climate policies, in terms of criteria like effectiveness, equity and public support

Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices