Nature-Inspired Coordination Models: Current Status and Future Trends

Coordination models and languages are meant to provide abstractions and mechanisms to harness the space of interaction as one of the foremost sources of complexity in computational systems. Nature-inspired computing aims at understanding the mechanisms and patterns of complex natural systems in order to bring their most desirable features to computational systems. Thus, the promise of nature-inspired coordination models is to prove themselves fundamental in the design of complex computational systems|such as intelligent, knowledge-intensive, pervasive, adaptive, and self-organising ones. In this paper, we survey the most relevant nature-inspired coordination models in the literature, focussing in particular on tuple-based models, and foresee the most interesting research trends in the field

Processes models, environmental analyses, and cognitive architectures: Quo vadis quantum probability theory?

A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architecture

Ambient Water Monitoring Strategy for Iowa: 2016-2021

The Iowa Department of Natural Resources (DNR) Ambient Water Monitoring Program provides consistent, unbiased information about the condition of Iowa’s water resources to support decisions affecting the development, management and protection of these resources. To strengthen its services, the program worked with a variety of stakeholders and other DNR programs to develop a five-year strategy for Iowa’s ambient water monitoring efforts. The strategy identifies opportunities to improve the program’s effectiveness in several categories: monitoring objectives, sampling design, data management, products and services, and program evaluation and coordination. Iowa DNR managers and technical staff will use the new strategy to guide decisions affecting the ambient monitoring program over the next five years. The strategy should also serve as a robust informational resource for stakeholders, policy makers, legislators and the public

Reasoning in non-probabilistic uncertainty: logic programming and neural-symbolic computing as examples

This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty (and even more, that there are kinds of uncertainty which are for principled reasons not addressable with probabilistic means); and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: Logic Programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of Input/Output logic for dealing with uncertainty in dynamic normative context

Key performance indicators for sustainable manufacturing evaluation in automotive companies

The automotive industry is regarded as one of the most important and strategic industry in manufacturing sector. It is the largest manufacturing enterprise in the world and one of the most resource intensive industries of all major industrial system. However, its products and processes are a significant source of environmental impact. Thus, there is a need to evaluate sustainable manufacturing performance in this industry. This paper proposes a set of initial key performance indicators (KPIs) for sustainable manufacturing evaluation believed to be appropriate to automotive companies, consisting of three factors divided into nine dimensions and a total of 41 sub-dimensions. A survey will be conducted to confirm the adaptability of the initial KPIs with the industry practices. Future research will focus on developing an evaluation tool to assess sustainable manufacturing performance in automotive companies