With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.
MULTIFILE
One potential renewable energy resource is green gas production throughanaerobic digestion (AD). However, only part of the biogas produced (up to50-60%) contains the combustible methane; the remainder are incombustiblegasses with the biggest being carbon dioxide. These gasses are often not usedand expelled in the atmosphere. Through the use of BIO-P2M where hydrogenis mixed with the remaining CO2 additional methane can be produced,increasing the yield and using the feedstocks more effectively. Within thisresearch the environmental sustainability and effectiveness of BIO-P2M isevaluated using the MEFA and aLCA method, expressed in; net green gasproduction, efficiency in (P)EROI, emissions in GWP100, and environmentalimpact in Ecopoints. The functional unit is set as a normal cubic meter ofGroningen quality natural gas. Results indicate a net improvement of allindicators when applying BIO-P2M in several configurations (in situ, ex situ).When allocating the production of renewable energy to the BIO-P2M systemenvironmental impacts for wind the results are still positive; however, whenusing solar PV as an energy source the environmental impact in Ecopointsexceeds that of the reference case of Groningen natural gas. An additionaloption for improving the indicators is optimization of the process. When usingBIO-P2M combined with heat and power unit for producing the internalelectricity and heat demands all indicators are improved substantially. On anational scale when utilizing al available waste materials for the BIO-P2Msystem around 1217 MNm3/a of green gas can be produced, which is 3% ofthe total yearly consumption in the Netherlands and around 60% more thanwhen using normal AD systems. Within the context BIO-P2M is an interestingoption for increasing green gas output and improving the overall sustainabilityof the AD process. However, the source of green electricity needs to be takeninto account and process optimization can ensure better environmentalperformance.
A symbiotic relationship between human factors and safety scientists is needed to ensure the provision of holistic solutions for problems emerging in modern socio-technical systems. System Theoretic Accident Model and Processes (STAMP) tackles both interactions and individual failures of human and technological elements of systems. Human factors topics and indicative models, tools and methods were reviewed against the approach of STAMP. The results showed that STAMP engulfs many human factors subjects, is more descriptive than human factors models and tools, provides analytical power, and might be further improved by including more aspects of human factors. STAMP can serve in minimizing the gap between human factors and safety engineering sciences, which can collectively offer inclusive solutions to the industry.
