Tootfinder

Communication System Design using Synthetic Photoisomerizable Azobenzene-Regulated K (SPARK) channel
Taha Sajjad, Andrew W. Eckford
https://arxiv.org/abs/2510.08971 https://

Communication System Design using Synthetic Photoisomerizable Azobenzene-Regulated K+(SPARK) channel
Biomolecules exhibit a remarkable property of transforming signals from their environment. This paper presents a communication system design using a light-modulated protein channel: Synthetic Photoisomerizable Azobenzene-regulated K+ (SPARK). Our approach involves a comprehensive design incorporating the SPARK-based receiver, encoding methods, modulation techniques, and detection processes. By analyzing the resulting communication system, we determine how different parameters influence its perf…

Improving the State of the Art for Training Human-AI Teams: Technical Report #5 -- Individual Differences and Team Qualities to Measure in a Human-AI Teaming Testbed
Lillian Asiala, James E. McCarthy
https://arxiv.org/abs/2507.18878

Improving the State of the Art for Training Human-AI Teams: Technical Report #5 -- Individual Differences and Team Qualities to Measure in a Human-AI Teaming Testbed
Sonalysts, Inc. (Sonalysts) is working on an initiative to expand our expertise in teaming to include Human-Artificial Intelligence (AI) teams. The first step of this process is to develop a Synthetic Task Environment (STE) to support our original research. Prior knowledge elicitation efforts within the Human-AI teaming research stakeholder community revealed a desire to support data collection using pre- and post-performance surveys. In this technical report, we review a number of constructs t…

Agent-Based Exploration of Recommendation Systems in Misinformation Propagation
Lise Jakobsen, Anna Johanne Holden, \"Onder G\"urcan, \"Ozlem \"Ozg\"obek
https://arxiv.org/abs/2507.21724

Agent-Based Exploration of Recommendation Systems in Misinformation Propagation
This study uses agent-based modeling to examine the impact of various recommendation algorithms on the propagation of misinformation on online social networks. We simulate a synthetic environment consisting of heterogeneous agents, including regular users, bots, and influencers, interacting through a social network with recommendation systems. We evaluate four recommendation strategies: popularity-based, collaborative filtering, and content-based filtering, along with a random baseline. Our res…