Tootfinder

Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels
Till Aust, Christoph Karl Heck, Eduard Buss, Heiko Hamann
https://arxiv.org/abs/2509.24992

Embedded Deep Learning for Bio-hybrid Plant Sensors to Detect Increased Heat and Ozone Levels
We present a bio-hybrid environmental sensor system that integrates natural plants and embedded deep learning for real-time, on-device detection of temperature and ozone level changes. Our system, based on the low-power PhytoNode platform, records electric differential potential signals from Hedera helix and processes them onboard using an embedded deep learning model. We demonstrate that our sensing device detects changes in temperature and ozone with good sensitivity of up to 0.98. Daily and …

Stochastic Dynamics of Urban Predator-Prey Systems: Integrating Human Disturbance and Functional Responses
Arhonefe Joseph Ogethakpo, Ozioma Ogoegbulem, Sarduana Joshua Apanapudor, Helen Onovwerosuoke Sanubi
https://arxiv.org/abs/2509.18217

Stochastic Dynamics of Urban Predator-Prey Systems: Integrating Human Disturbance and Functional Responses
Urban ecosystems exhibit complex predator-prey dynamics increasingly disrupted by anthropogenic disturbances (e.g., noise, habitat fragmentation). Classical Lotka-Volterra (LV) models fail to capture these human-induced stressors, and integrated frameworks incorporating functional responses, stochasticity, and spatial dynamics remain scarce. We develop a comprehensive stochastic model to quantify how human disturbance reshapes predator-prey interactions in urban landscapes, using rat-cat system…