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4 results for “Force-Field”
Suppression force-fields and diffuse competition: Competition de-escalation is an evolutionarily stable strategy
<p><span>Competition theory is founded on the premise that individuals benefit from harming their competitors, which helps them secure resources and prevent inhibition by neighbours. When multiple individuals compete, however, competition has complex indirect effects that reverberate through competitive neighbourhoods. The consequences of such "diffuse" competition are poorly understood. For example, competitive effects may dilute as they propagate through a neighbourhood, weakening benefits of neighbour suppression. Another possibility is that competitive effects may rebound on strong competitors, as their inhibitory effects on their neighbours benefit other competitors in the community. Diffuse competition is unintuitive in part because we lack a clear conceptual framework for understanding how individual interactions manifest in communities of multiple competitors. Here, I use mathematical and agent-based models to illustrate that diffuse interactions—as opposed to direct pairwise interactions—are likely the dominant mode of interaction among multiple competitors. Consequently, competitive effects may regularly rebound, incurring fitness costs under certain conditions, especially when kin-kin interactions are common. These models provide a powerful framework for investigating competitive ability and its evolution and produce clear predictions in ecologically realistic scenarios.</span></p>
Suppression force-fields and diffuse competition: Competition de-escalation is an evolutionarily stable strategy
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Screener and Enumerator with Force-Field Optimization (SEFFO): algorithm for searching adsorption sites and configurations on 2D materials
<p>See ref:</p> <p> </p> <p>(submission stage)</p>
Dataset and analyses for: Force-field perturbations and muscle vibration strengthen stability-related foot placement responses during steady-state gait in healthy adults
<p>We have collected kinematic data (heel and pelvis markers) from healthy adults during treadmill walking, whilst force-field perturbations and timed muscle vibrations were applied. We assessed the (after-)effects of these two interventions by evaluating foot placement control through outcome measures derived from a regression model which predicts foot placement based on the center-of-mass kinematic state. The data and analyses provided belong to the scientific publication: "Force-field perturbations and muscle vibration strengthen stability-related foot placement responses during steady-state gait in healthy adults". In the manuscript we place these analyses in the context of stability control and speculate on how these trainining interventions may improve stability control in patient populations.</p>
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