Accessibility vs. Feasibility: Optimizing Function Allocation for Accommodation of Heterogeneous Populations

Human beings are physically and cognitively variable, leading to a wide array of potential system use cases. To design safe and effective systems for highly heterogeneous populations, engineers must cater to this variability to minimize the chance of error and system failure. This can be a challenge because of the increasing costs associated with providing additional product variety. Most guidance for navigating these trade-offs is intended for late-stage design, when significant resources have been expended, thus risking expensive redesign or exclusion of users when new human concerns become apparent. Despite the critical need to evaluate accommodation-cost trade-offs in early stages of design, there is currently a lack of structured guidance. In this work, an approach to function modeling is proposed that allows the simultaneous consideration of human and machine functionality. This modeling approach facilitates the allocation of system functions to humans and machines to be used as an accessible baseline for concept development. Further, a multi-objective optimization model was developed to allocate functions with metrics for accommodation and cost. The model was demonstrated on a design case study. 16 senior mechanical engineering students were recruited and tasked with performing the allocation task manually. The results were compared to the output of the optimization model. Results indicated that participants were unable to produce concepts with the same accommodation-cost efficiency as the optimization model. Further, the optimization model successfully produced a wide range of potential product concepts, demonstrating its utility as a decision-aid.

Fate and adaptive plasticity of heterogeneous resistant population of Echinochloa colona in response to glyphosate

Understanding the fate of heterogenous herbicide resistant weed populations in response to management practices can help towards overcoming the resistance issues. We selected one pair of susceptible (S) and resistant (R) phenotypes (2B21-R vs 2B21-S and 2B37-R vs 2B37-S) separately from two glyphosate resistant heterogeneous populations (2B21 and 2B37) of Echinochloa colona and their fate and adaptive plasticity were evaluated after glyphosate application. Our study revealed the glyphosate concentration required to cause a 50% plant mortality (LD50) was 1187, 200, 3064, and 192 g a. e. ha−1 for the four phenotypes 2B21-R, 2B21-S, 2B37-R, and 2B37-S respectively. Both S phenotypes accumulated more biomass than the R phenotypes at the lower application rates (34 and 67.5 g a. e. ha−1) of glyphosate. However, the R phenotypes generally produced more biomass at rates of glyphosate higher than 100 g a. e. ha−1 throughout the growth period. Plants from the R phenotypes of 2B21 and 2B37 generated 32% and 38% fewer spikesplant−1 than their respective S counterparts in the absence of glyphosate respectively. The spike and seed numbersplant-1 significantly higher in R than S phenotypes at increased rates of glyphosate and these relationships were significant. Our research suggests that glyphosate-resistant E. colona plants will be less fit than susceptible plants (from the same population) in the absence of glyphosate. But in the presence of glyphosate, the R plants may eventually dominate in the field. The use of glyphosate is widespread in field, would favour the selection towards resistant individuals.