ScholarWorks@UMassAmherst

Pesticides pose significant threats to pollinators, and honey bees are frequently exposed through foraging and beekeeping practices. We assessed honey bee pesticide exposure by analyzing 92 pesticide residues in honey from 30 hobbyist apiaries across Massachusetts, along with store-bought honey and commercial wax foundation. For all samples, we calculated the risk of multiresidue toxicity to honey bees and assessed the role of landscape composition in predicting pesticides in local honey. Both honey and wax contained multiple pesticides, particularly neonicotinoids and piperonyl butoxide. Store-bought honey accumulated at least two times more residues than local but did not differ significantly in toxicity. Overall, honey toxicity levels remained below thresholds of concern for bees and human consumption. Although our study had low agricultural land (~6 %), croplands were positively correlated with pesticides in honey, while wetlands (~ 15 %) were negatively correlated. Additionally, our study suggests that commercial wax exacerbates pesticide exposure.

An “embedded” research approach aims to inform programme improvement and has the potential to facilitate more interactive horizontal engagement and knowledge co-production. Concurrently, this approach demands negotiating relationships with institutions and individuals. The purpose of this chapter is to identify the values, opportunities and challenges of conducting embedded research with allied health professionals, drawing from our experience assessing the Health On the Go (Salud Al Paso) Program in Quito, Ecuador. The analysis shows that an embedded research approach requires supporting personnel to become partners, and as staff members become co-creators of knowledge, researchers have less vertical control over the study. Conducting research with health personnel may be stimulating for them when their opinions are valued and may help them reflect on their own practices. Furthermore, it may also demonstrate the importance of jointly assessing a health promotion initiative. Qualitative techniques, such as focus groups, may be useful for participants to contrast their perceptions and opinions with those of their colleagues. It is important to follow a three-step process: (1) developing a relationship of trust by being transparent, (2) being flexible when engaging and communicating with decision-makers and (3) co-producing knowledge while remaining continuously reflexive on potential biases.

Integrating the physically realistic Lennard-Jones (LJ) potential into the Direct Simulation Monte Carlo (DSMC) framework has historically been hindered by the computational cost of evaluating complex scattering dynamics. This study presents a high-fidelity, machine-learning-accelerated framework that bridges the gap between rigorous molecular physics and large-scale kinetic simulations. This new approach is implemented in the standard Bird's suite of DSMC algorithms (DSMC1, DSMC1S, and DS2V), offering a high-precision platform for rarefied-gas studies. To achieve this, two problems are addressed: incorporating Lennard-Jones-specific properties into the inherently total cross-section concept of the method and replacing the computationally intensive particle-scattering process with a surrogate machine-learning model. As a result, a universal Variable Effective Diameter (VED) model is developed through local viscosity matching, ensuring accurate capture of attractive-repulsive interactions across a wide range of temperatures, a critical advance over traditional models limited to narrow thermal bands. The surrogate model, crucial for the framework's efficiency, employs a Deep Operator Network (DeepONet) as a high-performance substitute for the computationally intensive LJ scattering integral. The framework reveals critical physical insights often missed by standard models. The framework is validated against three canonical problems: shock waves in helium and argon, supersonic Couette flow at low temperature, and hypersonic cylinder flow at two Mach numbers. In the argon shock-wave problem, we showed that although the density profile of the Variable Hard Sphere (VHS) model does not match the experimental data, its velocity distribution function follows the LJ prediction. In supersonic Couette flow with cryogenic walls (40 K), the LJ model predicts a smaller shear stress than the VHS model, highlighting the dominant role of long-range attractive forces in low-temperature shear layers. In parallel, for hypersonic flow over a cylinder at Mach=10, the LJ and VHS results agree well, even in the wake region where temperatures range above 800 K; in this regime, high-energy repulsive collisions dominate, rendering the attractive potential well negligible. As we reduced the cylinder and incoming flow temperatures to the cryogenic regime (Mach 5, Tw=40K), profound deviations became apparent: the LJ model predicted a larger, more elongated wake vortex than the VHS model, a direct macroscopic manifestation of long-range attractive forces that reduce the local effective viscosity. By leveraging Scientific Machine Learning (SciML)-based operator learning, the DeepONet surrogate preserves the intricate balance of molecular forces while accelerating the collision subroutine by 40% and reducing total simulation wall-clock time by 36%. This work establishes a scalable, physically grounded, and computationally efficient pathway for high-fidelity kinetic modeling in the era of scientific machine learning, while providing fluid physical insights beyond standard VHS predictions.