A model of faulty and faultless disagreement for post-hoc assessments of knowledge utilization in evidence-based policymaking.

When evidence-based policymaking is so often mired in disagreement and controversy, how can we know if the process is meeting its stated goals? We develop a novel mathematical model to study disagreements about adequate knowledge utilization, like those regarding wild horse culling, shark drumlines and facemask policies during pandemics. We find that, when stakeholders disagree, it is frequently impossible to tell whether any party is at fault. We demonstrate the need for a distinctive kind of transparency in evidence-based policymaking, which we call transparency of reasoning.

Fault Detection for Vaccine Refrigeration via Convolutional Neural Networks Trained on Simulated Datasets.

In low-and middle-income countries, the cold chain that supports vaccine storage and distribution is vulnerable due to insufficient infrastructure and interoperable data. To bolster these networks, we developed a convolutional neural network-based fault detection method for vaccine refrigerators using datasets synthetically generated by thermodynamic modelling. We demonstrate that these thermodynamic models can be calibrated to real cooling systems in order to identify system-specific faults under a diverse range of operating conditions.

Unpacking the root causes of gambling in the Asian community: Contesting the myth of the Asian gambling culture.

Introduction: Problem gambling is a public health issue both in the United States and internationally and can lead to mental health and socioeconomic concerns for individuals, families, and communities. Large epidemiological studies on problem gambling have neglected to include working-class, immigrant Asian Americans, who are at higher risk for problem gambling. The lack of data on Asian American gambling may explain a subsequent lack of culturally and linguistically appropriate treatment and prevention services.