Search

This article details a correction to: Wolpert, D.H., Price, M.H., Crabtree, S.A., Kohler, T.A., Jost, J., Evans, J., Stadler, P.F., Shimao, H. and Laubichler, M.D. (2024) ‘The Past as a Stochastic Process’, Journal of Computer Applications in Archaeology, 7(1), p. 134–152. Available at: https://doi.org/10.5334/jcaa.113.

Historical processes manifest remarkable diversity. Nevertheless, scholars have long attempted, with some success, to identify patterns and categorize historical actors and influences. A stochastic process framework provides a structured approach for the analysis of large historical datasets that allows for detection of sometimes surprising patterns, identification of relevant causal actors both endogenous and exogenous to the process, and comparison between different historical cases. The combination of data, analytical tools and the organizing theoretical framework of stochastic processes complements traditional narrative approaches in history and archaeology.

Stochastic thermodynamics is formulated under the assumption of perfect knowledge of all thermodynamic parameters. However, in any real-world situation, there is nonzero uncertainty about the precise value of temperatures, chemical potentials, energy spectrum, etc. Here we investigate how this uncertainty modifies the theorems of stochastic thermodynamics. We consider two scenarios: in the effective scenario, we fix the (unknown, randomly generated) parameters of an experimental apparatus and then repeatedly observe (stochastic) trajectories of the system for that fixed apparatus. In contrast, in the (consistently with the effective scenario) phenomenological scenario, the (unknown) apparatus is re-generated for each trajectory. We derive expressions for thermodynamic quantities in both scenarios. For the effective scenario, we also discuss the physical interpretation of entropy production (EP) and derive the mismatch cost. To illustrate this scenario, we also provide a numerical analysis of the thermodynamics of a quantum dot implementing bit erasure with uncertain temperature. We also analyze the protocol for changing the distribution over states in a way that maximizes work extraction, again in the effective scenario. Next, we investigate the effective thermodynamic value of information, focusing on the case where there is a delay between the initialization of the system and the start of the protocol. Finally, we derive the detailed and integral fluctuation theorems (FTs) for the phenomenological EP. In particular, we show how the phenomenological FTs account for the fact that the longer a trajectory runs, the more information it provides concerning the precise experimental apparatus, and therefore the less EP it generates. Our results provide a very preliminary investigation of the myriad issues that arise when one tries to expand stochastic thermodynamics to account for uncertainty in the parameters governing a physical process.

Optimizing postprandial glucose control in persons with type 1 diabetes (T1D) is challenging. We hypothesized that in free-living individuals, meal composition (high and low glycemic index [HGI and LGI], high and low fat [HF and LF]) may impact insulin requirements. Adults ( N = 25) with T1D using open-loop insulin and continuous glucose monitoring were provided a meal-tagging app and prepackaged meals with defined macronutrient content. Data from 463 meals were analyzed. LGI meals required significantly more insulin than HGI meals ( P = 0.01). Furthermore, the mean (±standard deviation) carbohydrate-to-insulin ratio (CIR) was significantly different overall among the LGI-LF (5.5 ± 3.4), LGI-HF (4.5 ± 3.8), HGI-LF (7.6 ± 5.1), and HGI-HF (8.7 ± 5.8) meals ( P = 0.001). The risk of nocturnal hypoglycemia is associated with daytime hypoglycemia and amount of insulin administered prior to the evening and exercise. This exploratory study designed to examine the impact of different meal types on insulin dosing requirements in free-living adults with T1D emphasizes the need for individualized adjustment of the CIR depending on meal composition.

Background: Recent advancements in diabetes technology have significantly improved Type 1 diabetes (T1D) management, but disparities persist, particularly in the adoption of automated insulin delivery (AID) systems within minoritized communities. We aimed to improve patient access to AID system training and overcome clinical inertia to referral., Methods: We report on a transformative program implemented at Boston Medical Center, the largest safety-net hospital in New England, aimed at reducing disparities in AID system utilization. We employed a multidisciplinary team and quality improvement principles to identify barriers and develop solutions. Strategies included increasing access to diabetes educators, creating a referral system, and developing telemedicine education classes. We also made efforts to raise clinician awareness and confidence in recommending AID therapy., Results: At baseline, 13.5% of our clinic T1D population was using an insulin pump. The population referred included 97 people with T1D (49% female, mean A1c 8.7%, 68% public insurance beneficiaries, 25% Hispanic and 25% non-Hispanic Black). Results from the first year showed a 166% increase in AID system use rates, with 64% of referred patients starting on AID. Notably, 78% of patients with A1c >8.5% adopted AID systems, addressing a gap in representation observed in clinical efficacy trials. The initiative successfully narrowed disparities in AID use among minoritized populations., Conclusions: The program's success among minoritized patients underscores the significance of tailored, collaborative, team-based care and targeted educational initiatives. Our experience provides a foundation for future efforts to ensure equitable access to diabetes technologies, emphasizing the potential of local quality improvement interventions., Competing Interests: Disclosure The authors have no multiplicity of interest to disclose., (Copyright © 2024 AACE. Published by Elsevier Inc. All rights reserved.)