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Background: At some US Academic Health Centers (AHCs), patients with predominantly Medicaid insurance are seen in one clinic and patients with other insurance are seen in another. The extent of this practice and implications are unknown. Objective: To estimate the proportion of AHCs that have at least two primary care internal medicine clinics that differ substantially in proportion of patients with Medicaid and to compare patient demographic, staffing, and operational features. Participants: General internal medicine chiefs and clinic directors at 40 randomly selected US AHCs plus the top 10 AHCs in terms of NIH funding. Main Measure: An AHC was classified as maintaining clinics that differed substantially in the proportion of patients with Medicaid if any two differed by ≥ 40% (absolute). Other criteria were used for pre-specified secondary analyses (e.g., ≥ 30%). Key Results: Thirty-nine of 50 AHCs (78%) participated. Four of 39 (10%; 95% CI, 3 to 24%) had two clinics differing by ≥ 40% in the proportion of patients with Medicaid, eight (21%; 95% CI, 9 to 36%) had clinics differing by ≥ 30%, and 15 (38%; 95% CI, 23 to 55%) had clinics differing by ≥ 20%. Clinics with more patients with Medicaid by any of the three criteria were more likely to employ resident physicians as providers of longitudinal care (with faculty supervision) and more likely to have patients who were Black or Hispanic. Conclusions: Some US AHCs maintain separate clinics defined by the proportion of patients with Medicaid. Clinics with a higher proportion of patients insured by Medicaid are more likely to employ residents (with faculty oversight), feature residents as providers of longitudinal care, and serve patients who are Black and Hispanic. Further research is needed to understand why some AHCs have primary care clinics distinguishable by insurance mix with the goal of ensuring that racism and discrimination are not root causes. [ABSTRACT FROM AUTHOR]

A major challenge in agent-based modelling is the management of the process to generate executable simulations from the initial conceptual models. This process is complex and usually involves several roles, which may raise communication problems due to the diverse backgrounds and perspectives of participants and the use of non-explicit knowledge. This situation demands a clear separation and precise definition of the multiple aspects of the process, in order to facilitate their understanding, grasp their relationships and develop them. This paper addresses this goal with a fine-step refinement process for information based on the use of domain-specific languages. It considers analysis contexts that include a particular theoretical framework, domain, type of problem and target platform. For a given context, the process formally defines modelling languages conceptually close to the different aspects relevant to it. It also defines mappings between concepts in those languages. Researchers develop simulations by specifying models with the languages, and share and refine information by using mappings between these models. This infrastructure provides guidance throughout the process and makes the information involved explicit. A case study of continuous double auctions illustrates the approach. [ABSTRACT FROM AUTHOR]

gent-based models informed by empirical data are growing in popularity. Many models make extensive use of collected data for the development, initialisation or validation. In parallel, models are growing in size and complexity, generating large amounts of output data. On the other hand, Data Mining is used to extract hidden patterns from large collections of data using different techniques. This work proposes the intense use of Data Mining techniques for the improvement and development of agent-based models. It presents a methodological approach explaining why and when to use Data Mining, with a formal description of each stage of the corresponding process. This is illustrated with a case study, showing the application of the proposed approach step by step. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one by Natalie Fridman and Gal A. Kaminka on "First Steps Towards a Social Comparison Model of Crowds," one by Javier Arroyo and colleagues on "Re-thinking Modelling: a Call for the Use of Data Mining in Data-driven Social Simulation," and one by Yu Zhang and Jason Leezeron on "A Reinforcement and Memory-Based Model of Human-Like Behavior."

A theoretical investigation into the effect of small vibrations on the behaviour of a small particle contained in a fluid cell under microgravity is presented. Diffusion-controlled material processing such as protein crystal growth can be adversely affected by small vibrations called g-jitter, if a relative motion is induced between the particle and surrounding fluid. When a fluid cell containing a small particle such as a protein crystal is vibrated parallel to the wall nearest to the particle, the particle oscillates with a certain amplitude and a hydrodynamic force in the direction normal to the wall is induced. Theoretical models based on an inviscid fluid assumption are used to predict the particle amplitude variation and drifting motion. Due to an external vibration such as g-jitter, the oscillating particle is predicted to drift towards the wall and the particle oscillation amplitude to decrease slightly as the distance between the particle and wall is reduced. The reduction in particle ampitude also depends on the particle-to-fluid density ratio. The particle drift towards the nearest wall acclerates due to an increasing attraction force, and the drifting speed increases with both the vibration frequency and particle diameter. Even for small protein crystals with a density close to that of the fluid, the time required to drift from the center of the fluid cell to the wall is predicted to be much shorter than the growth time. [ABSTRACT FROM AUTHOR]