Constructing stability landscapes to identify alternative states in coupled social-ecological agent-based models

The resilience of a social-ecological system is measured by its ability to retain core functionality when subjected to perturbation. Resilience is contextually dependent on the state of system components, the complex interactions among these components, and the timing, location, and magnitude of perturbations. The stability landscape concept provides a useful framework for considering resilience within the specified context of a particular social-ecological system but has proven difficult to operationalize. This difficulty stems largely from the complex, multidimensional nature of the systems of interest and uncertainty in system response. Agent-based models are an effective methodology for understanding how cross-scale processes within and across social and ecological domains contribute to overall system resilience. We present the results of a stylized model of agricultural land use in a small watershed that is typical of the Midwestern United States. The spatially explicit model couples land use, biophysical models, and economic drivers with an agent-based model to explore the effects of perturbations and policy adaptations on system outcomes. By applying the coupled modeling approach within the resilience and stability landscape frameworks, we (1) estimate the sensitivity of the system to context- specific perturbations, (2) determine potential outcomes of those perturbations, (3) identify possible alternative states within state space, (4) evaluate the resilience of system states, and (5) characterize changes in system-scale resilience brought on by changes in individual land use decisions

Constructing stability landscapes to identify alternative states in coupled social-ecological agent-based models

The resilience of a social-ecological system is measured by its ability to retain core functionality when subjected to perturbation. Resilience is contextually dependent on the state of system components, the complex interactions among these components, and the timing, location, and magnitude of perturbations. The stability landscape concept provides a useful framework for considering resilience within the specified context of a particular social-ecological system but has proven difficult to operationalize. This difficulty stems largely from the complex, multidimensional nature of the systems of interest and uncertainty in system response. Agent-based models are an effective methodology for understanding how cross-scale processes within and across social and ecological domains contribute to overall system resilience. We present the results of a stylized model of agricultural land use in a small watershed that is typical of the Midwestern United States. The spatially explicit model couples land use, biophysical models, and economic drivers with an agent-based model to explore the effects of perturbations and policy adaptations on system outcomes. By applying the coupled modeling approach within the resilience and stability landscape frameworks, we (1) estimate the sensitivity of the system to context- specific perturbations, (2) determine potential outcomes of those perturbations, (3) identify possible alternative states within state space, (4) evaluate the resilience of system states, and (5) characterize changes in system-scale resilience brought on by changes in individual land use decisions

Genetic Algorithms and the Satisfiability of Large-Scale Boolean Expressions.

The two new genetic methods overpopulation and bitwise expected value are introduced. In overpopulation a temporary population of size Mn (M $\u3e$ 1) is created using genetic operators and the n children with the highest estimated fitness values are selected as the next generation. The rest are discarded. Bitwise expected value (bev) is the fitness estimation function used. Overpopulation and bitwise expected value are applied to the NP-complete problem 3SAT (a special form of Satisfiability in which the boolean expression consists of the conjunction of an arbitrary number of clauses where each clause consists of the disjunction of 3 boolean variables) with excellent empirical results when compared to the performance of the standard genetic algorithm. Overpopulation increases the cost of producing each generation due to the overhead required to maintain the larger temporary population but results in many fewer generations to solution. Using bitwise expected value as a fitness estimator causes the algorithm to take slightly more generations to solution but is much faster to calculate than the fitness function, leading to a decrease in wall-clock time to solution. Theoretical justification for the success of overpopulation is seen as a result of the generalization of the schema growth equation. Bitwise expected value is viewed as an analogy to the Building Block Hypothesis. Empirical evidence of high correlation between bev and the fitness function is presented. We also introduce the target problem concept, in which a difficult problem is transformed into a well-known problem for which a good genetic method of solution is known. As an example of the target problem concept a transformation from the Traveling Salesman Problem to Satisfiability is demonstrated. Overpopulation and bitwise expected value are applied to the resulting boolean expression, with good results. An interesting convergence property is observed

Considerations for health care institutions training large language models on electronic health records

Large language models (LLMs) like ChatGPT have excited scientists across fields; in medicine, one source of excitement is the potential applications of LLMs trained on electronic health record (EHR) data. But there are tough questions we must first answer if health care institutions are interested in having LLMs trained on their own data; should they train an LLM from scratch or fine-tune it from an open-source model? For healthcare institutions with a predefined budget, what are the biggest LLMs they can afford? In this study, we take steps towards answering these questions with an analysis on dataset sizes, model sizes, and costs for LLM training using EHR data. This analysis provides a framework for thinking about these questions in terms of data scale, compute scale, and training budgets

Predicting Elective Orthopaedic Sports Medicine Surgical Cancellations Based on Patient Demographics.

Purpose:To evaluate whether patient demographics are associated with cancellation of elective orthopaedic sports medicine surgical procedures. Methods:We retrospectively reviewed the electronic medical records of 761 patients who were scheduled to undergo an elective sports medicine orthopaedic operation from January 1, 2015, to December 31, 2017. The patients were divided into 2 groups: those who underwent the scheduled procedure (group A) and those in whom the operation was canceled for any reason prior to the surgical date and not rescheduled (group B). Univariate analysis assessed patient factors consisting of age, sex, race, language, marital status, occupation status, type of insurance (Medicaid or Medicare vs private), smoking history, employment status, and history of surgery to determine which demographic factors led to an increased risk of elective case cancellation. Results:Patients who canceled were significantly older (46.5 years vs 41.5 years, t = 2.432, P = .015) than those who do not. In addition, current smokers (22.5% vs 10.9%, χ2 = 10.85, P = .001), patients with Medicare or Medicaid versus private insurance (16.7% vs 10.0%, χ2 = 5.35, P = .021), non-English-speaking patients (29.5% vs 11.6%, χ2 = 11.43, P = .001), and patients without a history of surgery requiring anesthesia (18.8% vs 9.6%, χ2 = 9.96, P = .002) were all more likely to cancel. When all studied variables were examined in a logistic regression analysis, of the above demographic variables, only insurance status was no longer significant, given its correlation with age and language. Conclusions:Increased age (≥46.5 years), non-English speaking, smoking, lack of a history of surgery requiring anesthesia, and Medicaid or Medicare insurance were found to contribute to an increased risk of elective orthopaedic surgery cancellation. Level of Evidence:Level III, case-control study

Secondary dentin formation mechanism: The effect of attrition

Human dentin consists of a primary layer produced during tooth formation in early child-hood and a second layer which first forms upon tooth eruption and continues throughout life, termed secondary dentin (SD). The effect of attrition on SD formation was considered to be confined to the area subjacent to attrition facets. However, due to a lack of three‐dimensional methodologies to demonstrate the structure of the SD, this association could not be determined. Therefore, in the current study, we aimed to explore the thickening pattern of the SD in relation to the amount of occlusal and interproximal attrition. A total of 30 premolars (50–60 years of age) with varying attrition rates were evaluated using micro‐computerized tomography. The results revealed thickening of the SD below the cementoenamel junction (CEJ), mostly in the mesial and distal aspects of the root (p < 0.05). The pattern of thickening under the tooth cervix, rather than in proximity to attrition facets, was consistent regardless of the attrition level. The amount of SD thickening mildly corre-lated with occlusal attrition (r = 0.577, p < 0.05) and not with interproximal attrition. The thickening of the SD below the CEJ coincided with previous finite element models, suggesting that this area is mostly subjected to stress due to occlusal loadings. Therefore, we suggest that the SD formation might serve as a compensatory mechanism aimed to strengthen tooth structure against deflection caused by mechanical loading. Our study suggests that occlusal forces may play a significant role in SD formation