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Objective: Cryoballoon ablation for pulmonary vein isolation is a time-efficient procedure that can alleviate stress on electrophysiology lab resources. This analysis modeled the impact of cryoballoon ablation on electrophysiology lab operation using data from Latin America. Methods: Data from centers in Argentina, Mexico, Colombia, and Chile of the were used as inputs for an electrophysiology lab efficiency simulation model. The model used the assumption that either two (today's electrophysiology lab operations) or three (including electrophysiology lab operational changes) cryoballoon ablation procedures could be performed per day. The endpoints were the percentage of days that resulted in 1) overtime and 2) time left for an extra non-ablation electrophysiology procedure. Results: Data from a total of 232 procedures from six Latin American centers were included in the analysis. The average electrophysiology lab occupancy time for all procedures in Latin America was 132 ± 62 minutes. In the Current Scenario (two procedures per day), 7.4% of simulated days resulted in overtime, and 81.4% had enough time for an extra electrophysiology procedure. In the Enhanced Productivity Scenario (three procedures per day), 16.4% of days used overtime, while 67.4% allowed time for an extra non-ablation electrophysiology procedure. Conclusions: Using real-world, Latin American-specific data, we found that with operational changes, three ablation procedures could feasibly be performed daily, leaving time for an extra electrophysiology procedure on more than half of days. Thus, use of cryoballoon ablation is an effective tool to enhance electrophysiology lab efficiency in resource-constrained regions such as Latin America.

Objetivos: La ablación con criobalón para el aislamiento de venas pulmonares es un procedimiento que ahorra tiempo y puede ahorrar recursos del laboratorio de electrofisiología. Este análisis modeló el impacto de la ablación con criobalón en el funcionamiento del laboratorio de electrofisiología utilizando datos de América Latina. Métodos: Los datos de los centros de Argentina, México, Colombia y Chile del se utilizaron como datos de entrada para un modelo de simulación de la eficiencia del laboratorio de electrofisiología. El modelo partió del supuesto de que se podían realizar dos (operaciones actuales del laboratorio de electrofisiología) o tres (incluidos los cambios operativos del laboratorio de electrofisiología) procedimientos de ablación con criobalón por día. Los criterios de valoración eran el porcentaje de días en los que se producían 1) horas extraordinarias y 2) tiempo restante para un procedimiento electrofisiológico adicional no relacionado con la ablación. Resultados: Se incluyeron en el análisis los datos un total de 232 procedimientos de seis centros latinoamericanos. El tiempo medio de ocupación del laboratorio de electrofisiología para todos los procedimientos en Latinoamérica fue de 132 ± 62 minutos. En el escenario actual (dos procedimientos por día), el 7,4% de los días simulados resultaron en horas extras, y el 81,4% tuvo tiempo suficiente para un procedimiento de electrofisiología adicional. En el escenario de productividad mejorada (tres procedimientos por día), el 16,4% de los días utilizó horas extraordinarias, mientras que el 67,4% dispuso de tiempo suficiente para un procedimiento electrofisiológico extra sin ablación. Conclusiones: Utilizando datos del mundo real específicos de América Latina, descubrimos que, aplicando cambios operativos, es factible realizar tres procedimientos de ablación al día, lo que deja tiempo para un procedimiento de electrofisiología adicional en más de la mitad de los días. Por lo tanto, el uso de la ablación con criobalón es una herramienta eficaz para mejorar la eficiencia de los laboratorios de electrofisiología en regiones con recursos limitados como América Latina.

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Understanding the impacts of changing climate and disturbance regimes on forest ecosystems is greatly aided by the use of process-based models. Such models simulate processes based on first principles of ecology, which requires parameterization. Parameterization is an important step in model development and application, defining the characteristics of trees and their responses to the environment, i.e., their traits. For species-specific models, parameterization is usually done at the level of individual species. Parameterization is indispensable for accurately modeling demographic processes, including growth, mortality, and regeneration of trees, along with their intra- and inter-specific interactions. As it is time-demanding to compile the parameters required to simulate forest ecosystems in complex models, simulations are often restricted to the most common tree species, genera, or plant-functional types. Yet, as tree species composition might change in the future, it is important to account for a broad range of species and their individual responses to drivers of change explicitly in simulations. Thus, species-specific parameterization is a critical task for making accurate projections about future forest trajectories, yet species parameters often remain poorly documented in simulation studies. We compiled and harmonized all existing tree species parameters available for the individual-based forest landscape and disturbance model (iLand). Since its first publication in 2012, iLand has been applied in 50 peer-reviewed publications across three continents throughout the Northern Hemisphere (i.e., Europe, North America, and Asia). The model operates at individual-tree level and simulates ecosystem processes at multiple spatial scales, making it a capable process-based model for studying forest change. However, the extensive number of processes and their interactions as well as the wide range of spatio-temporal scales considered in iLand require intensive parameterization, with tree species characterized by 66 unique parameters in the model. The database presented here includes parameters for 150 temperate and boreal tree species and provenances (i.e., regional variations). Excluding missing values, the database includes a total of 9,249 individual parameter entries. In addition, we provide parameters for the individual susceptibility of tree species to wind disturbance (five parameters) for a subset of 104 tree species and provenances (498 parameter entries). To guide further model parameterization efforts, we provide an estimate of uncertainty for each species based on how thoroughly simulations with the respective parameters were evaluated against independent data. Our dataset aids the future parameterization and application of iLand, and sets a new standard in documenting parameters used in process-based forest simulations. This dataset will support model application in previously unstudied areas and can facilitate the investigation of new tree species being introduced to well-studied systems (e.g., simulating assisted migration in the context of rapid climate change). Given that many process-based models rely on similar underlying processes our harmonized parameter set will be of relevance beyond the iLand community. Our work could catalyze further research into improving the parameterization of process-based forest models, increasing the robustness of projections of climate change impacts and adaptation strategies.

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The quartz tuning fork (QTF) is a promising instrument for biosensor applications due to its advanced properties such as high sensitivity to physical quantities, cost-effectiveness, frequency stability, and high-quality factor. Nevertheless, the fork's small size and difficulty in modifying the prongs' surfaces limit its wide use in experimental research. Our study presents the development of a QTF immunosensor composed of three active layers: biocompatible natural melanin nanoparticles (MNPs), glutaraldehyde (GLU), and anti-IgG layers, for the detection of immunoglobulin G (IgG). Frequency shifts of QTFs after MNP functionalization, GLU activation, and anti-IgG immobilization were measured with an Asensis QTF F-master device. Using QTF immunosensors that had been modified under optimum conditions, the performance of QTF immunosensors for IgG detection was evaluated. Accordingly, a finite element method (FEM)-based model was produced using the COMSOL Multiphysics software program (COMSOL License No. 2102058) to simulate the effect of deposited layers on the QTF resonance frequency. The experimental results, which demonstrated shifts in frequency with each layer during QTF surface functionalization, corroborated the simulation model predictions. A modelling error of 0.05% was observed for the MNP-functionalized QTF biosensor compared to experimental findings. This study validated a simulation model that demonstrates the advantages of a simulation-based approach to optimize QTF biosensors, thereby reducing the need for extensive laboratory work.

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BACKGROUND: Evaluating sugar-sweetened beverage (SSB) taxation often relies on simulation models. We assess how assumptions about the response to SSB taxation affect the projected body weight change and subsequent health and economic impacts related to type 2 diabetes mellitus (T2DM) using Germany as an example. METHODS: In the main analysis, we estimated changes in energy intake by age and sex under a 20% value-added tax on SSBs in Germany using marginal price elasticities (PE) and applied an energy equilibrium model to predict body weight changes. We then quantified the impact of several assumption modifications: SSB own-PE adjusted for consumption (M1)/based on alternative meta-analysis (M2); SSB consumption adjusted for underreporting (M3); substitution via marginal (M4a) or adjusted (M4b) cross-PE/as % of calorie change (M4c). We also assessed scenarios with alternative tax rates of 10% (S1) or 30% (S2) and including fruit juice (S3). We calculated overweight and obesity rates per modification and scenario. We simulated the impact on T2DM, associated healthcare costs, and disability-adjusted life years (DALYs) over the lifetime of the 2011 German adult population with a Markov model. Data included official demographics, national surveys, and meta-analyses. RESULTS: A 20% value-added tax in Germany could reduce the number of men and women with obesity by 210,800 [138,800; 294,100] and 80,800 [45,100; 123,300], respectively. Over the population's lifetime, this would lead to modest T2DM-related health and economic impacts (76,700 DALYs [42,500; 120,600] averted; €2.37 billion [1.33; 3.71] costs saved). Policy impacts varied highly across modifications (all in DALYs averted): (M1) 94,800 [51,500; 150,700]; (M2) 164,200 [99,500; 243,500]; (M3) 52,600 [22,500; 91,100]; (M4a) -18,100 [-111,500; 68,300]; (M4b) 25,800 [-31,400; 81,500]; (M4c) 46,700 [25,300; 77,200]. The variability in policy impact related to modifications was similar to the variability between alternative policy scenarios (all in DALYs averted): (S1) 26,400 [9,300; 47,600]; (S2) 126,200 [73,600; 194,500]; (S3) 342,200 [234,200; 430,400]. CONCLUSIONS: Predicted body weight reductions under SSB taxation are sensitive to assumptions by researchers often needed due to data limitations. Because this variability propagates to estimates of health and economic impacts, the resulting structural uncertainty should be considered when using results in decision-making.

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OBJECTIVES: The purpose of this study is to evaluate the cost-effectiveness of increasing access to colorectal cancer (CRC) diagnosis, considering resource limitations in Thailand. METHODS: We analyzed the cost-effectiveness of increasing access to fecal immunochemical test screening (strategy I), symptom evaluation (strategy II), and their combination through healthcare and societal perspectives using Colo-Sim, a simulation model of CRC care. We extended our analysis by adding a risk-stratification score (RS) to the strategies. We analyzed all strategies under the currently limited annual colonoscopy capacity and sufficient capacity. We estimated quality-adjusted life-years (QALYs) and costs over 2023 to 2047 and performed sensitivity analyses. RESULTS: Annual costs for CRC care will increase over 25 years in Thailand, resulting in a cumulative cost of 323B Thai baht (THB). Each strategy results in higher QALYs gained and additional costs. With the current colonoscopy capacity and willingness-to-pay threshold of 160 000 THB, strategy I with and without RS is not cost-effective. Strategy II + RS is the most cost-effective, resulting in 0.68 million QALYs gained with additional costs of 66B THB. Under sufficient colonoscopy capacity, all strategies are deemed cost-effective, with the combined approach (strategy I + II + RS) being the most favorable, achieving the highest QALYs (1.55 million) at an additional cost of 131 billion THB. This strategy also maintains the highest probability of being cost-effective at any willingness-to-pay threshold above 96 000 THB. CONCLUSIONS: In Thailand, fecal immunochemical test screening, symptom evaluation, and RS use can achieve the highest QALYs; however, boosting colonoscopy capacity is essential for cost-effectiveness.

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Understanding the factors influencing species range limits is increasingly crucial in anticipating migrations due to human-caused climate change. In the boreal biome, ongoing climate change and the associated increases in the rate, size, and severity of disturbances may alter the distributions of boreal tree species. Notably, Interior Alaska lacks native pine, a biogeographical anomaly that carries implications for ecosystem structure and function. The current range of lodgepole pine (Pinus contorta var. latifolia) in the adjacent Yukon Territory may expand into Interior Alaska, particularly with human assistance. Evaluating the potential for pine expansion in Alaska requires testing constraints on range limits such as dispersal limitations, environmental tolerance limits, and positive or negative biotic interactions. In this study, we used field experiments with pine seeds and transplanted seedlings, complemented by model simulations, to assess the abiotic and biotic factors influencing lodgepole pine seedling establishment and growth after fire in Interior Alaska. We found that pine could successfully recruit, survive, grow, and reproduce across our broadly distributed network of experimental sites. Our results show that both mammalian herbivory and competition from native tree species are unlikely to constrain pine growth and that environmental conditions commonly found in Interior Alaska fall well within the tolerance limits for pine. If dispersal constraints are released, lodgepole pine could have a geographically expansive range in Alaska, and once established, its growth is sufficient to support pine-dominated stands. Given the impacts of lodgepole pine on ecosystem processes such as increases in timber production, carbon sequestration, landscape flammability, and reduced forage quality, natural or human-assisted migration of this species is likely to substantially alter responses of Alaskan forest ecosystems to climate change.

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In order to address the issue of suppressing thermal runaway (TR) in power battery, a thermal generation model for power batteries was established and then modified based on experimental data. On the basis of simulation calculations, a scheme was designed to suppress thermal runaway of the battery module and battery pack, and samples were produced for testing. The results of the test and simulation calculations were very consistent, confirming the accuracy of the simulation calculation model. The results of thermal runaway test also demonstrate that the measures designed to suppress thermal runaway are effective and meet the design requirements.

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Coal is a non-renewable fossil energy source on which humanity relies heavily, and producing one ton of raw coal requires the discharge of 2-7 tons of mine water from the ground. The huge drainage task increases the cost of coal mining in coal mines significantly, so saving the drainage cost while guaranteeing the safe production of coal mines is a problem that needs to be solved urgently. Most of the fuzzy controllers used in the traditional dynamic planning methods applied to mine drainage are two-dimensional fuzzy controllers with limited control effect, so the traditional two-dimensional fuzzy controllers are improved by introducing the rate of change of gushing water to form a three-dimensional fuzzy controller with three-dimensional control of instantaneous section-water level-rate of change of gushing water, and at the same time, the optimized dynamic planning method is designed by combining the Avoiding Peak Filling Valley strategy and the optimal dy-namic planning method is used in conjunction with the un-optimized dynamic planning method. The optimized dynamic planning method is applied to the same coal mine water silo gushing water experiments; experimental comparison found that the pumping station system before the optimi-zation of the electricity consumed is 52,586 yuan/day, while after the optimization of the electricity consumed is reduced to 41,692 yuan/day, the cost per day consumed compared with the previous reduction of 20.69%, a year can be saved 3,969,730 yuan. Therefore, the mine water bin drainage method based on fuzzy control and Avoiding Peak Filling Valley strategy proposed in this paper can be used as an improvement method of the existing mine drainage method, which can further ex-pand the economic benefits of coal mines and realize safe production while realizing cost savings.

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OBJECTIVE: Models simulating the potential impacts of Human Papillomavirus (HPV) vaccine have been used globally to guide vaccination policies and programs. We sought to understand how and why marginalized populations have been incorporated into HPV vaccine simulation models. METHODS: We conducted a systematic search of PubMed, CINAHL, Scopus, and Embase to identify studies using simulation models of HPV vaccination incorporating one or more marginalized population through stratification or subgroup analysis. We extracted data on study characteristics and described these overall and by included marginalized groups. RESULTS: We identified 36 studies that met inclusion criteria, which modeled vaccination in 21 countries. Models included men who have sex with men (MSM; k = 16), stratification by HIV status (k = 9), race/ethnicity (k = 6), poverty (k = 5), rurality (k = 4), and female sex workers (k = 1). When evaluating for a marginalized group (k = 10), HPV vaccination was generally found to be cost-effective, including for MSM, individuals living with HIV, and rural populations. In studies evaluating equity in cancer prevention (k = 9), HPV vaccination generally advanced equity, but this was sensitive to differences in HPV vaccine uptake and use of absolute or relative measures of inequities. Only one study assessed the impact of an intervention promoting HPV vaccine uptake. DISCUSSION: Incorporating marginalized populations into decision models can provide valuable insights to guide decision making and improve equity in cancer prevention. More research is needed to understand the equity impact of HPV vaccination on cancer outcomes among marginalized groups. Research should emphasize implementation - including identifying and evaluating specific interventions to increase HPV vaccine uptake.

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BACKGROUND: Treatment variation from observational data has been used to estimate patient-specific treatment effects. Causal Forest Algorithms (CFAs) developed for this task have unknown properties when treatment effect heterogeneity from unmeasured patient factors influences treatment choice - essential heterogeneity. METHODS: We simulated eleven populations with identical treatment effect distributions based on patient factors. The populations varied in the extent that treatment effect heterogeneity influenced treatment choice. We used the generalized random forest application (CFA-GRF) to estimate patient-specific treatment effects for each population. Average differences between true and estimated effects for patient subsets were evaluated. RESULTS: CFA-GRF performed well across the population when treatment effect heterogeneity did not influence treatment choice. Under essential heterogeneity, however, CFA-GRF yielded treatment effect estimates that reflected true treatment effects only for treated patients and were on average greater than true treatment effects for untreated patients. CONCLUSIONS: Patient-specific estimates produced by CFAs are sensitive to why patients in real-world practice make different treatment choices. Researchers using CFAs should develop conceptual frameworks of treatment choice prior to estimation to guide estimate interpretation ex post.

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AbstractModels of range expansion have independently explored fitness consequences of life history trait evolution and increased rates of genetic drift-or "allele surfing"-during spatial spread, but no previous model has examined the interactions between these two processes. Here, using spatially explicit simulations, we explore an ecologically complex range expansion scenario that combines density-dependent selection with allele surfing to asses the genetic and fitness consequences of density-dependent selection on the evolution of life history traits. We demonstrate that density-dependent selection on the range edge acts differently depending on the life history trait and can either diminish or enhance allele surfing. Specifically, we show that selection at the range edge is always weaker at sites affecting competitive ability (K-selected traits) than at sites affecting birth rate (r-selected traits). We then link differences in the frequency of deleterious mutations to differences in the efficacy of selection and rate of mutation accumulation across distinct life history traits. Finally, we demonstrate that the observed fitness consequences of allele surfing depend on the population density in which expansion load is measured. Our work highlights the complex relationship between ecology and expressed genetic load, which will be important to consider when interpreting both experimental and field studies of range expansion.

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FLASH radiotherapy (FLASH-RT) has great potential to improve patient outcomes. It delivers radiation doses at an ultra-high dose rate (UHDR: ≥ 40 Gy/s) in a single instant or a few pulses. Much higher irradiation doses can be administered to tumors with FLASH-RT than with conventional dose rate (0.01-0.40 Gy/s) radiotherapy. UHDR irradiation can suppress toxicity in normal tissues while sustaining antitumor efficiency, which is referred to as the FLASH effect. However, the mechanisms underlying the effects of the FLASH remain unclear. To clarify these mechanisms, the development of simulation models that can contribute to treatment planning for FLASH-RT is still underway. Previous studies indicated that transient oxygen depletion or augmented reactions between secondary reactive species produced by irradiation may be involved in this process. To discuss the possible mechanisms of the FLASH effect and its clinical potential, we summarized the physicochemical, chemical, and biological perspectives as well as the development of simulation modeling for FLASH-RT.

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The benefits of gastric cancer screening are related to age and comorbidity status, but reliable estimates are lacking in China. This study aimed to estimate the benefits and affordability of the gastric cancer screening strategy by level of comorbidity to inform decisions to screening age. We assessed six current gastric cancer screening strategies in China using a microsimulation model with different starting and stopping ages and comorbidity profiles, for a total of 378 strategies. 1,000,000 individuals were simulated in the model and followed the alternative strategies. Primary outcomes included gastric cancer incidence, the number of endoscopy and complications, life-years, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. Future costs and QALYs are discounted by 5% per year. Sensitivity analyses were used to evaluate model uncertainty. Strategies with longer screening durations were associated with higher benefits of life-year gained and gastric cancer deaths averted, but were also accompanied by a large number of endoscopy screening, and complication events. Using the threshold of US$18,575 per QALY gained, at the no, moderate, and severe comorbidity level, the leading cost-effectiveness strategies were the new gastric cancer screening scoring system strategy (NGCS) screening from age 40 years to 60 years (40-60), 40-55-NGCS, and 40-55-NGCS strategy, respectively. The results are robust in sensitivity analyses. Our study illustrates the importance of considering comorbidity conditions and age when determining the starting and stopping screening age for gastric cancer and informs the discussion on personalizing decisions. The trade-off between benefits and harms can also be referenced when necessary.

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Barriers to adequate healthcare in rural areas remain a grand challenge for local healthcare systems. In addition to patients' travel burdens, lack of health insurance, and lower health literacy, rural healthcare systems also experience significant resource shortages, as well as issues with recruitment and retention of healthcare providers, particularly specialists. These factors combined result in complex change management-focused challenges for rural healthcare systems. Change management initiatives are often resource intensive, and in rural health organizations already strapped for resources, it may be particularly risky to embark on change initiatives. One way to address these change management concerns is by leveraging socio-technical simulation models to estimate techno-economic feasibility (e.g., is it technologically feasible, and is it economical?) as well as socio-utility feasibility (e.g., how will the changes be utilized?). We present a framework for how healthcare systems can integrate modeling and simulation techniques from systems engineering into a change management process. Modeling and simulation are particularly useful for investigating the amount of uncertainty about potential outcomes, guiding decision-making that considers different scenarios, and validating theories to determine if they accurately reflect real-life processes. The results of these simulations can be integrated into critical change management recommendations related to developing readiness for change and addressing resistance to change. As part of our integration, we present a case study showcasing how simulation modeling has been used to determine feasibility and potential resistance to change considerations for implementing a mobile radiation oncology unit. Recommendations and implications are discussed.

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Objective To study the applicability and optimization of computer simulation methods during the allocation of health care providers for medical evacuation on board medical trains.Methods Using Anylogic as a simulation modeling tool,the process of mass evacuation of the injured by means of medical trains was simulated.The simulated process of rescue involved the generation,categorization,treatment and surgery of the injured individuals.The allocation of health care resources was assessed based on the different rates at which the injured arrived.Results In the carriage for mild to moderate patients,24 doctors and 36 nurses could meet the need of treatment.In the carriage for critically ill ones,4 doctors and 6 nurses could meet the need when the rate at which the injured arrived was 100-200 people/an hour.When the injured arrived at the rate of 300-476 people/an hour,4 doctors and 8 nurses were needed.Conclusion Computer simulation can be feasibly used to study the allocation of human resources for health care,which can facilitate decision-making about mass evacuation of injured personnel by means of medical trains.

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OBJECTIVES: Thyroid cancer incidence increased over 200% from 1992 to 2018, whereas mortality rates had not increased proportionately. The increased incidence has been attributed primarily to the detection of subclinical disease, raising important questions related to thyroid cancer control. We developed the Papillary Thyroid Carcinoma Microsimulation model (PATCAM) to answer them, including the impact of overdiagnosis on thyroid cancer incidence. METHODS: PATCAM simulates individuals from age 15 until death in birth cohorts starting from 1975 using 4 inter-related components, including natural history, detection, post-diagnosis, and other-cause mortality. PATCAM was built using high-quality data and calibrated against observed age-, sex-, and stage-specific incidence in the United States as reported by the Surveillance, Epidemiology, and End Results database. PATCAM was validated against US thyroid cancer mortality and 3 active surveillance studies, including the largest and longest running thyroid cancer active surveillance cohort in the world (from Japan) and 2 from the United States. RESULTS: PATCAM successfully replicated age- and stage-specific papillary thyroid cancers (PTC) incidence and mean tumor size at diagnosis and PTC mortality in the United States between 1975 and 2015. PATCAM accurately predicted the proportion of tumors that grew more than 3 mm and 5 mm in 5 years and 10 years, aligning with the 95% confidence intervals of the reported rates from active surveillance studies in most cases. CONCLUSIONS: PATCAM successfully reproduced observed US thyroid cancer incidence and mortality over time and was externally validated. PATCAM can be used to identify factors that influence the detection of subclinical PTCs.

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Distribution and administration strategy are critical to successful population immunization efforts. Agent-based modeling (ABM) can reflect the complexity of real-world populations and can experimentally evaluate vaccine strategy and policy. However, ABMs historically have been limited in their time-to-development, long runtime, and difficulty calibrating. Our team had several technical advances in the development of our GradABMs: a novel class of scalable, fast and differentiable simulations. GradABMs can simulate million-size populations in a few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous sources. This allows for rapid and real-world sensitivity analyses. Our first epidemiological GradABM (EpiABMv1) enabled simulation interventions over real million-scale populations and was used in vaccine strategy and policy during the COVID-19 pandemic. Literature suggests decisions aided by evidence from these models saved thousands of lives. Our most recent model (EpiABMv2) extends EpiABMv1 to allow improved regional calibration using deep neural networks to incorporate local population data, and in some cases different policy recommendations versus our prior models. This is an important advance for our model to be more effective at vaccine strategy and policy decisions at the local public health level.

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BACKGROUND: Opioid overdose continues to be a major cause of death in the United States. One effort to control opioid use has been to implement policies that enhance criminalization of opioid possession. Laws to further criminalize possession of fentanyl have been enacted or are under consideration across the country, including at the national level. OBJECTIVE: Estimate the long-term effects on opioid death and incarceration resulting from increasingly strict fentanyl possession laws . DESIGN: We built a Markov simulation model to explore the potential outcomes of a 2022 Colorado law which made possession of >1 g of drug with any amount of fentanyl a Level 4 drug felony (and escalation of the previous law, where >4 g of any drug with any amount of fentanyl in possession was considered a felony). The model simulates a cohort of people with fentanyl possession moving through the criminal justice system, exploring the probability of overdose and incarceration under different scenarios, including various fentanyl possession policies and potential interventions. SETTING: Colorado PARTICIPANTS: A simulated cohort of people in possession of fentanyl. MEASUREMENTS: Number of opioid overdose deaths, people incarcerated, and associated costs over 5 years. RESULTS: When >4 g of a drug containing any amount of fentanyl is considered a felony in Colorado, the model predicts 5460 overdose deaths (95% CrI 410-9260) and 2,740 incarcerations for fentanyl possession (95% CrI: 230-10,500) over 5 years. When the policy changes so that >1 g possession of drug with fentanyl is considered a felony, opioid overdose deaths increase by 19% (95% CRI: 16-38%) and incarcerations for possession increase by 98% (CrI: 85-98%). Diversion programs and MOUD in prison help alleviate some of the increases in death and incarceration, but do not completely offset them. LIMITATIONS: The mathematical model is meant to offer broad assessment of the impact of these policies, not forecast specific and exact numerical outcomes. CONCLUSIONS: Our model shows that lowering thresholds for felony possession of fentanyl containing drugs can lead to more opioid overdose deaths and incarceration.

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Accurately forecasting electrical signals from three-phase Direct Torque Control (DTC) induction motors is crucial for achieving optimal motor performance and effective condition monitoring. However, the intricate nature of multiple DTC induction motors and the variability in operational conditions present significant challenges for conventional prediction methodologies. To address these obstacles, we propose an innovative solution that leverages the Fast Fourier Transform (FFT) to preprocess simulation data from electrical motors. A Bidirectional Long Short-Term Memory (Bi-LSTM) network then uses this altered data to forecast processed motor signals. Our proposed approach is thoroughly examined using a comparative examination of cutting-edge forecasting models such as the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This rigorous comparison underscores the remarkable efficacy of our approach in elevating the precision and reliability of forecasts for induction motor signals. The results unequivocally establish the superiority of our method across stator and rotor current testing data, as evidenced by Mean Absolute Error (MAE) average results of 92.6864 and 93.8802 for stator and rotor current data, respectively. Additionally, compared to alternative forecasting models, the Root Mean Square Error (RMSE) average results of 105.0636 and 85.7820 underscore reduced prediction loss.

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BACKGROUND: Low and middle-income countries face constraints for early colorectal cancer (CRC) detection, including restricted access to care and low colonoscopy capacity. Considering these constraints, we studied strategies for increasing access to early CRC detection and reducing CRC progression and mortality rates in Thailand. METHODS: We developed a system dynamics model to simulate CRC death and progression trends. We analyzed the impacts of increased access to screening via fecal immunochemical test and colonoscopy, improving access to CRC diagnosis among symptomatic individuals, and their combination. RESULTS: Projecting the status quo (2023-2032), deaths per 100K people increase from 87.5 to 115.4, and CRC progressions per 100K people rise from 131.8 to 159.8. In 2032, improved screening access prevents 2.5 CRC deaths and 2.5 progressions per 100K people, with cumulative prevented 7K deaths and 9K progressions, respectively. Improved symptom evaluation access prevents 7.5 CRC deaths per 100K with no effect on progression, totaling 35K saved lives. A combined approach prevents 9.3 deaths and 1.8 progressions per 100K, or 41K and 7K cumulatively. The combined strategy prevents most deaths; however, there is a tradeoff: It prevents fewer CRC progressions than screening access improvement. Increasing the current annual colonoscopy capacity (200K) to sufficient capacity (681K), the combined strategy achieves the best results, preventing 15.0 CRC deaths and 10.3 CRC progressions per 100K people, or 54K and 30K cumulatively. CONCLUSION: Until colonoscopy capacity increases, enhanced screening and symptom evaluation are needed simultaneously to curb CRC deaths, albeit not the best strategy for CRC progression prevention.