SpecSims: A Scalable Speculative Tree-based Simulation Cloning Framework for Finite Memory Machines

In this work we consider the problem of universal sequential probability assignment, under self-information loss, where the machine for performing the universal probability assignment is constrained to have a finite memory. Sequential probability assignment is equivalent to lossless source coding if we ignore the number of states required to convert the probability estimate into code bits. We consider both the probabilistic setting where the sequence is generated by a probabilistic source (either Bernoulli IID source or q-th order Markov source), and the deterministic setting where the sequence is a deterministic individual sequence. We also consider the case where the universal machine is deterministic, randomized, time-invariant or time-variant. We provide in most cases lower bounds and describe finite memory universal machines whose performance, in terms of the memory size, is compared to these bounds.

In this study, the transmission dynamics of hand, foot, and mouth disease (HFMD), incorporating vaccination, were comprehensively assessed.A Susceptible-Vaccinated-Exposed-Infectious-Recovered (SVEIR) model was formulated and its stability was evaluated in relation to disease-free and endemic equilibrium points.The fundamental reproduction number, R0, was derived utilizing the Next-Generation Matrix method.This work demonstrates the local and global asymptotic stability of both disease-free and endemic equilibria under defined conditions.The local stability of the disease-free equilibrium set was ascertained via the Jacobian matrix method, contingent upon certain prerequisites.Conversely, the stability of the endemic equilibrium set was affirmed using the Routh-Hurwitz criteria.In the context of global stability, a Lyapunov function was employed to establish the disease-free equilibrium case, demonstrating that the equilibrium E0 is globally asymptotically stable within region .Stability of the endemic equilibrium set for the susceptible and infected compartments was exhibited using Dulac's criteria.Additionally, a sensitivity analysis was performed, revealing a significant correlation of the basic reproduction number to specific parameters, namely A, 1, 2, 3, 4, and .This analysis indicates that these aforementioned parameters have a substantial influence on HFMD propagation.The analytical findings were corroborated through numerical simulations which further reinforced the validity of the model.This work presents a profound exploration of HFMD transmission dynamics, offering valuable insights for the development of efficacious control strategies.

In responding to the needs of the material characterization community, the recently developed mesh-free random grid method (MFRGM) has been exhibiting very promising characteristics of accuracy, adaptability, implementation flexibility and efficiency. In order to address the design specification of the method according to the intended application, in the present paper we are presenting a sensitivity analysis that aids into determining the effects of the experimental and computational parameters characterizing the MFRGM in terms of its performance. There are two sets of parameters that affect the performance characteristics of MFRGM for whole field strain measurement applications. The first set involves parameters associated with the characteristics of the experimental setup and the random grid applied on the specimen under measurement. The second set involves the computational characteristics of the mesh-free approximation and the solution (minimization) algorithms utilized. The performance characteristics of the MFRGM are mainly its accuracy, sensitivity, smoothing properties and efficiency. In the present paper we are presenting a classification of the first set of parameters as well as their relationship followed by the above mentioned sensitivity analysis to establish the influence trends in the performance characteristics with the intension to optimize the selection of the user controlled variables for a desired performance specification.

Urban areas are increasingly vulnerable to flooding due to rapid urbanization, inadequate stormwater infrastructure, and the intensifying impacts of climate change. To manage these challenges, reliable hydrological models are essential for mitigating the adverse impacts of hydrometeorological events. In data-scarce regions, the calibration of such models is hindered by limited field data. This study presents a practical methodology for calibrating a 1D–2D Stormwater Management Model (SWMM) for tropical urban catchments through a case study in Udon Thani City, Thailand. An iterative calibration process was applied to address major flooding identified in initial simulations, leading to improved runoff routing that ensured compliance with the 5-year, 1-h design storm of the system. The detailed one-factor-at-a-time (OAT) sensitivity analysis identified Curve Number (CN) and Imperviousness as the most influential parameters, followed by sub-catchment width and slope. The findings underscore the importance of prioritizing these parameters for calibration, particularly in regions with limited data availability. The proposed approach provides a scalable and transferable framework for flood risk assessment and management in similar tropical urban catchments globally. By integrating sensitivity analysis into the planning and optimization of green-grey infrastructure, the study contributes to the development of resilient and sustainable stormwater management strategies in vulnerable urban areas.

Frailty is a state of vulnerability in older adults which predicts adverse health outcomes. Although clinical measures, such as a Comprehensive Geriatric Assessment–Frailty Index (CGA-FI) and the Fried phenotype (FP), are reference standards, claims-based measures are often used for research. Given the rapidly growing Medicare Advantage (MA) population and concerns regarding MA data completeness, we assessed performance of claims-based frailty models in detecting frailty by CGA-FI and FP among community-dwelling MA and fee-for-service (FFS) beneficiaries. We measured the Kim’s Claims-based Frailty Index (CFI) and Gilbert’s Hospital Frailty Risk Score (HFRS) in the National Health and Aging Trends Study linked to Medicare claims. In 2019 (FFS n = 2,153, MA n = 1,672), CFI had high specificity (FFS 0.97, MA 0.99) but low sensitivity (FFS 0.31, MA 0.24) in detecting frailty by CGA-FI. The CFI’s PPV was higher for CGA-FI than FP (FFS: 0.84 vs 0.44, MA: 0.90 vs 0.47). In comparison, HFRS had high sensitivity (FFS 0.91, MA 0.91) but low specificity (FFS 0.38, MA 0.43) for CGA-FI frailty, with similar results for FP. Model discrimination was good (C-statistics: CFI 0.81, HFRS 0.79), with both models performing better when benchmarked against CGA-FI. 2021 analyses were similar. In sensitivity analysis using only inpatient claims, HFRS sensitivity decreased while specificity increased. These results suggest that claims-based frailty measures may perform comparably in MA and FFS beneficiaries. However, performance characteristics between models vary – highlighting the importance of model selection for a given population or purpose.

A framework demonstrating the application of inverse modeling technology for engine performance data matching is presented. Transient aero-thermodynamic cycle models are used to simulate engine performance and control characteristics over the entire flight envelope. These models are used not only for engine design and certification but also to provide performance guarantees to the customer and for engine diagnostics. Therefore, it is extremely important that these models are able to accurately predict the performance metrics of interest. Accuracy of these models can be improved by fine-tuning model parameters so that the model output best matches the flight test data. The performance of an aircraft engine is fine tuned from several sensor observations, e.g. exhaust gas temperature, fuel flow, and fan speed. These observations vary with parameters like power level, core speed and operating conditions like altitude, inlet conditions (temperature and pressure), and Mach number, and are used in conjunction with a transient performance simulation model to assess engine performance. This is normally achieved through an iterative manual approach that requires a lot of expert judgment. Simulating transient performance characteristics often requires an engineer to estimate model parameters by matching model response to engine sensor data. Such an estimation problem can be posed using inverse modeling technology. One of the main challenges in the application of inverse modeling for parameter estimation is that the problem can be ill-posed that leads to instability and non-uniqueness of the solution. The inverse method employed here for parameter estimation provides a solution for both well-posed and ill-posed problems. Sensitivity analysis can be used to better pose the data-matching problem. Singular value decomposition (SVD) technique is used to address the ill-posed nature of the inverse problem, which is solved as a finite dimensional non-linear optimization problem. Typically, the transient response is highly nonlinear and it may not be possible to match the whole transient simultaneously. This paper extends the framework on transient inverse modeling developed in [1] for engine transient performance applications. Variable weighting mechanism allows providing different weights to different sensors. This helps in better control on data matching, identify drift in parameter values over time, and point towards incorrect modeling assumptions. The application of the inverse methodology is demonstrated on a single spool non-afterburning engine and a commercial aviation engine model.

Recent advances in passive flying systems inspired by wind-dispersed seeds contribute to increasing interest in their use for remote sensing applications across large spatial domains in the Lagrangian frame of reference. These concepts create possibilities for developing and studying structures with performance characteristics and operating mechanisms that lie beyond those found in nature. Here, we demonstrate a hybrid flier system, fabricated through a process of controlled buckling, to yield unusual geometries optimized for flight. Specifically, these constructs simultaneously exploit distinct fluid phenomena, including separated vortex rings from features that resemble those of dandelion seeds and the leading-edge vortices derived from behaviors of maple seeds. Advanced experimental measurements and computational simulations of the aerodynamics and induced flow physics of these hybrid fliers establish a concise, scalable analytical framework for understanding their flight mechanisms. Demonstrations with functional payloads in various forms, including bioresorbable, colorimetric, gas-sensing, and light-emitting platforms, illustrate examples with diverse capabilities in sensing and tracking.

Global hydrological models (GHM) are indispensable tools for understanding hydrological dynamics in natural settings as well as for analysing complex hydrological human-nature systems in critical regions of the globe and for supporting sustainable management policies in current context and future climate change scenarios. However, GHMs suffer from high predictive uncertainties which stem from input data and climate forcing uncertainties, incomplete knowledge about hydrological processes and their imprecise mathematical description, unknown initial and boundary conditions, and uncertain parameters. Reduction of these uncertainty by model calibration has almost never been performed globally for any GHM due to the high number of parameters in these models, the limited availability of observations of critical hydrological variables at a scale suitable for these models, and the high computational complexity and demand of model calibration. To address these issues, we have developed and employed a parallel and scalable multi-criterial Pareto optimal calibration framework to estimate parameters of the state-of-the-art global hydrological model WaterGAP for 1509 drainage basins with available streamflow observations. Model calibration was done against gauge-based observations of streamflow (Q) and terrestrial water storage anomalies of GRACE/GRACE-FO (TWSA). The influential parameters of each basin were identified prior to calibration by a multi-variable sensitivity analysis for the variables Q, TWSA, and percentage snow cover in the case of basins with relevant snow accumulation. We expect that our study will advance methodologies for sensitivity and calibration analyses of GHMs.

MP55-14 IsoPSA PERFORMANCE CHARACTERISTICS ARE NOT IMPACTED BY 5-ALPHA REDUCTASE INHIBITORS OR ALPHA-BLOCKERS

Research on improving the performance of feedforward neural networks has concentrated mostly on the optimal setting of initial weights and learning parameters, sophisticated optimization techniques, architecture optimization, and adaptive activation functions. An alternative approach is presented in this paper where the neural network dynamically selects training patterns from a candidate training set during training, using the network's current attained knowledge about the target concept. Sensitivity analysis of the neural network output with respect to small input perturbations is used to quantify the informativeness of candidate patterns. Only the most informative patterns, which are those patterns closest to decision boundaries, are selected for training. Experimental results show a significant reduction in the training set size, without negatively influencing generalization performance and convergence characteristics. This approach to selective learning is then compared to an alternative where informativeness is measured as the magnitude in prediction error.

The rise in the popularity of heat pumps should be accompanied by the increase in the utilization of environmentally-safe working fluids. To push for wider uptake of hybrid heat pump systems that use natural working fluids, information about their performance and operating characteristics should be made available. This study models a CO2 solar-assisted ground-coupled heat pump (SAGCHP) system and investigates its performance for space and water heating. Sensitivity analysis, parametric study, and long-term performance simulation were implemented, with the system’s seasonal performance factor (SPF), levelized cost of heating (LCOH), and ground temperature change (GTC) considered as performance indicators. It was seen that ensuring a good combination of design and operating specifications can result in an SPF (∼3.5) and LCOH (0.184 USD/kWh) that are comparable with those of SAGCHP systems that use conventional working fluids. The heat pump’s high-side pressure and output temperature exhibited notable effects on all the performance indicators. Below the optimal operating pressure, a 5% increase in the heat pump’s high-side pressure brought about a ∼7–10% improvement to the SPF, a ∼3–5% reduction to the LCOH, and a ∼6–10% increase to the GTC. Reducing the output temperature by 5% increased the SPF by ∼7–8%, decreased the LCOH by ∼3%, and increased the GTC by ∼6%. Parametric studies identified the presence of optimal heat pump discharge pressure and heat source circulation rate that should be used for operations. Long-term simulation shows that managing the GTC ensures the longevity of the system. Rather than oversizing the borehole heat exchangers (BHEs), it is more practical to reduce ground temperature decline by increasing the BHE spacing or by adding solar collectors.

Critical processes and parameters in the development of accident tolerant fuels drop-in capsule irradiation tests

This paper discusses the steady-state nonlinear reaction-diffusion processes in porous catalysts. This model is based on a nonlinear second-order differential equation that includes a nonlinear term associated with the Michaelis-Menten and non-Michaelis-Menten kinetics of the reaction. The nonlinear equations can be approximately solved using the Taylors series, modified Taylors series and Akabri-Ganji technique to obtain the concentration of dissolved species. The influence of the half-saturation parameter and the characteristic reaction rate on concentration is explored. Sensitivity analysis of parameters is discussed. Our analytical findings were compared with numerical solutions.

In this exposition, a three compartment model has established to analyze the purchasing behavior of buyer. Population for a specific product marketing is classified into non-adopter group N(t), thinker group I(t) and adopter group A(t). The relation within adopter and the non-adopter group is word of mouth. For enhance the impact of word of mouth, media is the another dominating factor for impressing the non-adopter population, who has to become an adopter. In this model, there are two types of buyers instant buyers and thinker buyers. Instant purchasing is an unplanned decision to purchase a brand-new product, done just before purchase. These types of non-adopters become instant adopters. Thinker buyer is not immediate-adopter, and it will take the time for the buying of a brand-new product. Boundedness, Positivity and Basic influence numbers(BINs) of the model is studied. Stability analysis is carried out for all the possible equilibrium points. Hopf bifurcation analysis has also been determined at the adopter-free state and interior equilibrium. Sensitivity analysis for the basic reproduction number and variables about the parameters of the model has been examined for the internal steady state. Finally, numerically experimentations have been carried out to support our analytical findings with a different set of parameters. https://doi.org/10.28919/jmcs/3496

The swelling behavior of clay-sulfate rocks poses significant challenges in geotechnical engineering due to complex hydro-mechanical and chemical interactions. This study introduces a hybrid framework combining finite element modeling (FEM) with machine learning (ML) to efficiently analyze and predict the nonlinear behavior of swelling processes. We generated synthetic datasets representing swelling phenomena at the Staufen site, Germany, using a coupled hydro-mechanical FEM simulation in OpenGeoSys. A parametric analysis was conducted to systematically vary critical parameters, including Young's modulus, permeability, maximum swelling pressure, and air entry pressure, to capture the inherent uncertainty and variability of swelling processes. A constrained CatBoost ML model was trained on the FEM outputs to predict porosity, saturation, and displacement under varying hydro-mechanical conditions. Results demonstrate strong alignment between the ML surrogate and FEM simulations, achieving high accuracy while significantly reducing computational demands. Sensitivity analysis indicated the dominance of swelling pressure and Young's modulus in influencing swelling-induced deformation, while Monte Carlo simulations quantified prediction uncertainties. This contribution discusses the potential of integrating FEM with ML for site-specific risk assessment and mitigation planning in geotechnical engineering.