Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS.

This paper presents a new modeling method to abstract the collective behavior of Smart IoT Systems in CPS, based on process algebra and a lattice structure. In general, process algebra is known to be one of the best formal methods to model IoTs, since each IoT can be represented as a process; a lattice can also be considered one of the best mathematical structures to abstract the collective behavior of IoTs since it has the hierarchical structure to represent multi-dimensional aspects of the interactions of IoTs. The dual approach using two mathematical structures is very challenging since the process algebra have to provide an expressive power to describe the smart behavior of IoTs, and the lattice has to provide an operational capability to handle the state-explosion problem generated from the interactions of IoTs. For these purposes, this paper presents a process algebra, called dTP-Calculus, which represents the smart behavior of IoTs with non-deterministic choice operation based on probability, and a lattice, called n:2-Lattice, which has special join and meet operations to handle the state explosion problem. The main advantage of the method is that the lattice can represent all the possible behavior of the IoT systems, and the patterns of behavior can be elaborated by finding the traces of the behavior in the lattice. Another main advantage is that the new notion of equivalences can be defined within n:2-Lattice, which can be used to solve the classical problem of exponential and non-deterministic complexity in the equivalences of Norm Chomsky and Robin Milner by abstracting them into polynomial and static complexity in the lattice. In order to prove the concept of the method, two tools are developed based on the ADOxx Meta-Modeling Platform: SAVE for the dTP-Calculus and PRISM for the n:2-Lattice. The method and tools can be considered one of the most challenging research topics in the area of modeling to represent the collective behavior of Smart IoT Systems.

Meta-modeling methods for estimating ammonia volatilization from nitrogen fertilizer and manure applications.

Accurate estimations of ammonia (NH3) emissions due to nitrogen (N) fertilization are required to identify efficient mitigation techniques and improve agricultural practices. Process-based models such as Volt'Air can be used for this purpose because they incorporate the effects of several key factors influencing NH3 volatilization at fine spatio-temporal resolutions. However, these models require a large number of input variables and their implementation on a large scale requires long computation times that may restrict their use by public environmental agencies. In this study, we assess the capabilities of various types of meta-models to emulate the complex process-based Volt'Air for estimating NH3 emission rates from N fertilizer and manure applications. Meta-models were developed for three types of fertilizer (N solution, cattle farmyard manure, and pig slurry) for four major agricultural French regions (Bretagne, Champagne-Ardenne, Ile-de-France, and Rhône-Alpes) and at the national (France) scale. The meta-models were developed from 106,092 NH3 emissions simulated by Volt'Air in France. Their performances were evaluated by cross-validation, and the meta-models providing the best approximation of the original model were selected. The results showed that random forest and ordinary linear regression models were more accurate than generalized additive models, partial least squares regressions, and least absolute shrinkage and selection operator regressions. Better approximations of Volt'Air simulations were obtained for cattle farmyard manure (3% < relative root mean square error of prediction (RRMSEP) < 8%) than for pig slurry (17% < RRMSEP < 19%) and N solution (21% < RRMSEP < 40%). The selected meta-models included between 6 and 15 input variables related to weather conditions, soil properties and cultural practices. Because of their simplicity and their short computation time, our meta-models offer a promising alternative to process-based models for NH3 emission inventories at both regional and national scales. Our approach could be implemented to emulate other process-based models in other countries.

Optimizing nurse capacity in a teaching hospital neonatal intensive care unit.

Patients in intensive care units need special attention. Therefore, nurses are one of the most important resources in a neonatal intensive care unit. These nurses are required to have highly specialized training. The random number of patient arrivals, rejections, or transfers due to lack of capacity (such as nurse, equipment, bed etc.) and the random length of stays, make advanced knowledge of the optimal nurse a requirement, for levels of the unit behave as a stochastic process. This stochastic nature creates difficulties in finding optimal nurse staffing levels. In this paper, a stochastic approximation which is based on the required nurse: patient ratio and the number of patients in a neonatal intensive care unit of a teaching hospital, has been developed. First, a meta-model was built to generate simulation results under various numbers of nurses. Then, those experimented data were used to obtain the mathematical relationship between inputs (number of nurses at each level) and performance measures (admission number, occupation rate, and satisfaction rate) using statistical regression analysis. Finally, several integer nonlinear mathematical models were proposed to find optimal nurse capacity subject to the targeted levels on multiple performance measures. The proposed approximation was applied to a Neonatal Intensive Care Unit of a large hospital and the obtained results were investigated.

From Ambient Sensing to IoT-based Context Computing: An Open Framework for End to End QoC Management.

Quality of Context (QoC) awareness is recognized as a key point for the success of context-aware computing. At the time where the combination of the Internet of Things, Cloud Computing, and Ambient Intelligence paradigms offer together new opportunities for managing richer context data, the next generation of Distributed Context Managers (DCM) is facing new challenges concerning QoC management. This paper presents our model-driven QoCIM framework. QoCIM is the acronym for Quality of Context Information Model. We show how it can help application developers to manage the whole QoC life-cycle by providing genericity, openness and uniformity. Its usages are illustrated, both at design time and at runtime, in the case of an urban pollution context- and QoC-aware scenario.

Global sensitivity analysis of a mathematical model of acute inflammation identifies nonlinear dependence of cumulative tissue damage on host interleukin-6 responses.

The precise inflammatory role of the cytokine interleukin (IL)-6 and its utility as a biomarker or therapeutic target have been the source of much debate, presumably due to the complex pro- and anti-inflammatory effects of this cytokine. We previously developed a nonlinear ordinary differential equation (ODE) model to explain the dynamics of endotoxin (lipopolysaccharide; LPS)-induced acute inflammation and associated whole-animal damage/dysfunction (a proxy for the health of the organism), along with the inflammatory mediators tumor necrosis factor (TNF)-α, IL-6, IL-10, and nitric oxide (NO). The model was partially calibrated using data from endotoxemic C57Bl/6 mice. Herein, we investigated the sensitivity of the area under the damage curve (AUCD) to the 51 rate parameters of the ODE model for different levels of simulated LPS challenges using a global sensitivity approach called Random Sampling High Dimensional Model Representation (RS-HDMR). We explored sufficient parametric Monte Carlo samples to generate the variance-based Sobol' global sensitivity indices, and found that inflammatory damage was highly sensitive to the parameters affecting the activity of IL-6 during the different stages of acute inflammation. The AUCIL6 showed a bimodal distribution, with the lower peak representing healthy response and the higher peak representing sustained inflammation. Damage was minimal at low AUCIL6, giving rise to a healthy response. In contrast, intermediate levels of AUCIL6 resulted in high damage, and this was due to the insufficiency of damage recovery driven by anti-inflammatory responses from IL-10 and the activation of positive feedback sustained by IL-6. At high AUCIL6, damage recovery was interestingly restored in some population of simulated animals due to the NO-mediated anti-inflammatory responses. These observations suggest that the host's health status during acute inflammation depends in a nonlinear fashion on the magnitude of the inflammatory stimulus, on the host's propensity to produce IL-6, and on NO-mediated downstream responses.

Metamodeling-based reliability analysis framework for activated sludge processes.

The reliability of activated sludge processes will be adversely affected by alterations in wastewater production and pollutant loading foreseen due to population growth, urbanization, and climate change, as well as the tendency to amend environmental regulations to mandate stricter effluent quality standards to alleviate water pollution. Until now, there was no framework capable of effectively managing these multifaceted challenges in reliability analysis. Previous attempts conducted a low number of simulations leading to insufficient statistical significance to properly validate reliability quantification. A metamodeling-based reliability analysis framework for the activated sludge process is introduced to cope with alterations in wastewater production and pollutant loading, assesses the reliability under different effluent regulations, and leverages metamodels to conduct extensive simulation work, to estimate the reliability. All metamodels produced high-resolution results, enabling reliability estimation after 100 000 simulations. The framework effectively assessed the annual failure rates of various activated sludge facility designs under four regulations, demonstrating the impact of stricter effluent quality standards. Integrating metamodels for reliability analysis greatly lowers computational costs, making the framework a time and resource-efficient choice for quick decision-making in facility design.

Physician-Customized Strategies for Reducing Outpatient Waiting Time in South Korea Using Queueing Theory and Probabilistic Metamodels.

The time a patient spends waiting to be seen by a healthcare professional is an important determinant of patient satisfaction in outpatient care. Hence, it is crucial to identify parameters that affect the waiting time and optimize it accordingly. First, statistical analysis was used to validate the effective parameters. However, no parameters were found to have significant effects with respect to the entire outpatient department or to each department. Therefore, we studied the improvement of patient waiting times by analyzing and optimizing effective parameters for each physician. Queueing theory was used to calculate the probability that patients would wait for more than 30 min for a consultation session. Using this result, we built metamodels for each physician, formulated an effective method to optimize the problem, and found a solution to minimize waiting time using a non-dominated sorting genetic algorithm (NSGA-II). On average, we obtained a 30% decrease in the probability that patients would wait for a long period. This study shows the importance of customized improvement strategies for each physician.

When Do We Need Massive Computations to Perform Detailed COVID-19 Simulations?

The COVID-19 pandemic has infected over 250 million people worldwide and killed more than 5 million as of November 2021. Many intervention strategies are utilized (e.g., masks, social distancing, vaccinations), but officials making decisions have a limited time to act. Computer simulations can aid them by predicting future disease outcomes, but they also require significant processing power or time. It is examined whether a machine learning model can be trained on a small subset of simulation runs to inexpensively predict future disease trajectories resembling the original simulation results. Using four previously published agent-based models (ABMs) for COVID-19, a decision tree regression for each ABM is built and its predictions are compared to the corresponding ABM. Accurate machine learning meta-models are generated from ABMs without strong interventions (e.g., vaccines, lockdowns) using small amounts of simulation data: the root-mean-square error (RMSE) with 25% of the data is close to the RMSE for the full dataset (0.15 vs 0.14 in one model; 0.07 vs 0.06 in another). However, meta-models for ABMs employing strong interventions require much more training data (at least 60%) to achieve a similar accuracy. In conclusion, machine learning meta-models can be used in some scenarios to assist in faster decision-making.

The impact of shape uncertainty on aortic-valve pressure-drop computations.

Patient-specific image-based computational fluid dynamics (CFD) is widely adopted in the cardiovascular research community to study hemodynamics, and will become increasingly important for personalized medicine. However, segmentation of the flow domain is not exact and geometric uncertainty can be expected which propagates through the computational model, leading to uncertainty in model output. Seventy-four aortic-valves were segmented from computed tomography images at peak systole. Statistical shape modeling was used to obtain an approximate parameterization of the original segmentations. This parameterization was used to train a meta-model that related the first five shape mode coefficients and flowrate to the CFD-computed transvalvular pressure-drop. Consequently, shape uncertainty in the order of 0.5 and 1.0 mm was emulated by introducing uncertainty in the shape mode coefficients. A global variance-based sensitivity analysis was performed to quantify output uncertainty and to determine relative importance of the shape modes. The first shape mode captured the opening/closing behavior of the valve and uncertainty in this mode coefficient accounted for more than 90% of the output variance. However, sensitivity to shape uncertainty is patient-specific, and the relative importance of the fourth shape mode coefficient tended to increase with increases in valvular area. These results show that geometric uncertainty in the order of image voxel size may lead to substantial uncertainty in CFD-computed transvalvular pressure-drops. Moreover, this illustrates that it is essential to assess the impact of geometric uncertainty on model output, and that this should be thoroughly quantified for applications that wish to use image-based CFD models.

A finite element-guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions.

A finite element (FE)-guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body's response to impacts. FE modeling is often used for crash analyses but requires time and computational cost. However, surrogate modeling can predict injury trends between the FE data, requiring fewer FE simulations to evaluate the complete testing range. To determine the viability of this methodology for injury assessment, crash-induced occupant head injury criterion (HIC15) trends were predicted from Kriging models across varied impact velocities (10-45 mph; 16.1-72.4 km/h), locations (near side, far side, front, and rear), and angles (-45 to 45°) and compared to previously published data. These response trends were analyzed to locate high-risk target regions. Impact velocity and location were the most influential factors, with HIC15 increasing alongside the velocity and proximity to the driver. The impact angle was dependent on the location and was minimally influential, often producing greater HIC15 under oblique angles. These model-based head injury trends were consistent with previously published data, demonstrating great promise for the proposed methodology, which provides effective and efficient quantification of human response across a wide variety of car crash scenarios, simultaneously. This study presents a finite element-guided mathematical surrogate modeling methodology to evaluate occupant injury response trends for a wide range of impact velocities (10-45 mph), locations, and angles (-45 to 45°). Head injury response trends were predicted and compared to previously published data to assess the efficacy of the methodology for assessing occupant response to variations in impact conditions. Velocity and location were the most influential factors on the head injury response, with the risk increasing alongside greater impact velocity and locational proximity to the driver. Additionally, the angle of impact variable was dependent on the location and, thus, had minimal independent influence on the head injury risk.

Risk analysis for the highly pathogenic avian influenza in Mainland China using meta-modeling.

A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference vegetation index, environmental factors were considered in generating a probability map with the aid of logistic regression. A Bayesian maximum entropy model was employed to explore the spatial and temporal correlations of HPAI incidence. The results show that proximity to water bodies and national highways was statistically relevant to the occurrence of HPAI. Migratory birds, mainly waterfowl, were important infection sources in HPAI transmission. In addition, the HPAI outbreaks had high spatiotemporal autocorrelation. This epidemic spatial range fluctuated 45 km owing to different distribution patterns of cities and water bodies. Furthermore, two outbreaks were likely to occur with a period of 22 d. The potential risk of occurrence of HPAI in Mainland China for the period from January 23 to February 17, 2004 was simulated based on these findings, providing a useful meta-model framework for the application of environmental factors in the prediction of HPAI risk.

Combining statistical shape modeling, CFD, and meta-modeling to approximate the patient-specific pressure-drop across the aortic valve in real-time.

BACKGROUND: Advances in medical imaging, segmentation techniques, and high performance computing have stimulated the use of complex, patient-specific, three-dimensional Computational Fluid Dynamics (CFD) simulations. Patient-specific, CFD-compatible geometries of the aortic valve are readily obtained. CFD can then be used to obtain the patient-specific pressure-flow relationship of the aortic valve. However, such CFD simulations are computationally expensive, and real-time alternatives are desired. AIM: The aim of this work is to evaluate the performance of a meta-model with respect to high-fidelity, three-dimensional CFD simulations of the aortic valve. METHODS: Principal component analysis was used to build a statistical shape model (SSM) from a population of 74 iso-topological meshes of the aortic valve. Synthetic meshes were created with the SSM, and steady-state CFD simulations at flow-rates between 50 and 650 mL/s were performed to build a meta-model. The meta-model related the statistical shape variance, and flow-rate to the pressure-drop. RESULTS: Even though the first three shape modes account for only 46% of shape variance, the features relevant for the pressure-drop seem to be captured. The three-mode shape-model approximates the pressure-drop with an average error of 8.8% to 10.6% for aortic valves with a geometric orifice area below 150 mm2 . The proposed methodology was least accurate for aortic valve areas above 150 mm2 . Further reduction to a meta-model introduces an additional 3% error. CONCLUSIONS: Statistical shape modeling can be used to capture shape variation of the aortic valve. Meta-models trained by SSM-based CFD simulations can provide an estimate of the pressure-flow relationship in real-time.