Uncertainty In Input Parameters Research Articles

A “data-driven uncertainty” computational method to model and predict instabilities of a frictional system

Most of the recently developed methods for predicting instabilities of frictional systems couple stochastic algorithms with the finite element method (FEM). They use random variables to model the uncertainty of input parameters through standard probability laws. Regardless of the fact that advanced numerical schemes are available nowadays, a systematic and accurate method to describe finely the uncertainties upstream the model, and thus predict its response is still missing. In this contribution, we present a data-driven stochastic finite element scheme to predict the dynamic behavior of a rubbing system. The proposed framework relies on data-driven approach and uses four steps. In the first, the measured data are integrated directly, for the uncertainty quantification, by means of the random balance design (RBD). In the second step, the generated stochastic data are evaluated in an iterative way to solve friction-induced vibration problem. In the third step, the resulted data are reordered in such a way that the corresponding values of each measured input parameters are ranked in ascending order. Finally, the Fourier spectrum is introduced on the reordered results to compute the sensitivity indices. Thus, instead of Monte Carlo-based formalism or Fourier Amplitude Sensitivity Test (FAST), the computational cost of the proposed method is kept down to mathcal {O}(N) with N the number of samples. We investigate the efficiency of the suggested solver on a reduced brake system. Altogether, the suggested procedure achieves excellent accuracy at a much reduced computational time compared to the methods available in the literature.

Development of water re-allocation policy under uncertainty conditions in the inflow to reservoir and demands parameters: a case study of Karaj AmirKabir dam

The process of optimal operation of multipurpose reservoirs is accompanied by large dimensions of decision variables and the uncertainty of hydrological parameters and water demands. Therefore, in determining the optimal operation policies (OOPs), the decision making for water allocation is faced with problems and complexities. One of the effective approaches for sustainable management and optimal allocation from water resources is the multi-objective structural development based on the uncertainty of input parameters. The purpose of this study is to provide OOPs from Karaj AmirKabir multi-purpose reservoir with applying uncertainty in the inflow to reservoir and downstream water demand. The proposed approach has been investigated in two certain and uncertain models, and three objective functions of the system including maximizing hydropower generation, water supply demands, and flood control have been considered to formulate OOPs. Non-dominated sorting genetic algorithm-II (NSGA-II) was performed to optimize the three proposed objective functions and by applying multi-criteria decision-making (MCDM) methods, the best operation scenario was selected. In the uncertainty model, using the interval method and repeated implementation of the deterministic model for completely random scenarios that generated based on the variation interval of the uncertain parameters, the non-deterministic optimal allocation values were produced. Based on these optimal allocation values and the fitting of the standard probability distribution on it, the probability of occurrence of the deterministic allocation values was determined. Production of optimal probabilistic allocation policies can be very useful and efficient in providing real vision to managers to select appropriate policies in different conditions and rare hydrological events. The results obtained from the certain model shows that as a result of optimal allocation to demands, the fuzzy reliability, resiliency, and system stability indexes were improved to 67.81, 21.99, and 24.98 percentage, respectively. Also, in an uncertain model, applying changes of 48% and 22%, respectively, for the inflow and downstream demand has led to changes of 23%, 55%, and 18%, respectively, in the first, second, and third objective functions. The highest impact from uncertain conditions, has been related to the water supply demands with 55% of the range of variations So, the water supply demands, has a higher sensitivity and priority than other reservoir objective functions under uncertain conditions. Another important result extracted from this study is to determine the monthly probability of optimal allocations achievement. Accordingly, in the warm seasons and years in which the reservoir is facing drought, the occurrence probability of the optimal allocations decreases. Given the comprehensiveness of the proposed methodology, this approach is a very suitable tool for determining the optimal water allocations as probabilistic based on the scenarios desired by managers and reservoir operators.

Identifying Contributors to PM2.5 Simulation Biases of Chemical Transport Model Using Fully Connected Neural Networks

AbstractAccurate prediction of ambient PM2.5 concentrations using air quality models can provide governments with information for public health alerts. However, due to large uncertainties of input parameters and over‐simplification of the chemical mechanism, the model simulations tend to have a certain deviation from the observations. To provide an insight into the discrepancy and to explain the contributors to the model bias, we propose here a machine learning based method to identify the contributors to PM2.5 simulation biases. A fully connected deep neural network (noted as FCNN) was designed to correct the PM2.5 biases between the simulations from a common air quality model (i.e., Community Multiscale Air Quality, CMAQ) and observations with meteorological and pollutants variables. The FCNN was applied in two polluted regions in China including Beijing‐Tianjin‐Hebei (BTH) and Yangtze River Delta (YRD) in 2015, exhibiting excellent performance in reducing the root mean square error of annual PM2.5 by 46.6% and 37.2%, respectively. The relative contribution of each input feature for the bias correction was also estimated from the FCNN. Results suggest that the temperature and humidity exhibit the greatest contribution to the PM2.5 simulation bias among all meteorological factors, probably due to their high association with the physical and chemical reaction conditions. NO2 and SO2 concentrations and associated biases were also found to be crucial to CMAQ model accuracy, implying the importance of NO2‐ and SO2‐related reaction for PM2.5 formation. The study also revealed a cumulative effect of pollution and an enhancement effect of atmospheric oxidation on the formation of heavy pollution.

Assessing techno-economic uncertainties in nuclear power-to-X processes: The case of nuclear hydrogen production via water electrolysis

Nuclear assisted low carbon hydrogen production by water electrolysis represents a potential application of nuclear cogeneration towards deep decarbonization of several fossil fuel-dependent industrial sectors. This work builds a probabilistic techno-commercial model of a water electrolysis plant coupled to an existing nuclear reactor for base load operations. The objective is to perform discounted cash flow (DCF) calculations for levelized nuclear hydrogen production cost under input parameter uncertainty. The probability distributions of inputs are used with the Monte Carlo-Latin Hypercube (MC-LH) sampling technique to generate 105 input scenarios and corresponding distribution of the levelized or life cycle hydrogen production cost instead of deterministic point values. Based on current techno-economic conditions, the levelized production costs of electrolytic hydrogen using electricity from large water-cooled nuclear reactors are determined to be US $ 12.205 ± 1.342, 8.384 ± 1.148 and 6.385 ± 1.051/kg H2 respectively at rated alkaline water electrolyser capacities of 1.25 MW(e), 2.5 MW(e) and 5 MW(e). The corresponding values for PEM water electrolysers are US $ 13.162 ± 1.356, 8.891 ± 1.141 and 6.663 ± 1.057/kg H2. The potential for flexible nuclear reactor operation and management of power demand uncertainties through nuclear hydrogen cogeneration is also examined through a case study.

Bottom-up multilevel flood hazard mapping by integrated inundation modelling in data scarce cities

With increasing urban flood risk due to urbanisation and climate change, flood hazard prediction is ever more crucial for flood risk management and emergency response. However, when sufficient high-quality data are lacking, a standard flood inundation modelling approach has significant uncertainties. Therefore, this study develops a bottom-up approach for urban flood hazard mapping at multiple levels (grid-kilometre-district), built upon the integration of grid-based flood modelling with data acquisition from open sources. This reduces the adverse effects of data scarcity and quality on hazard modelling. In the paper, we first set out an integrated approach for gridded inundation mapping in an urban basin using a hydrodynamic model supported by crowd-sourced social media data. Then, applying the model to flooding in Chengdu in August 2020, we articulate how input data bias and parameter uncertainty both affect urban inundation modelling. The results show that the choice of terrain data and the quantification of urban drainage flows significantly influence the modelled urban inundation extent, depth and duration. This indicates the potential for large variation when using urban inundation mapping merely relying on a single-scale flood hydrodynamic modelling even when supported by crowd-sourced data. The multilevel hazard mapping approach developed here presents multi-layered and comprehensive inundation mapping; thus the effects of data bias or availability are reduced and the coarser hazard mapping shows less sensitivity to the data input quality and model uncertainty, indicating relatively higher reliability at this higher spatial scale. The grid-kilometre-district three level approach provides more reliable flood hazard mapping, which can support rainstorm-induced flood management in data scarce cities.

Cost-effectiveness of Novel Macrophage-Regulating Treatment for Wound Healing in Patients With Diabetic Foot Ulcers From the Taiwan Health Care Sector Perspective

Diabetic foot ulcers (DFUs) and subsequent amputation incur enormous health and economic burdens to patients, health care systems, and societies. As a novel macrophage-regulating drug, ON101 is a breakthrough treatment for DFUs, which demonstrated significant complete wound healing effects in a phase 3 randomized clinical trial, but its economic value remains unknown. To assess the cost-effectiveness of an ON101 cream added on to general wound care (GWC; ie, conventional treatments for DFUs, which comprised initial and regular foot examinations, ulcer management, comorbidity control, patient education, and multidisciplinary care) vs GWC alone for DFUs from the Taiwan health care sector perspective. This economic evaluation used a hypothetical cohort of patients with diabetes, with characteristics mirroring those of the participants in the ON101 trial. A Markov state-transition simulation model was constructed to estimate costs and health outcomes associated with the ON101 with GWC and GWC alone strategies over a 5-year time horizon, discounting costs and effectiveness at 3% annually. Costs were in 2021 US dollars. Data were sourced from the ON101 trial and supplemented from published literature. Deterministic and probabilistic sensitivity analyses were performed to assess the uncertainty of input parameters and study generalizability. The analysis was designed and conducted from September 1, 2020, to January 31, 2022. ON101 with GWC vs GWC alone. DFU-related complications, costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio. Patients in the hypothetical cohort had a mean age of 57 years and an uninfected DFU of 1 to 25 cm2 that was present for 4 or more weeks with a Wagner grade of 1 or 2. Over 5 years, the ON101 with GWC group vs the GWC alone group experienced more healing events, stayed for a longer time in the healing state, and had fewer infected DFUs, gangrene, and amputations (eg, 2787 additional healing events and 2766 fewer infected DFU, 72 fewer amputation, and 7 fewer gangrene events in the ON101 with GWC group vs GWC alone group). The ON101 with GWC strategy vs GWC alone yielded an additional 0.038 QALYs at an incremental cost of $571, resulting in $14 922/QALY gained. Economic results were most sensitive to healing efficacy, drug cost, and health utility of the healing state. Cost-saving results were observed in patient subgroups with poor glycemic control, larger ulcer sizes, longer ulcer durations, and current smoking. The ON101 with GWC strategy was considered cost-effective in 60% to 82% of model iterations against willingness-to-pay thresholds of $32 787/QALY gained to $98 361/QALY gained. In this economic evaluation study using a simulated patient cohort, the ON101 with GWC strategy represented good value compared with GWC alone for patients with DFUs from the Taiwan health care sector perspective and may be prioritized for those with high risks for disease progression of DFUs.

A rate-of-rise facility for measuring properties of wick structures

This work details a mass rate-of-rise (mROR) apparatus and analysis method for the accurate and precise determination of capillary wick parameters: permeability, K, effective pore radius, r eff, and porosity, ϵ. Three factors were examined: (a) the accuracy of the theoretical models and their curve-fitting approaches associated with the mROR technique, (b) the influence of the experimental procedure on repeatability, and (c) how the uncertainty of the experimental input parameters propagates through the data analysis procedure and compounds the overall uncertainty of the wick parameters (K and r eff). Four models and fittings methods were investigated: the Lucas–Washburn method, the gravity-based dm/dt method, the gravity-based t–m method, and the gravity-based m–t method. It is demonstrated that the m–t method developed here shows the lowest error and, equally importantly, that it is free of user decisions in the context of ‘data scrubbing’ because the entire mROR data set is used in its raw form. To test accuracy and repeatability, a precision-controlled mROR apparatus is proposed. Experiments were performed for commercially available wicks. A robust Monte Carlo error analysis method was developed and applied to quantify the overall uncertainty in the wick parameters as a function of the input uncertainties of all measured quantities.

Acceleration of Nuclear Reactor Simulation and Uncertainty Quantification Using Low-Precision Arithmetic

In recent years, interest in approximate computing has been increasing significantly in many disciplines in the context of saving energy and computation cost by trading off on the quality of numerical simulation. The hardware acceleration based on the low-precision floating-point arithmetic is anticipated by the upcoming generation of microprocessors and code compilers and has already proven to be beneficial for weather and climate modelling and neural network training. The present work illustrates the application of low-precision arithmetic for the nuclear reactor core uncertainty analysis. We studied the performance of an elementary transient reactor core model for the arbitrary precision of the floating-point multiplication in a direct linear system solver. Using this model, we calculated the reactor core transients initiated by the control rod ejection taking into account the uncertainty of the model input parameters. Then, we evaluated the round-off errors of the model outputs for different precision levels. The comparison of the round-off errors and the model uncertainty showed the model could be run using a 15-bit floating-point number precision with an acceptable degradation of the result’s accuracy. This precision corresponds to a gain of about 6× in the bit complexity of the linear system solution algorithm, which can be actualized in terms of reduced energy costs on low-precision hardware.

Estimated Incidence and Prevalence of Gonorrhea in the United States, 2006-2019.

We extend recent work estimating incidence and prevalence of gonococcal infections among men and women aged 15 to 39 years in the United States in 2018 by applying the same modeling framework to estimate gonococcal incidence and prevalence during 2006 to 2019. The model is informed by cases from the Nationally Notifiable Disease Surveillance System, data from the National Survey of Family Growth, and data on other factors known to impact gonococcal incidence and prevalence. We use Monte Carlo simulation to account for uncertainty in input parameters. Results are reported as median annual per-capita incidence and prevalence; uncertainty intervals are characterized by the 25th and 75th simulated percentiles. There were 1,603,473 (1,467,801-1,767,779) incident cases of gonorrhea estimated in 2019. Per-capita incidence increased 32%, from 1101 (1002-1221) to 1456 (1333-1605) infections per 100,000 persons. This trend in per-capita incidence had 3 phrases: an initial decline during 2006 to 2009, a plateau through 2013, and a rapid increase of 66% through 2019. Men aged 25 to 39 years experienced the greatest increase in incidence (125%, 541 [467-651] to 1212 infections [1046-1458] per 100,000 men). Women aged 25 to 39 years had the lowest incidence in 2019, with 1040 infections (882-1241) per 100,000 women. Prevalence increased more slowly among those aged 25 to 39 years versus 15 to 24 years. The incidence ratio comparing men with women aged 25 to 39 years increased from 0.76 to 1.18. The burden of gonorrhea has increased among men and women aged 15 to 39 years since 2013. An increasing proportion of incident infections are among men. Additional biomedical and behavioral interventions are needed to control gonococcal transmission.

Control of the lifecycle of a technological complex for the rectification of multicomponent mixtures under conditions of parameter uncertainty

Input parameter uncertainty is paid more attention among such parameters as parameter uncertainty, model uncertainty, choice uncertainty under variability, spatial variability uncertainty, temporal variability uncertainty, inter-site variability uncertainty. When the technological component changes, the control starts working again based on a new paradigm of internal and external circuits. The possibility of using the Monte Carlo method for the analysis and estimation of the uncertainty of input parameters is presented. In the analysis of the uncertainties of the input parameters, the simplification of the selection process and the probability density functions were used. Deviations from the normal distribution were represented by a matrix transformation. The technological function of the rectification complex is presented in an expanded form, the uncertainty of parameters and control actions are highlighted separately. The influence of the uncertainty of actions and the uncertainty of input parameters on the functions performed by the technological complex, the functions of planning and coordination is analyzed. It is argued that the impact of parameter uncertainty on these functions has a direct impact on the state of the complex in its lifecycle.

Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm

The thermal infrared sensor-2 (TIRS-2) carried on Landsat 9 is the newest thermal infrared (TIR) sensor for the Landsat project and provides two adjacent TIR bands, which greatly benefits the land surface temperature (LST) retrieval at high spatial resolution. In this article, a radiance based split window (RBSW) algorithm for retrieving LST from Landsat 9 TIRS-2 data was proposed. In addition, the split-window covariance-variance ratio (SWCVR) algorithm was improved and applied to Landsat 9 TIRS-2 data for estimating atmospheric water vapor (AWV) that is required for accurate LST retrieval. The performance of the proposed method was assessed using the simulation data and satellite observations. Results reveal that the retrieved LST using the RBSW algorithm has a bias of 0.06 K and root-mean-square error (RMSE) of 0.51 K based on validation with the simulation data. The sensitivity analysis exhibited a LST error of <1.75 K using the RBSW algorithm when the uncertainties in input parameters (i.e., AWV, emissivity, and at-sensor radiance) were considered. There is a marginal discrepancy between LST retrievals using the estimated AWV and moderate resolution imaging spectroradiometer AWV, with a difference in bias of −0.14 K and RMSE of 0.22 K, which indicates that the improved SWCVR method can provide an optional means to obtain AWV inputted in LST retrieval. With regard to the validation using the in situ measurements, the retrieved LST from Landsat 9 TIRS-2 data exhibits a bias of 0.44 K and RMSE of 1.98 K, respectively, showing a higher accuracy than the USGS Landsat LST product with bias of 1.21 and RMSE of 2.56 K. In conclusion, the proposed algorithm combining RBSW algorithm and improved SWCVR algorithm for LST retrieval from Landsat 9 has a good accuracy without dependence on external atmospheric data, and it is expected to be a reliable method for LST generation from Landsat 9 TIRS-2 data.

A Neural Emulator for Uncertainty Estimation of Fire Propagation

Wildfire propagation is a highly stochastic process where small changes in environmental conditions (such as wind speed and direction) can lead to large changes in observed behaviour. A traditional approach to quantify uncertainty in fire-front progression is to generate probability maps via ensembles of simulations. However, use of ensembles is typically computationally expensive, which can limit the scope of uncertainty analysis. To address this, we explore the use of a spatio-temporal neural-based modelling approach to directly estimate the likelihood of fire propagation given uncertainty in input parameters. The uncertainty is represented by deliberately perturbing the input weather forecast during model training. The computational load is concentrated in the model training process, which allows larger probability spaces to be explored during deployment. Empirical evaluations indicate that the proposed model achieves comparable fire boundaries to those produced by the traditional SPARK simulation platform, with an overall Jaccard index (similarity score) of 67.4% on a set of 35 simulated fires. When compared to a related neural model (emulator) which was employed to generate probability maps via ensembles of emulated fires, the proposed approach produces competitive Jaccard similarity scores while being approximately an order of magnitude faster.

Probabilistic Soil Liquefaction Assessment: A Case Study for Agartala City in Tripura in North-East India

A sudden increase in pore water pressure causes the effective stress to decrease significantly, which in turn causes a loss of shear strength and the subsequent outcome of allowing the soil to behave as a liquid, resulting in soil liquefaction. Agartala, the capital of Tripura, is located in northeastern India, and the entire region is considered seismically active according to the seismic zoning map of India as per IS 1893:2002 (Part 1); the entire region is classified as Zone-V. Significant earthquakes have struck this region, including the 1897 Shillong and 1950 Assam earthquakes. The city is vulnerable to liquefaction due to alluvial soil and a low groundwater table. Several parameters are involved in assessing liquefaction potential in the deterministic method and have different input parameter uncertainties, resulting in inconsistent outcomes. As a result, evaluating liquefaction susceptibility requires a thorough probability approach that considers parameter uncertainty. In this work, the soil liquefaction potential of Agartala City is assessed using fuzzy logic and compared to the conventional Reliability technique using the first-order second-moment method. Soil liquefaction potential was properly calculated using both approaches for the 71 boreholes in and around Agartala city. The outcomes are shown as contour maps representing spatial variations of the probability of liquefaction at different depths. In this study, Fuzzy logic is used to model the relationships between different soil parameters, which can help better understand the soil\'s behaviour during liquefaction. This developed method can assess the vulnerability of liquefaction potential and select appropriate materials and construction processes.

BEPU analysis of a CANDU LBLOCA RD-14M experiment using RELAP/SCDAPSIM

A key element of the safety analysis is Loss of Coolant Analysis (LOCA) which must be performed using system thermal-hydraulic codes. These codes are extensively validated against separate effect and integral experiments.RELAP/SCDAPSIM is one such code that may be used to predict LBLOCA response in a CANDU reactor. The RD-14M experiment selected for the Best Estimate Plus Uncertainty study is a 44 mm (22.7%) inlet header break test with no Emergency Coolant Injection. This work has two objectives first is to simulate pipe break with RELAP and compare these results to those available from experiment and from comparable TRACE calculations. The second objective is to quantify uncertainty in the fuel element sheath (FES) temperature arising from model coefficient as well as input parameter uncertainties using Integrated Uncertainty Analysis package.RELAP calculated results are found to be in good agreement with those of TRACE and with those of experiments. The base case maximum FES temperature is 335.5 °C while that of 95% confidence 95th percentile is 407.41 °C for the first order Wilk's formula. The experimental measurements fall within the predicted band and the trends and sensitivities are similar to those reported for the TRACE code.

Uncertainty quantification for CO2 storage during intermittent CO2-EOR in oil reservoirs

CO2 enhanced oil recovery (CO2-EOR) process is considered the most feasible option for a secure storage of CO2 in underground formations. However, predicting amounts of CO2 stored and the oil recovered is associated with high risks due to data uncertainty. Sources of uncertainty lie in the challenges of accurately characterizing reservoir properties. Hence, the main objective of this study is to quantify the uncertainty of CO2 storage and oil recovery during intermittent CO2-assisted gravity drainage (CO2-GAGD) EOR process with a focus on key uncertain parameters including reservoir properties (permeability heterogeneity, porosity, and compressibility); fluid properties (CO2 diffusion coefficient and Henry's constant); and rock-fluid properties (gas-oil relative permeability data). Additionally, we aim to generate a rapid tool that reduces the computational expenses of the compositional reservoir simulation process by employing artificial neural network (ANN) as a machine learning (ML) tool. ANN was applied to generate predictive models that accurately predicted oil recovery and gas storage during the intermittent CO2-GAGD process. A total of 173 numerical samples were simulated and the objective functions were obtained. From the defined ranges of input variables and simulation results, cumulative distribution functions (CDFs) were obtained and uncertainty bounds, i.e., P10, P50, and P90, were estimated. Additionally, the generated ANN model showed excellent predictions of the objective functions with high correlation coefficients, higher than 0.98 in most of the cases, and with an absolute average percentage error (AAPE) range from 2.6 to 9%. Finally, the results of sensitivity analysis, conducted using the developed ML models with generating 10,000 realizations from the input and output parameters, showed the most influential input parameters on the performance of the CO2 storage and oil recovery factor. Reservoir porosity was found to have a significant impact on oil recovery and CO2 storage. A response surface analysis showed that the main independent input parameters contributing to the total uncertainty of oil recovery were horizontal permeability, oil relative permeability, and formation porosity. This study highlights the significant impact of uncertainties of input parameters on simulating the performance of CO2 storage and oil recovery in underground reservoirs.

1385. Pathway with single-dose long-acting intravenous antibiotic dalbavancin is a cost-saving alternative to usual inpatient care of acute bacterial skin and skin structure infections (ABSSSI)

Abstract Background Acute bacterial skin and skin structure infections (ABSSSI) are a common type of bacterial infection, with the cost of hospitalization being the main contributing factor to overall treatment costs. Two pragmatic clinical trials (Table 1) demonstrated that a new treatment pathway in which patients are treated with the intravenous (IV) long-acting antibiotic, dalbavancin, reduced hospital admission rates and length of stay (LOS) in hospitalized patients. The objective of this study is to evaluate the cost-effectiveness of a single dose of dalbavancin administered in the emergency department (ED) compared with IV antibiotics for appropriate patients who would otherwise be admitted to hospital and receive usual care such as vancomycin or daptomycin. Table 1.References of the Two Pragmatic Clinical Trials Methods A decision-analytic cost-effectiveness model was developed from the perspective of the US healthcare system. The population was ABSSSI patients presenting at the ED, who would be eligible to receive IV antibiotic infusion. Patients can receive IV treatment in the ED, and then discharged or followed by hospital admission for continued management (Fig. 1). A 14-day time horizon was used, representing the typical duration of ABSSSI management. Hospital admission rates and LOS were from the two clinical trials. Cost included ED visits, drug cost, inpatient stay, and physician visits. Input parameter uncertainty was examined via one-way and probabilistic sensitivity analyses. Figure 1.Decision Analytic Model Structure Results Providing dalbavancin in the ED resulted in $4,848 savings per patient (2021 USD). The drug cost of dalbavancin treatment was offset by a mean reduction of 4.24 days LOS per patient, which translated as $1,144 savings per hospitalization day avoided. One-way sensitivity analyses demonstrated that the key drivers were the cost of inpatient hospital stay and the LOS with usual care; however, none of the sensitivity analyses resulted in the new pathway being more costly (Fig. 2). Dalbavancin was cost saving in 100% of simulated scenarios. Figure 2.One-way Sensitivity Analysis Conclusion These results could help guide the management of ABSSSI by shifting care for appropriate patients from the inpatient to the outpatient setting where patients can be managed successfully, thereby freeing up hospital resources and reducing the total costs of ABSSSI treatment. Disclosures FRANK LOVECCHIO, DO, MPH, ABBVIE: Speaker|NIH: Grant/Research Support Alasdair D. Henry, PhD, AbbVie: Grant/Research Support Quan V. Doan, PharmD, AbbVie: Advisor/Consultant|AbbVie: Grant/Research Support|Allergan: Advisor/Consultant|Allergan: Grant/Research Support John L. Lock, PharmD, Abbvie: Employee|Abbvie: Stocks/Bonds Rosie D. Lyles, MD, MHA, MSc, AbbVie: AbbVie Employee|AbbVie: Stocks/Bonds Xiaolan Ye, PhD, AbbVie Inc.: AbbVie Employee|AbbVie Inc.: Stocks/Bonds.

Deterministic and probabilistic Seismic Hazard Assessments for Merangin Kerinci Sumatera

This paper presents calculated ground motions for Merangin Kerinci in the Provinsi of Jambi Sumatera. The ground motions were calculated using a probabilistic seismic hazard analysis. The earthquake catalogue used in the analysis included all recorded earthquakes up to the end of July 2019, trimmed for magnitude completeness. Uncertainty in input parameters was considered using a logic tree, with branches including shallow crustal faults of the Great Sumatran Faults and Sumatera Megathrust Subduction. Ground motions are presented for the local site conditions, measured at those sites which can be classified as Site Class D/C (stiff soil to Soft Rock/Very Dense Soil). Ground motions, including Peak Ground Acceleration (PGA), and spectral accelerations at periods ranging from 0.1 s to 10.0s were calculated the site. Data are presented for return periods of 175 years, 475 years, 2,500 years, 5,000 years, and 10,000 years for PSHA, and at mean, 50th, and 84th percentile confidence levels for DSHA. Compared to the Indonesian Seismic Hazard Map (ISHM) 2017 (at DSHA at 5% damping and 84th Percentile), the deterministic PGA values calculated in this study are relatively closed, which is accounted for 0.58g found in this study compared to the range of 0.6g – 0.7 in the ISHM 2017. On the other hand, the probabilistic PGA values for a particular of 5,000 years return period (SEE), the PGA values found in this assessment is 0.73g which is lower than the range of 0.8g – 0.9g in the ISHM 2017.

Batteries, fuel cells, or engines? A probabilistic economic and environmental assessment of electricity and electrofuels for heavy goods vehicles

Uncertainty surrounding the total cost of ownership, system costs, and life cycle environmental impacts means that stakeholders may lack the required information to evaluate the risks of transitioning to low-carbon fuels and powertrains. This paper assesses the life cycle costs and well-to-wheel environmental impacts of using electricity and electrofuels in Heavy Good Vehicles (HGVs) whilst considering input parameter uncertainty. The complex relationship between electricity cost, electrolyser capacity factor, CO 2 capture cost and electricity emissions intensity is assessed within a Monte Carlo based framework to identify scenarios where use of electricity or electrofuels in heavy goods vehicles makes economic and environmental sense. For vehicles with a range of less than 450 km, battery electric vehicles achieve the lowest total cost of ownership for an electricity cost less than 100 €/MWh. For vehicles that require a range of up to 900 km, hydrogen fuel cell vehicles represent the lowest long-term cost of abatement. Power-to-methane and power-to-liquid scenarios become economically competitive when low-cost electricity is available at high-capacity factors and CO 2 capture costs for fuel synthesis are below 100 €/tCO 2 ; these fuels may be more applicable to decarbonise shipping and aviation. Battery electric HGVs reduce greenhouse gas emissions by 50% compared to the diesel baseline with electricity emissions of 350 gCO 2 e/kWh. Electricity emissions less than 35 gCO 2 e/kWh are required for the power-to-methane and power-to-liquid scenarios to meet EU emissions savings criteria. High vehicle capital costs and a lack of widespread refuelling infrastructure may hinder initial uptake of low-carbon fuels and powertrains for HGVs.

Interior Heating of Rocky Exoplanets from Stellar Flares with Application to TRAPPIST-1

Many stars of different spectral types with planets in the habitable zone are known to emit flares. Until now, studies that address the long-term impact of stellar flares and associated coronal mass ejections (CMEs) assumed that the planet’s interior remains unaffected by interplanetary CMEs, only considering the effect of plasma/UV interactions on the atmosphere of planets. Here, we show that the magnetic flux carried by flare-associated CMEs results in planetary interior heating by ohmic dissipation and leads to a variety of interior–exterior interactions. We construct a physical model to study this effect and apply it to the TRAPPIST-1 star whose flaring activity has been constrained by Kepler observations. Our model is posed in a stochastic manner to account for uncertainty and variability in input parameters. Particularly for the innermost planets, our results suggest that the heat dissipated in the silicate mantle is both of sufficient magnitude and longevity to drive geological processes and hence facilitate volcanism and outgassing of the TRAPPIST-1 planets. Furthermore, our model predicts that Joule heating can further be enhanced for planets with an intrinsic magnetic field compared to those without. The associated volcanism and outgassing may continuously replenish the atmosphere and thereby mitigate the erosion of the atmosphere caused by the direct impact of flares and CMEs. To maintain consistency of atmospheric and geophysical models, the impact of stellar flares and CMEs on atmospheres of close-in exoplanetary systems needs to be studied in conjunction with the effect on planetary interiors.

Analysis of Hepatic Lipid Metabolism Model: Simulation and Non-Stationary Global Sensitivity Analysis

Lipid metabolism is a complex process and it is extremely helpful to simulate its performance with different models that explain all the biological processes that comprise it, which then enables its better understanding as well as understanding the kinetics of the process itself. Typically, kinetic parameters are obtained from a number of sources under specific experimental conditions, and they are a source of uncertainty. Sensitivity analysis is a useful technique for controlling the uncertainty of model parameters. It evaluates a model's dependence on its input variables. In this work, hepatic lipid metabolism was mathematically simulated and analyzed. Simulations of the model were performed using different initial plasma glucose (GB) and plasma triacylglyceride (TAG) concentrations according to proposed menus for different meals (breakfast, lunch, snack and dinner). A non-stationary Fourier amplitude sensitivity test (FAST) was applied to analyze the effect of 78 kinetic parameters on 24 metabolite concentrations and 45 reaction rates of the biological part of the hepatic lipid metabolism model at five time points (tf = 10, 50, 100, 250 and 500 min). This study examined the total influence of input parameter uncertainty on the variance of metabolic model predictions. The majority of the propagated uncertainty is due to the interactions of numerous factors rather than being linear from one parameter to one result. Obtained results showed differences in the model control regarding the different initial concentrations and also the changes in the model control over time. The aforementioned knowledge enables dietitians and physicians, working with patients who need to regulate fat metabolism due to illness and/or excessive body mass, to better understand the problem.