Uncertainty In Input Parameters Research Articles

Cost-effectiveness analysis of five anti-obesity medications from a US payer's perspective

Background and aimsTo determine the cost-effectiveness of anti-obesity medications (AOM): tirzepatide, semaglutide, liraglutide, phentermine plus topiramate (PpT), and naltrexone plus bupropion (NpB). Methods and resultsFrom a U.S. perspective we developed a Markov model to simulate weight change over a 40-year time horizon using results from clinical studies. According to the body mass index (BMI), cardiovascular diseases, diabetes and mortality risk were the health states considered in the model, being mutually exclusive. Costs of AOM, adverse events, cardiovascular events, and diabetes were included. We applied a 3% per-year discount rate and calculated the incremental cost-effectiveness ratios (ICERs) of cost per quality-adjusted life-year (QALY) gained. Probabilistic sensitivity analyses incorporated uncertainty in input parameters. A deterministic analysis was conducted to determine the robustness of the model. The model included a cohort of 78.2% females with a mean age of 45 years and BMI of 37.1 (SD 4.9) for females and 36.8 (SD 4.9) for males. NpB and PpT were the least costly medications and, all medications differed no more than 0.5 QALYs. Tirzepatide ICER was $355,616 per QALY. Liraglutide and semaglutide options were dominated by PpT. ConclusionCompared to other AOM, PpT was lowest cost treatment with nearly identical QALYs with other agents.

Uncertainty Assessment of WinSRFR Furrow Irrigation Simulation Model Using the GLUE Framework under Variability in Geometry Cross Section, Infiltration, and Roughness Parameters

Quantitatively analyzing models’ uncertainty is essential for agricultural models due to the effect of inputs parameters and processes on increasing models’ uncertainties. The main aim of the current study was to explore the influence of input parameter uncertainty on the output of the well-known surface irrigation software model of WinSRFR. The generalized likelihood uncertainty estimation (GLUE) framework was used to explicitly evaluate the uncertainty model of WinSRFR. The epistemic uncertainties of WinSRFR furrow irrigation simulations, including the advance front curve, flow depth hydrograph, and runoff hydrograph, were assessed in response to change key input parameters related to the Kostiakov–Lewis infiltration function, Manning’s roughness coefficient, and the geometry cross section. Three likelihood measures of Nash–Sutcliffe efficiency (NSE), percentage bias (PBIAS), and the coefficient of determination (R2) were used in GLUE analysis for selecting behavioral estimations of the model outputs. The uncertainty of the WinSRFR model was investigated under two furrow irrigation system conditions, closed end and open end. The results showed the likelihood measures considerably influence the width of uncertainty bounds. WinSRFR outputs have high uncertainty for cross section parameters relative to soil infiltration and roughness parameters. Parameters of soil infiltration and roughness coefficient play an important role in reducing the uncertainty bound width and number of observations, especially by filtering non-behavioral data using likelihood measures. The simulation errors of advance front curve and runoff hydrograph outputs with a PBIAS function were relatively lower and stable compared with other those of the likelihood functions. The 95% prediction uncertainties (95PPU) of the advance front curve were calculated to be 87.5% in both close-ended and open-ended conditions whereas, it was 91.18% for the runoff hydrograph in the open-ended condition. Additionally, the NSE likelihood function more explicitly determined the uncertainty related to flow depth hydrograph estimations. The outputs of the model showed more uncertainty and instability in response to variability in soil infiltration parameters than the roughness coefficient did. Therefore, applying accurate field methods and equipment and proper measurements of soil infiltration is recommended. The results highlight the importance of accurately monitoring and determining model input parameters to access a suitable level of WinSRFR uncertainty. In conclusion, considering and analyzing the uncertainty of input parameters of WinSRFR models is critical and could provide a reference to obtain realistic and stable furrow irrigation simulations.

Fixed-Time Adaptive Tracking Control for a Quadrotor Unmanned Aerial Vehicle with Input Saturation

Considering the problem of tracking control for a quadrotor unmanned aerial vehicle (QUAV) with input saturation, parameter uncertainties and external disturbances, a command filtered backstepping-based fixed-time adaptive control scheme was developed. The problem of “explosion of complexity” (EOC) is tackled by utilizing the fixed-time command filter, and the influence of filtered error is removed based on the fractional power-error-compensation mechanism. A fixed-time auxiliary system was designed to compensate for the input saturation of the QUAV. It strictly proves that the closed-loop system signals are fixed-time bounded, and the tracking errors converge to a sufficiently small region near the origin in a fixed time, and the convergence time is independent of the initial states. Finally, the effectiveness of the proposed fixed-time adaptive control algorithm is demonstrated via a numerical simulation.

Deterministic–stochastic modeling of transcranial magnetic stimulation featuring the use of method of moments and stochastic collocation

This work examines the influence of the human brain tissue parameters’ uncertainty and the stimulation coil positioning variations within the framework of transcranial magnetic stimulation (TMS). A combination of deterministic modeling and the stochastic collocation method (SCM) is used for the assessment of the input parameter uncertainties’ effects on several outputs of interest: the induced electric field and the related electric current density in the homogeneous human brain model, as well as the maximum values of electric field, magnetic flux density and the induced electric current density. The deterministic model features the formulation based on the surface integral equation approach whose numerical solution is carried out using the method of moments. The SCM shows satisfactory convergence in the assessment of the stochastic mean, variance and standard deviation for the output parameters of interest. Confidence intervals are computed, and the impact of input permittivity, conductivity and coil positioning is assessed. It is found that due to a non-symmetric nature of the utilized brain model the highest electric field variance is shifted from the expected location directly under the coil geometric center.

Urban Flood Modeling: Uncertainty Quantification and Physics‐Informed Gaussian Processes Regression Forecasting

AbstractEstimating uncertainty in flood model predictions is important for many applications, including risk assessment and flood forecasting. We focus on uncertainty in physics‐based urban flooding models. We consider the effects of the model's complexity and uncertainty in key input parameters. The effect of rainfall intensity on the uncertainty in water depth predictions is also studied. As a test study, we choose the Interconnected Channel and Pond Routing (ICPR) model of a part of the city of Minneapolis. The uncertainty in the ICPR model's predictions of the floodwater depth is quantified in terms of the ensemble variance using the multilevel Monte Carlo (MC) simulation method. Our results show that uncertainties in the studied domain are highly localized. Model simplifications, such as disregarding the groundwater flow, lead to overly confident predictions, that is, predictions that are both less accurate and uncertain than those of the more complex model. We find that for the same number of uncertain parameters, increasing the model resolution reduces uncertainty in the model predictions (and increases the MC method's computational cost). We employ the multilevel MC method to reduce the cost of estimating uncertainty in a high‐resolution ICPR model. Finally, we use the ensemble estimates of the mean and covariance of the flood depth for real‐time flood depth forecasting using the physics‐informed Gaussian process regression method. We show that even with few measurements, the proposed framework results in a more accurate forecast than that provided by the mean prediction of the ICPR model.

Adaptive Trajectory Tracking Control With Novel Heading Angle and Velocity Compensation for Autonomous Underwater Vehicles

In this paper, a control scheme is designed for the trajectory tracking problem of underactuated autonomous underwater vehicles with input saturation, parameter uncertainty, and disturbance. First, a novel continuous desired heading angle is designed, which is better than traditional tracking error-based desired angle design and excludes the discontinuity problem of the desired angle in traditional design. Second, based on the desired heading angle design, dynamic surface control is introduced, which reduces the computational complexity, and the desired speed can be obtained. A compensation filter is used to compensate for the loss caused by the input saturation of the control signal. Third, novel velocity compensation laws are designed to compensate the sway velocity, which can overcome the challenge of underactuation. Moreover, the parameter uncertainty issue, which widely exists in most engineering systems, can also be handled by the novel velocity compensation laws. In the simulation, the performance of the desired heading angle and velocity compensation law designed in this paper are compared, and simulation results verify the effectiveness of the method proposed in this paper.

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.