Uncertainty In Input Parameters Research Articles

Cost-effectiveness of mask mandates on subways to prevent SARS-CoV-2 transmission in the United States.

Community-based mask wearing has been shown to reduce the transmission of SARS-CoV-2. However, few studies have conducted an economic evaluation of mask mandates, specifically in public transportation settings. This study evaluated the cost-effectiveness of implementing mask mandates for subway passengers in the United States by evaluating its potential to reduce COVID-19 transmission during subway travel. We assessed the health impacts and costs of subway mask mandates compared to mask recommendations based on the number of infections that would occur during subway travel in the U.S. Using a combined box and Wells-Riley infection model, we estimated monthly infections, hospitalizations, and deaths averted under a mask mandate scenario as compared to a mask recommendation scenario. The analysis included costs of implementing mask mandates and COVID-19 treatment from a limited societal perspective. The cost-effectiveness (net cost per averted death) of mandates was estimated for three different periods based on dominant SARS-CoV-2 variants: Alpha, Beta, and Gamma (November 2020 to February 2021); Delta (July to October 2021); and early Omicron (January to March 2022). Compared with mask recommendations only, mask mandates were cost-effective across all periods, with costs per averted death less than a threshold of $11.4 million (ranging from cost-saving to $3 million per averted death). Additionally, mask mandates were more cost-effective during the early Omicron period than the other two periods and were cost saving in January 2022. Our findings showed that mandates remained cost-effective when accounting for uncertainties in input parameters (e.g., even if mandates only resulted in small increases in mask usage by subway ridership). The findings highlight the economic value of mask mandates on subways, particularly during high virus transmissibility periods, during the COVID-19 pandemic. This study may inform stakeholders on mask mandate decisions during future outbreaks of novel viral respiratory diseases.

Cost-Effectiveness Analysis of Pharmacogenomics-Guided Versus Standard Dosing of Warfarin in Patients with Mechanical Prosthetic Heart Valve

Background: Warfarin is the only approved anticoagulant for antithrombotic treatment in patients with mechanical prosthetic heart valves (MPHV). However, dosing warfarin is challenging due to its narrow therapeutic window and highly variable clinical outcomes. Both low and high doses of warfarin can lead to thrombotic and bleeding events, respectively, with these complications being more severe in individuals with sensitive genetic polymorphisms. Incorporating genetic testing could enhance the accuracy of warfarin dosing and minimize its adverse events. Objectives: This study aims to evaluate the utilities and cost-effectiveness of pharmacogenomics-guided versus standard dosing of warfarin in patients with MPHV in Iran. Methods: In this economic evaluation study, a cost-effectiveness analysis was conducted to compare pharmacogenomics-guided versus standard warfarin dosing. Data related to quality of life (QoL) were collected through a cross-sectional study involving 105 randomly selected MPHV patients using the EuroQol-5D (EQ-5D) Questionnaire. Costs were calculated with input from clinical experts and a review of relevant guidelines. Additional clinical data were extracted from published literature. The pharmacoeconomic threshold set for medical interventions within Iran's healthcare system was $1,500. A decision tree model was designed from the perspective of Iran's healthcare system with a one-year study horizon. Sensitivity analyses were also performed to assess the uncertainty of input parameters. Results: The utility scores derived from the questionnaire for standard and pharmacogenomics-guided warfarin treatments were 0.68 and 0.76, respectively. Genotype-guided dosing of warfarin was more costly compared to the standard dosing ($246 vs $69), and the calculated incremental cost-effectiveness ratio (ICER) was $2474 per quality-adjusted life year (QALY) gained. One-way sensitivity analyses showed that our model is sensitive to the percentage of time in the therapeutic range (PTTR), the cost of genetic tests, and the utility of both pharmacogenomics-guided and standard dosing arms. However, the probabilistic sensitivity analysis demonstrates the robustness of our model. Conclusions: Warfarin dosing with pharmacogenomics testing is currently not cost-effective. However, if the cost of genotyping tests decreases to $118, the ICER would become cost-effective.

A cost analysis of a simplified model for HCV screening and treatment at a tertiary hospital in Zimbabwe

ABSTRACT Background The treatment of chronic hepatitis C virus (HCV) infection using directly acting antivirals was recently adopted in the treatment guidelines of Zimbabwe. The objectives of this study were to design a simplified model of HCV care and estimate the cost of screening and treatment of hepatitis C infection at a tertiary hospital in Zimbabwe. Methods We developed a model of care for HCV using WHO 2018 guidelines for the treatment of HCV infection and expert opinion. We then performed a micro-costing to estimate the costs of implementing the model of care from the healthcare sector perspective. Deterministic and probabilistic sensitivity analyses were performed to explore the impact of uncertainty in input parameters on the estimated total cost of care. Results The total cost of screening and treatment was estimated to be US$2448 (SD=$290) per patient over a 12-week treatment duration using sofosbuvir/velpatasvir. The cost of directly acting antivirals contributed 57.5% to the total cost of care. The second largest cost driver was the cost of diagnosis, US$819, contributing 34.6% to the total cost of care. Conclusion Screening and treatment of HCV-infected individuals using directly acting antivirals at a tertiary hospital in Zimbabwe may require substantial financial resources.

Deep learning-driven interval uncertainty propagation for aeronautical structures

Interval Uncertainty Propagation (IUP) holds significant importance in quantifying uncertainties in structural outputs when confronted with interval input parameters. In the aviation field, the precise determination of probability models for input parameters of aeronautical structures entails substantial costs in both time and finances. As an alternative, the use of interval variables to describe input parameter uncertainty becomes a pragmatic approach. The complex task of solving the IUP for aeronautical structures, particularly in scenarios marked by pronounced nonlinearity and multiple outputs, necessitates innovative methodologies. This study introduces an efficient deep learning-driven approach to address the challenges associated with IUP. The proposed approach combines the Deep Neural Network (DNN) with intelligent optimization algorithms for dealing with the IUP in aeronautical structures. An inventive extremal value-oriented weighting technique is presented, assigning varying weights to different training samples within the loss function, thereby enhancing the computational accuracy of the DNN in predicting extremal values of structural outputs. Moreover, an adaptive framework is established to strategically balance the global exploration and local exploitation capabilities of the DNN, resulting in a predictive model that is both robust and accurate. To illustrate the effectiveness of the developed approach, various applications are explored, including a high-dimensional numerical example and two aeronautical structures. The obtained results highlight the high computational accuracy and efficiency achieved by the proposed approach, showcasing its potential for addressing complex IUP challenges in aeronautical engineering.

Is lithium from geothermal brines the sustainable solution for Li-ion batteries?

The rising demand for Li, paramount for energy storage, necessitates expanded supply. As the supply is concentrated in a few countries, this poses supply chain risks for Li-ion battery makers. To diversify suppliers, alternative Li ore deposits such as geothermal brines are being explored. However, Li extraction from geothermal brines is challenging due to the unique chemistry and elevated temperatures. Since Li-extraction from geothermal brines is in its infancy, data availability and quality are still poor, hampering life cycle assessments. Hence, this study provides a parametrized life cycle inventory model of Li carbonate production from geothermal brines. The model accounts for site-specific environmental conditions and technological features. Life cycle impacts at the Salton Sea in the US (1686 cases) and the Upper Rhine Graben in Germany (1982 cases) are quantified. The high case numbers are chosen to mitigate the high uncertainties in input parameters. Specifically, the brine chemistry, adsorption yield, drilling required and energy inputs are varied. Climate change impacts of selected cases vary within 18–59 kg CO2eq/kg Li carbonate at the Salton Sea and within 5.3–46 kg CO2eq/kg Li carbonate at the Upper Rhine Graben, compared to 2.1–11 kg CO2eq/kg Li carbonate in existing ecoinvent data sets. The wide range of potential impacts underscore the necessity of early-stage assessments of the technologies. In case of high drilling demand and use of fossil energy, climate change impacts of Li-ion batteries using Li carbonate from geothermal brines can increase by 30–41 % compared to literature values.

Robust topology optimization for transient dynamic response minimization

This paper presents a novel framework for topology optimization (TO) of structures subjected to uncertain transient loading. Random transient uncertain but bounded loading is modeled in the form of an ellipsoid convex model. A transformation matrix is defined with uncertainty in input parameters based on orientation and lengths of semi-major and semi-minor axes of the ellipsoid. Latin hypercube sampling is utilized to generate samples inside the normalized space. These samples are then converted to a physical space using a transformation matrix. The generalized-α method is employed to compute the dynamic response of the system subjected to time-dependent loading. A density-based TO formulation is used to minimize dynamic response. The robust topology optimization problem is defined as bi-criteria optimization to minimize both the mean and variance across all deterministic cases within the ellipsoidal bounds. The solid isotropic material with penalization method is employed for the interpolation of material stiffness. Helmholtz filter is used to stabilize the problem numerically. The optimality criteria method is used to update the design variables. The proposed scheme’s effectiveness and the efficiency of the optimized designs are checked using a set of numerical examples. For most of the cases, robust design exhibits a substantial reduction of approximately 70%–75% in standard deviation when compared with deterministic design. The results demonstrate the proposed framework’s ability to reduce structure transient response under uncertain but bounded regions.

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

Risk Analysis of Wellbore Instability of “Felt Layer” in Qiudong Sag Based on Monte Carlo Method

Both domestic and international scholars have conducted in-depth research on wellbore stability issues. They have established various empirical models, analytical models, and numerical simulation methods. However, there is relatively little research on the impact of the uncertainty of input parameters on wellbore stability, and the understanding of this aspect remains unclear. To address this, this paper introduces a probability distribution method. It is based on a wellbore stability mechanical analytical model and, using reliability theory, establishes a method for evaluating wellbore instability risks. By employing the Monte Carlo random simulation method, this study investigates the sensitivity of input parameters to wellbore stability, clarifying that ground stress is the main controlling factor affecting wellbore stability. Combining the analysis of the “felt layer” ground stress profile, this study utilizes two-dimensional simulation experiments to validate the accurate determination of ground stress magnitude in wellbore stability analysis. It also examines the impact of reducing its uncertainty. The results show that this approach significantly reduces the risk of wellbore instability, addressing the challenging issue of identifying wellbore instability in the Qiu Dong Depression’s “felt layer” within the TH Basin.

Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants

Uncertainty quantification and sensitivity analysis are essential for improving the modeling and estimation of greenhouse gas emissions in livestock farming to evaluate and reduce the impact of uncertainty in input parameters to model output. The present study is a comprehensive review of the sources of uncertainty and techniques used in uncertainty analysis, quantification, and sensitivity analysis. The search process involved rigorous selection criteria and articles retrieved from the Science Direct, Google Scholar, and Scopus databases and exported to RAYYAN for further screening. This review found that identifying the sources of uncertainty, implementing quantifying uncertainty, and analyzing sensitivity are of utmost importance in accurately estimating greenhouse gas emissions. This study proposes the development of an EcoPrecision framework for enhanced precision livestock farming, and estimation of emissions, to address the uncertainties in greenhouse gas emissions and climate change mitigation.

Single Intravenous Dose Dalbavancin Pathway for the Treatment of Acute Bacterial Skin and Skin Structure Infections: Considerations for Emergency Department Implementation and Cost Savings

BackgroundA pathway for the treatment of acute bacterial skin and skin structure infections (ABSSSI) with a single intravenous (IV) dose of dalbavancin was previously shown to reduce hospital admissions and shorten inpatient length of stay (LOS). ObjectivesTo describe pathway implementation at the emergency department (ED) and evaluate cost-effectiveness of a single-dose dalbavancin administered to ED patients who would otherwise be hospitalized to receive usual care with multidose IV antibiotics. MethodsThe dalbavancin pathway was previously implemented at 11 U.S. EDs (doi:10.1111/acem.14258). Patients with ABSSSI, without an unstable comorbidity or infection complication requiring complex management, were treated with a single dose of dalbavancin. At the emergency physicians’ discretion, patients were either discharged and received outpatient follow-up or were hospitalized for continued management. A decision analytic cost-effectiveness model was developed from the U.S. healthcare's perspective to evaluate costs associated with the dalbavancin pathway compared with inpatient usual care. Costs (2021 USD) were modeled over a 14-day horizon and included ED visits, drug costs, inpatient stay, and physician visits. One-way and probabilistic sensitivity analyses examined input parameter uncertainty. ResultsDriven largely by the per diem inpatient cost and LOS for usual care, the dalbavancin pathway was associated with savings of $5133.20 per patient and $1211.57 per hospitalization day avoided, compared with inpatient usual care. The results remained robust in sensitivity and scenario analyses. ConclusionThe new single-dose dalbavancin ED pathway for ABSSSI treatment, which was previously implemented at 11 U.S. EDs, offers robust cost savings compared to inpatient usual care.

Risk Assessment Method for Analyzing Borehole Instability Considering Formation Heterogeneity

In the study of borehole instability, the majority of input parameters often rely on the average values that are treated as fixed values. However, in practical engineering scenarios, these input parameters are often accompanied by a high degree of uncertainty. To address this limitation, this paper establishes a borehole stability model considering the uncertainty of input parameters, adopts the Monte Carlo method to calculate the borehole stability reliability at different drilling fluid densities, evaluates the sensitivity of borehole instability to a single parameter, and studies the safe drilling fluid density window at different borehole stability reliability values under multi-parameter uncertainties. The results show that the uncertainty of rock cohesion has a great influence on the fracture pressure of the vertical and horizontal wells. The minimum horizontal stress has the greatest influence on the fracture pressure of the vertical and horizontal wells, followed by pore pressure. In the analysis of borehole stability, the accuracy of cohesion and minimum horizontal stress parameters should be improved. In scenarios involving multiple parameter uncertainties, while the overall trend of the analysis results remains consistent with the conventional borehole stability outcomes, there is a noteworthy narrowing of the safe drilling fluid density window. This suggests that relying on conventional borehole stability analysis methods for designing the safe drilling fluid density window can considerably increase the risks of borehole instability. Uncertainty assessment is crucial to determine the uncertainties associated with the minimum required mud pressure, thereby ensuring more informed decision-making during drilling operations. To meet practical application demands, structure and boundary condition uncertainties should be implemented for a more comprehensive assessment of borehole stability.

Simple model combined with data assimilation to forecast buoyancy-driven ceiling jet propagation in tunnel fires

Faster-than-real-time forecast for the ceiling jet head propagation is crucial for evacuation guidance and rescue operation in the early stages of tunnel fires. The ceiling jet head propagation is driven by buoyancy and thus significantly dominated by the heat transfer process in the head region. Simplified models have been established but proved challenging in forecasting complex dynamic processes. The cumulative deviations induced by the model limitations and the input parameter uncertainties lead to distorted forecast during the ceiling jet dynamic propagation process, thereby constraining the applicability of such models in practical scenarios. In this study, an assimilation method, the ensemble Kalman filter (EnKF), was combined with a simple theoretical model to forecast the ceiling jet propagation. Using the EnKF to assimilate the monitored local data, the key input parameters such as mass flow rate m˙ and the temperature of the ceiling jet head Ts are corrected in real time, thus alleviating model prediction deviation. Meanwhile, a heat loss rate correction coefficient λα, introduced to correct the heat loss rate during the ceiling jet propagation process, is dynamically estimated as well. This enables the model to characterize the heat transfer process more accurately, consequently ensuring faster-than-real-time forecasts of the subsequent propagation stage. Finally, the forecasting effectiveness of the proposed combined method was examined in a 1:6 reduced-size experimental tunnel fire scenario and full-scale simulated tunnel fire scenarios with various fire source locations, tunnel geometries, and suddenly changing HRR. In response to the demand of minimizing both the type and quantity of sensors in practical applications, a factor analysis was also performed to examine the robustness of this method. The results exhibited good adaptability to different fire scenarios with very limited monitored data.

A comprehensive review and sensitivity analysis of the factors affecting the performance of buildings equipped with Variable Refrigerant Flow system in Middle East climates

Variable Refrigerant Flow (VRF) systems are becoming increasingly popular in commercial and residential buildings due to their flexibility and efficiency. Building design and operational parameters play an important role in the process of predicting cooling loads, and these variables contain a variety of uncertainties that must be taken into account when seeking to obtain a reliable prediction. It is common practice to oversize the Heating, Ventilation, and Air Conditioning (HVAC) system installed in a building in order to reduce uncertainties in its performance. However, oversizing is undesirable due to additional capital and operating costs, inefficient space utilization, and excessive emissions of greenhouse gases. This study provided a comprehensive literature review on previous uncertainly analysis studies from six perspectives: classification of uncertainty factors, sensitivity analysis methods, building energy model for uncertainty analysis, sampling method, the most effective parameter, and distribution of uncertainty parameters., and then developed a framework to investigate the effects of uncertainty of operational and design parameters on annual cooling load, total energy consumption, HVAC electricity use per conditioned floor area, VRF system cost, and the cooling-to-total electricity consumption ratio (R) in residential buildings equipped with VRF systems located in the Middle East (ME) region (Kuwait City, Tehran, and Istanbul). Artificial Neural Network (ANN) model was also developed to predict output parameters in residential building. Latin Hypercube Sampling (LHS) simulation was also applied to generate near-random samples of uncertainty parameter values from a multidimensional distribution. It should be mentioned that a correlation was derived in this study to predict VRF cost. The study evaluated thirteen uncertainty parameters related to building characteristics, occupants, building energy systems, VRF variables, and lighting conditions. Several methods were used to evaluate the effects of input uncertainty parameters on the output variables, namely, the Standardized Regression Coefficient (SRC), Partial Correlation Coefficient (PCC), and Spearman Rank Correlation Coefficient (SRCC) or Pearson Product-Moment Correlation Coefficient (PPMCC). The results indicated that VRF cost and HVAC electricity use per area have a greater coefficient of variation compared with other output parameters. The density distribution of output parameters indicated that uncertainty parameters had the greatest impact on output parameters in Kuwait (hyper arid climate), whereas in Istanbul (humid subtropical climate), they were the least. Among the variables examined in the Sensitivity Analysis (SA), cooling setpoint had the greatest impact on residential building energy consumption in ME climates. Finally, this paper highlights emerging trends and offers recommendations for advancing future research in the realm of building energy uncertainty analysis.

Designing a resilient medical laboratory service network under disruption and uncertainty: A case study

Health service networks play a key role in ensuring the health of society. Disruptive and operational risks are constantly affecting the goals of healthcare supply chain and service networks. Therefore, adopting a suitable approach to deal with risks is critical in the mentioned networks. This study proposes a mathematical model for designing an integrated resilient medical laboratory service network and the supply chain of laboratory consumables under multiple facility disruptions to minimize the total network design costs. A robust possibilistic optimization approach is employed to deal with the uncertainty of input parameters. Then, a case study is presented to extract the results and evaluate the proposed model. Regarding the results, the integration of the laboratory service network and laboratory consumables supply chain significantly reduces costs and improves the service process in the long run. Also, adopting more efficient resiliency strategies will increase the cost savings of the entire network.

The inclusion of uncertainty in circularity transition modeling: A case study on wind turbine blade end-of-life management

The transition to a more circular economy (CE) is complex and hard to predict, including in sustainable energy technologies. Many sources of uncertainty make it challenging to model CE scenarios and their potential benefits. As an example, the high variability in costs and revenues of different recycling options makes future wind turbine blade recycling highly uncertain. To better understand this challenge, the circular economy, life cycle assessment and visualization (CELAVI) framework – a discrete event simulation and life cycle assessment framework – is modified to incorporate uncertainty analysis capabilities. Moreover, a 3-step procedure that covers different aspects of uncertainty in CE studies and includes a Monte-Carlo analysis is proposed. The procedure is tested in a case study on wind turbine blade recycling using CELAVI. Results highlight that grinding and landfilling costs are the most influential parameters for wind power circularity. The model’s output coefficients of variation (when input parameter uncertainties are propagated) are between 92% and 384% depending on the indicator. The approach developed in this study may help researchers and decision-makers who study circularity prioritize their data collection effort. Finally, our method contributes to a mounting yet critical body of research: the measurement of uncertainty in circularity transitions.

Estimating the effectiveness of electric vehicles braking when determining the circumstances of a traffic accident

In the vast majority of cases, the braking process is used to prevent traffic accidents. The effectiveness of this process depends on the design and functionality of vehicle braking systems (presence of anti-lock braking system, emergency braking system, preventive safety systems, etc.) and is limited by the amount of frictional forces in contact of tires with the road. The improvement of methodical approaches to evaluating the effectiveness of braking of cars contributes to increasing the accuracy and objectivity of establishing the circumstances of the occurrence of emergency situations. The paper analyses existing methods of evaluating the braking parameters of vehicles (including those with an electric drive) and modern methods of evaluating electric vehicle braking parameters and conducting auto-technical investigations of traffic accidents, which relate to using different methodological approaches and digital technologies at all stages of expert research. In contrast to existing models, the proposed mathematical model for estimating the trajectory of two-axle cars during braking allows for considering various types of input parameter uncertainty, reducing the range of possible modeling errors by 39%. Comparing simulation results and experimental data showed that the average relative error is 4.58%, and the maximum error did not exceed 7.82%. The performed study of the stability of the electric vehicles' movement during emergency braking with the help of developed mathematical models in the Mathcad software environment reveals the content of the algorithm of a similar calculation in specialized computer programs of auto technical examination. Conducting such calculations is relevant in the analysis of real accident situations, where specific circumstances and features that cannot be considered during modeling in specialized software must be taken into account. Simultaneously, the probability of type I errors is reduced by 2–19%, and type II errors are reduced by 43–68%.

Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations

An understanding of how input parameter uncertainty in the numerical simulation of physical models leads to simulation output uncertainty is a challenging task. Common methods for quantifying output uncertainty, such as performing a grid or random search over the model input space, are computationally intractable for a large number of input parameters represented by a high-dimensional input space. It is, therefore, generally unclear as to whether a numerical simulation can reproduce a particular outcome (e.g., a set of experimental results) with a plausible set of model input parameters. Here, we present a method for efficiently searching the input space using Bayesian optimization to minimize the difference between the simulation output and a set of experimental results. Our method allows explicit evaluation of the probability that the simulation can reproduce the measured experimental results in the region of input space defined by the uncertainty in each input parameter. We apply this method to the simulation of charge-carrier dynamics in the perovskite semiconductor methyl-ammonium lead iodide (MAPbI3), which has attracted attention as a light harvesting material in solar cells. From our analysis, we conclude that the formation of large polarons, quasiparticles created by the coupling of excess electrons or holes with ionic vibrations, cannot explain the experimentally observed temperature dependence of electron mobility.

Integrated supply chain network design and superstructure optimization problem: A case study of microalgae biofuel supply chain

In recent years, several strategies have been developed and adopted in a bid to manage the biofuel supply chain. In this paper, a two-stage optimization model is proposed for integrated microalgae biofuel supply chain network design and superstructure optimization problems. In the first stage, the design of the carbon capture, utilization, and storage (CCUS) network is taken into account. A robust mixed integer linear programming (RMILP) model is proposed to optimize the strategic CCUS decisions, including the simultaneous selection of emission sources, capture facilitates, Carbon Dioxide (CO2) pipelines, intermediate transportation sites and storage sites, or microalgae cultivation sites. The second stage is dedicated to biorefinery superstructure optimization in order to determine the optimal/promising biorefinery configurations. The presented model is able to handle the inherent uncertainty of critical input parameters. Moreover, the results show that biodiesel production cost cannot compete with current diesel price, but it can be reduced significantly by improving biomass productivity.

Cost-effectiveness analysis of leadless cardiac resynchronization therapy.

The Wireless Stimulation Endocardially for CRT (WiSE-CRT) system is a novel technology used to treat patients with dyssynchronous heart failure (HF) by providing leadless cardiac resynchronization therapy (CRT). Observational studies have demonstrated its safety and efficacy profile, however, the treatment cost-effectiveness has not previously been examined. A cost-effectiveness evaluation of the WiSE-CRT System was performed using a cohort-based economic model adopting a "proportion in state" structure. In addition to the primary analysis, scenario analyses and sensitivity analyses were performed to test for uncertainty in input parameters. Outcomes were quantified in terms of quality-adjusted life year (QALY) differences. The primary analysis demonstrated that treatment with the WiSE-CRT system is likely to be cost-effective over a lifetime horizon at a QALY reimbursement threshold of £20 000, with a net monetary benefit (NMB) of £3781 per QALY. Cost-effectiveness declines at time horizons shorter than 10 years. Sensitivity analyses demonstrated that average system battery life had the largest impact on potential cost-effectiveness. Within the model limitations, these findings support the use of WiSE-CRT in indicated patients from an economic standpoint. However, improving battery technology should be prioritized to maximize cost-effectiveness in times when health services are under significant financial pressures.

Modelling of spent fuel dissolution process for assessment of criticality safety margin

Criticality safety margin assessment is a challenging task for a large dissolution tank under uncertain process conditions. The Monte-Carlo based neutron transport code (MCkeff) was used to model geometry, material and process conditions of dissolution tank containing fissile material. This manuscript presents a sampling-based probabilistic technique for estimating single-sided tolerance limit on criticality safety parameter due to random variation of process parameters. Based on their effect on fissile mass, fissile concentration, neutron moderation and neutron reflection, potential uncertain parameters impacting the criticality safety margin were identified. By withdrawing random samples from uncertain domain of process parameter, a simulation test matrix was formed to study the variability of the neutron multiplication factor. The Wilk's formula, which corresponds to the 95th percentile and 95% confidence level, was used to estimate required sample size to minimise statistical error (i.e., Type-I error associated to the rejection of the genuine sample and Type-II error connected to the acceptance of the false sample). In order to cut down the computation costs associated with requirement of large computer runs, a meta-model for the criticality safety problem was also constructed using a linear regression model. Additionally, associated model uncertainty was quantified. The variation in neutron multiplication factor caused by changes in process variables like pellet diameter, linear pellet mass, temperature, etc. was also investigated as an input parameter uncertainty. It is found that model uncertainty plays significant role while assessing the criticality safety margin using meta-model.