Uncertainty In Input Parameters Research Articles

Adaptive state-feedback control for nonlinear systems with input delay and state constraints

This paper investigates the adaptive tracking control for more general nonlinear systems that encompass input delay, state constraints, and parameter uncertainty. Firstly, the parametric nonlinearities of the system are addressed by adaptive control and parameter separation technique. By integrating the Pade approximation method with the barrier Lyapunov function in a unified framework, the uncertainties arising from input delay and state constraints are effectively tackled. Then, an adaptive state-feedback control strategy is constructed based on the backstepping design procedure and rigorous stability analysis. It is verified that all the signals in the closed-loop system are uniformly ultimately bounded, the tracking error of the system converges to a compact set of the origin, and the system state constraints are never violated. Finally, the designed controller is applied to a single-link robot system to demonstrate the effectiveness of the proposed control strategy.

Water and carbon fluxes from a supra-permafrost aquifer to a stream across hydrologic states

Supra-permafrost aquifers within the active layer are present in the Arctic during summer. Permafrost thawing due to Arctic warming can liberate previously frozen particulate organic matter (POM) in soils to leach into groundwater as dissolved organic carbon (DOC). DOC transport from groundwater to surface water is poorly understood because of the unquantified variability in subsurface properties and hydrological environments. These dynamics must be better characterized because DOC transport to surface waters is critical to predict the long-term fate of recently thawed carbon in permafrost environments. Here, we used distributed Darcy’s Law calculations to quantify groundwater and DOC fluxes into Imnavait Creek, Alaska, a representative headwater stream in a continuous permafrost watershed. We developed a statistical ensemble approach to model the parameter variability and range of potential contributions of steady-state groundwater flow to the creek. We quantified the model prediction uncertainty using statistical sampling of in-situ, active-layer soil hydro-stratigraphy (water table, ice table, and soil stratigraphy), high-resolution topography data, and DOC data. Moreover, the predicted groundwater discharge values representing all possible hydrologic conditions towards the end of the thawing season were also considered given the potential variability in saturation. The model predictions were similar to and span most of the observed range of Imnavait Creek streamflow, especially during recession periods, and also during saturation excess overland flow. As the Arctic warms and supra-permafrost aquifers deepen, groundwater flow is expected to increase. This increase is expected to impact stream, river, and lake biogeochemical processes by dissolving and mobilizing more soil constituents in continuous permafrost regions. This study highlights how quantifying the uncertainty of hydro-stratigraphical input parameters helps understand and predict supra-permafrost aquifer dynamics and connectivity to aquatic systems using a simple, but scalable, modeling approach.

Uncertainty Propagation of Monte Carlo Cross-Section Generation for Graphite-Moderated Pebble Bed Reactors

Monte Carlo codes have become increasingly popular for generating homogenized few-group cross-section data, especially for advanced reactor designs that have complex geometries and nontraditional compositions. However, the stochastic nature of Monte Carlo processes has the potential to introduce additional statistical uncertainties in the overall uncertainty in the prediction of core behavior. The work performed in this research quantified the additional uncertainty introduced by the use of Monte Carlo multigroup cross sections into the analysis of graphite-moderated pebble bed reactors. In this research, the objective was achieved by performing uncertainty quantification for the key output parameters in deterministic steady-state and transient safety calculations. The results show that when the homogenized multigroup cross sections are generated with a sufficient number of neutron histories in the Monte Carlo calculation, the uncertainties in the subsequent deterministic simulations caused by the Monte Carlo cross-section uncertainty are negligible compared to the contributions from the uncertainties of other input parameters.

A probabilistic approach for the levelized cost of energy of floating offshore wind farms

This paper aims to analyze the levelized cost of energy (LCOE) of floating offshore wind farms from a probabilistic point of view. Understanding and addressing the uncertainty associated with the main parameters that influence the wind energy generated by a wind farm during its lifetime is crucial for the economic evaluation of offshore wind energy in the broader energy landscape. The methodology for probabilistic assessment of LCOE is introduced, and the uncertainty in input parameters are discussed. In a base case study, an assumed Floating Offshore Wind Farm (FOWT) consisting of 250 5-MW wind turbines is considered. The use of bias and randomness in key random variables is discussed and studied in detail. Results indicate that LCOE estimates of 15 EURc/kWh for offshore wind turbines are achievable with reasonable confidence, while estimates of 5 EURc/kWh require careful consideration of uncertainty in the wind farm’s parameters. The feasibility analysis showed that techno-economic parameters are more influenced by wind characteristics and efficient use of wind turbines than by the cost of the wind farm. This paper provides general guidance on how to carry out early-stage techno-economic analysis of FOWFs.

An artificial neural network-based predictive model for tensile behavior estimation under uncertainty for fused deposition modeling

PurposeThis paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.Design/methodology/approachThe prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.FindingsThe results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.Originality/valueUsing the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

Sensitivity analysis and fuel performance of a control rod withdrawal in the generic fluoride-salt-cooled high temperature reactor

The purpose of this study is to develop boundary conditions with respect to input parameter uncertainty for fuel performance assessment of a control rod withdrawal transient in a fluoride-salt-cooled high-temperature reactor (FHR). Sensitivity analysis was performed to determine how certain key input parameters impacted the uncertainty of the outputs needed for the boundary conditions. The generated boundary conditions can be used in fuel performance codes to obtain failure probabilities and fuel performance envelopes. To accomplish this, we utilized a KP-AGREE model of the generic fluoride-salt-cooled high-temperature reactor (gFHR) and a pebble bed benchmark developed by Kairos Power. The key boundary conditions that were developed included the total power, maximum fuel region temperature, maximum fuel kernel temperature, and maximum moderator (graphite) temperature within the core. The sensitivity analysis was run until the Sobol indices and output converged within a 95 % confidence level; it was found that the coolant inlet temperature consistently had the highest impact on the uncertainty of the key core temperature values. The maximum bound of these temperatures also did not exceed 1123.5 °C for the fuel kernel and 973.7 °C for the moderator after the transient, demonstrating that there is a large margin between the predicted fuel and moderator temperature and accepted safety limits, given a 1650 °C limit for Tristructural Isotropic (TRISO) fuel. These boundary conditions were implemented into a BISON model to demonstrate their use in fuel performance and obtain a brief failure profile of the fuel. The maximum magnitude of the hoop stress on the SiC layer was −12.02 MPa, resulting in a failure probability of 4.90·10−19. Thus, the fuel performance simulations also emphasized the robustness of the TRISO fuel design utilized in the gFHR, as the predictions reveal substantial margin of the TRISO particles relative to temperature limits during the control rod withdrawal. The limiting temperature is expected to be the reactor vessel temperature and the fuel is not expected to be challenged at all by mechanical failure of the SiC layer during these events.

Metaheuristic algorithms for calibration of two-dimensional wildfire spread prediction model

Wildfires are complex phenomena with harmful consequences, ranging from environmental and property destruction to loss of human lives. In this sense, predicting wildfire behaviour is essential to mitigate its impacts and consequences. The Rothermel model is the most used fire rate of spread prediction model. However, input parameter uncertainty is a significant source of prediction error. In this paper, we propose the calibration of the input parameters of the fire propagation model by metaheuristic algorithms under a two-stage framework. The fire spread model consists on the Rothermel model in a two-dimensional approach for surface fires. The proposed calibration is performed in two stages iteratively repeated over time: (i) the calibration of the fire spread model’s input parameters and (ii) the wildfire spread prediction using the calibrated input parameters. The calibration was performed by the genetic algorithm, differential evolution, and simulated annealing, which calibrates the surface-area-to-volume ratio, fuel bed depth, live fuel moisture and dead fuel moisture. The symmetric difference between the real and predicted fire map shapes was defined as the fitness function of all three metaheuristic algorithms. For validation, simulations were done on two prescribed fires. The results for the real and estimated fire behaviour were then compared and revealed that all the tested metaheuristic algorithms produce a better fit to the real fire’s perimeter when compared to the uncalibrated Rothermel model. From the results, differential evolution provided the majority of best results when compared to genetic algorithm and simulated annealing algorithms in each scenario.

An advanced Polynomial Chaos Expansion method for sensitivity analysis of aero-engine fuel gear pumps

Global sensitivity analysis plays an important role in robustness optimization design of aero-engine fuel gear pumps to investigate how input parameters uncertainties contribute to performances uncertainties. In this paper, inspired by the Leave-One-Out method, an advanced Polynomial Chaos Expansion (PCE) method equipped with Adaptive design of both training Points and polynomial Order, denoted as APO-PCE, is proposed to efficiently estimate the variance-based sensitivity indices. A novel active learning strategy is developed for identifying the optimal candidate sample point to adaptively design training points and simultaneously update the polynomial order. Input parameters, including rotational speed, inlet pressure, outlet pressure, and gear tip clearance, are reasonably characterized as nine convenient sampling Gaussian variables by probabilistic modeling and Karhuben–Loève expansion. The results show that the proposed APO-PCE method is superior to the classical PCE method, and the uncertainties of rotational speed, inlet pressure, and outlet pressure are the main factors for the uncertainties of supply performances and flow field characteristics, while the gear tip clearance uncertainty only affects the flow loss at lower rotational speed.

Stability Analysis of Gravity Dam using Matlab Programming

Gravity dams are critical structures in civil engineering, designed to withstand the immense forces exerted by water pressure and gravity. Ensuring their stability is paramount to prevent catastrophic failures that could result in significant economic and environmental consequences. This study presents a comprehensive stability analysis of a gravity dam using MATLAB programming. The analysis focuses on evaluating the structural integrity and safety factors of the dam under various loading conditions, including hydrostatic pressure and seismic forces. MATLAB's computational capabilities are utilized to perform rigorous numerical simulations, incorporating finite element analysis (FEA) and stability criteria assessments. Key aspects of the stability analysis include the determination of critical failure modes such as sliding, overturning, and base pressure distribution. Through MATLAB programming, the study explores different dam geometries and material properties to assess their influence on overall stability. Furthermore, sensitivity analyses are conducted to investigate the impact of uncertainties in input parameters on the dam's stability margins. This provides insights into potential weak points and allows for optimization of design parameters to enhance dam safety.

Simulation of pulse-like ground motions with directionality effect for the 2001 Mw 7.6 Bhuj, India earthquake and sensitivity analysis of uncertain model input parameters

Ground motions exhibiting pulse-like (PL) features present a distinct and significant threat to the structural integrity of constructed environments, due to intense pulse, large amplitude, and rapid energy release. The lack of recorded PL ground motion data in the Indian region presents a significant challenge for accurate seismic risk assessment of the built environment. In the absence of recorded PL ground motions, the generation of synthetic PL ground motions can provide earthquake acceleration time histories for seismic response analysis of structures and also to evaluate ground motion prediction equations (GMPEs). Within this framework, the modified stochastic finite-fault approach based on the dynamic corner frequency is used to generate the synthetic ground motions for the 2001 Gujarat earthquake. These artificially generated ground motions are validated using available peak ground acceleration data from thirteen stations. After validation, the ground motions are generated at locations equidistant from the fault in all four directions (i.e., east, west, north, south). The expected acceleration time histories at specific sites, the spatial distribution of ground motion characteristics, and the effect of site non-linearity are investigated. Based on the geographical positioning of sites for the 2001 event, it was observed that 65.15 % of ground motions on the western side, relative to the epicenter, exhibited PL characteristics, whereas 23.64 % of ground motions on the eastern side were observed to have PL properties. The effect of uncertainty in the model input parameters on the simulated ground motions is examined by performing a sensitivity analysis. The reliability and validity of the simulated ground motions are assessed by deriving an empirical equation and compared with the results obtained from available GMPEs for the region.

Resilient and sustainable semiconductor supply chain network design under trade credit and uncertainty of supply and demand

Supply Chain (SC) performance appraisal extends beyond economic evaluation to encompass environmental and social impacts, as well as resilience to supply and demand disruptions. Thus, SC network design decisions should simultaneously optimize all these performance metrics as a primary objective. However, existing studies optimized these objectives independently or in a limited scope, failing to comprehensively address the interconnected nature of these performance metrics. Therefore, this study proposes a mixed integer linear programming (MILP) model with four objectives to address a multi-tier resilient and sustainable semiconductor SC network design problem under trade credit, supply, and demand uncertainties. The model aims to determine the optimal number and location of various facilities, the amount to order from suppliers, economic production quantities, quantity allocation between SC entities, and ideal supplier selections. The proposed supply chain network contributes to the global effort to reduce carbon emissions and enhance the circular economy by considering carbon trading, recycling, and the proper disposal of end-of life products. Uncertainties in input parameters are addressed using a fuzzy programming approach, while lexicographic optimization and the ɛ-constraint method are used to solve the proposed model. This paper presents a numerical example to illustrate the applicability of the proposed model and provide managerial insights. The numerical analysis results show that provided an appropriate confidence level for the uncertain parameters, the sets of Pareto optimal solutions can be generated. And it also shows that the total SC cost increases with decreasing carbon emissions level and increasing job opportunities created. This research advances sustainable and resilient supply chain management practices while offering practical guidance for decision-makers in real-world contexts.

Seismic performance of sheet pile walls in liquefiable soil using centrifuge tests and an assessment of nonlinear effective stress analysis using PM4Sand

Past earthquakes have revealed that damage to sheet-pile walls under saturated conditions is closely linked to excess pore water pressure buildup in the surrounding soil. Nonlinear effective stress analysis (ESA) is commonly employed to assess the seismic performance of sheet-pile walls in liquefiable soils, incorporating constitutive models for liquefaction simulation. However, ESA results are sensitive to uncertainties in input parameters, model calibration, and modeling techniques. Dynamic centrifuge tests conducted in the Liquefaction Experiments and Analysis Project (LEAP) offer valuable insights into important response mechanisms and validate ESA. Seven centrifuge tests on a cantilevered sheet-pile wall model showed that liquefaction did not occur in the backfill near the wall due to net seaward wall displacement but did occur farther away. In addition, the mechanism of wall displacement was mainly due to the shear deformation of the softened backfill, with the displacement magnitude depending on the relative density of soil, peak ground acceleration of base motion, and wall displacement during gravity loading. Nonlinear ESA was performed for three centrifuge tests using FLAC2D and the PM4Sand constitutive model for soil. Gravity analysis captured static wall displacement and initial stress distribution in the soil. Two calibrations of the PM4Sand model were pursued at the element level: C1 calibration for liquefaction strength and C2 calibration for liquefaction strength and the post-liquefaction shear strain accumulation rate. System-level simulations showed similar liquefaction behavior as observed in the tests for both calibrations. However, the C2 calibration provided closer predictions of wall displacements, while the C1 calibration (default for PM4Sand) resulted in larger and more conservative displacements. Overall, the PM4Sand model performed well with minimal calibration, making it suitable for nonlinear ESA of sheet-pile walls.

Can the allometric relationship between saturated water accumulation and dry mass be used to diagnose the water status of winter wheat?

ContextAgriculture is the major consumer of fresh water globally. However, climate change-induced water scarcity is challenging the freshwater resources to meet global crop water demands. Consequently, utilizing limited water resources efficiently is increasingly important in agriculture. Accurate diagnosis of crop water status and in-season demand is imperative for precision irrigation, as it allows growers to make informed decisions about fine-tuning in-season irrigation scheduling. Plant-based diagnostic approaches such as the critical saturated water accumulation (SWA) curve have been employed to diagnose the in-season crop water status in maize. However, it remains uncertain if this indicator applies to other crops including wheat. ObjectiveThe study aimed to develop and validate a model for quantifying the in-season water status of winter wheat using SWA curves and water diagnosis index (WDI). MethodsA four-year study was carried out by employing four water and two nitrogen rate treatments from 2019 to 2023 to establish the relationship between dry mass (DM) and SWA during the vegetative growth period of winter wheat. The effects of different water and nitrogen conditions on DM-SWA allometry were also analyzed. Besides, WDI was also derived as the ratio of the observed SWA value to the critical SWA value. ResultsThe allometric relationships between SWA and DM were used to construct critical SWA curves under sub-optimal and optimal nitrogen nutrition (N1: SWA = 5.34DM0.77; N2: SWA = 7.37DM0.83). The decrease in plant stem ratio and indirect soil nitrogen deficiency caused by water stress were the two main factors contributing to the decrease in plant WDI. However, uncertainties of observation and input data, model assumptions, model structure, and parameters led to a high degree of uncertainty in WDI, which affected its practical application. ConclusionsThe allometric relationships between SWA and DM can potentially be used as a diagnostic tool for assessing crop water status. Nitrogen deficiency would reduce the SWA without affecting its accumulation rate. Further research is required to employ WDI for plant water status diagnosis under field conditions. Significance/ImplicationsThe findings would assist in developing precision irrigation indicators for monitoring in-season plant water status, optimizing irrigation scheduling, selecting water-efficient cultivars, and enhancing wheat productivity.

Cost-effectiveness of durvalumab plus tremelimumab in combination with chemotherapy for the treatment of metastatic non-small-cell lung cancer from the US healthcare sector's and societal perspectives.

Metastatic non-small cell lung cancer (mNSCLC) has a high incidence rate, and economic burdens to patients, healthcare systems, and societies. Durvalumab plus tremelimumab and chemotherapy (T+D+CT) is a novel therapeutic strategy for mNSCLC, which demonstrated promising efficacy in a phase-3 randomized clinical trial, but its economic value remains unclear. This economic evaluation used a hypothetical cohort of patients with mNSCLC, with characteristics mirroring those of the participants in the POSEIDON trial. Several partitioned survival models were constructed to estimate 15-year costs and health outcomes associated with the T+D+CT, durvalumab plus chemotherapy (D+CT) and chemotherapy alone (CT) strategies, discounting costs and effectiveness at 3% annually. Costs were in 2023 US dollars. Data were derived from the POSEIDON trial and 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 2022 to March 2023. To evaluate the cost-effectiveness of T+D+CT, compared with CT and D+CT, for mNSCLC from the perspectives of the US healthcare sector and society. From the healthcare sector's perspective, the T+D+CT yielded an additional 0.09 QALYs at an increased cost of $7,108 compared with CT, which resulted in an ICER of $82,501/QALY. The T+D+CT strategy yielded an additional 0.02 QALYs at an increased cost of $27,779 compared with the D+CT, which resulted in an ICER of $1,243,868/QALY. The economic results of T+D+CT vs. CT were most sensitive to the annual discount rate, subsequent immunotherapy cost, tremelimumab cost, palliative care and death cost, pemetrexed cost, and durvalumab cost. The T+D+CT strategy was considered cost-effective relative to CT in 59%-82% of model iterations against willingness-to-pay. thresholds of $100,000/QALY gained to $150,000/QALY gained. From the societal perspective, the T+D+CT can be considered as cost-effective as compared with CT or D+CT, independent of histology. In this cost-effectiveness analysis, the T+D+CT strategy represented good value compared with CT for patients with mNSCLC from the perspectives of the healthcare sector and the society. This treatment strategy may be prioritized for mNSCLC patients at high risks of disease progression.

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.

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. This study aims to evaluate the utilities and cost-effectiveness of pharmacogenomics-guided versus standard dosing of warfarin in patients with MPHV in Iran. 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. 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. 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.