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Spectroscopic ellipsometry and some other optical metrology techniques, such as reflectometry and scatterometry, are model-based optical measurements and, therefore, require appropriate modeling to determine the geometric and material properties of substrates, thin films, and multilayer structures. Parametric sensitivity analysis (SA) provides essential assistance in the model-building process to quantify the relative importance of model parameters for model output and to identify those with high/little influence. SA can be performed in a variety of ways, and this article discusses an application of the Morris or elementary effect (EE) method, a screening type SA procedure, to spectroscopic ellipsometry modeling. The method is a global SA technique and uses a stepping of m parameters along certain so-called "trajectories" or sequences of points in parameter space, randomly constructed in order to maximally fill the volume of the m -dimensional parameter space. However, it was thought that the EE method relies greatly on a sampling strategy or a way of selecting "optimized trajectories" in the parameter space, i.e., a necessary number of trajectories chosen to be well spread over the space to properly cover the entire realistic ranges of all input factors. Here, we use two sampling methods for selecting trajectories with possibly different distributions and investigate their effects on the estimation of various sensitivity measures in spectroscopic ellipsometry data modeling. The SA results indicate that the performance of the sampling strategy should not be judged only by maximizing the trajectory spread but also include some additional convergence criteria for the sensitivity measures. [ABSTRACT FROM AUTHOR]

Typically, the parameters entering a physical simulation model carry some kind of uncertainty, e.g., due to the intrinsic approximations in a higher fidelity theory from which they have been obtained. Global sensitivity analysis (GSA) targets quantifying which parameter uncertainties impact the accuracy of the simulation results, e.g., to identify which parameters need to be determined more accurately. We present a GSA approach based on the Cramérs–von Mises distance. Unlike prevalent approaches, it combines the following properties: (i) it is equally suited for deterministic as well as stochastic model outputs, (ii) it does not require gradients, and (iii) it can be estimated from numerical quadrature without further numerical approximations. Using quasi-Monte Carlo for numerical integration and a first-principles kinetic Monte Carlo model for the CO oxidation on RuO2(110), we examine the performance of the approach. We find that the results agree very well with what is known in the literature about the sensitivity of this model and that the approach converges in a modest number of quadrature points. Furthermore, it appears to be robust against even extreme relative noise. All these properties make the method particularly suited for expensive (kinetic) Monte Carlo models because we can reduce the number of simulations as well as the target variance of each of these. [ABSTRACT FROM AUTHOR]

Nanofilms (NFs) have been widely used in many emerging applications, such as microelectronic devices, spintronics, and optical instruments. Characterizing the thermal conductivity (k) of NFs, kNFs, is nontrivial for both fundamental science and industrial applications. Time-domain thermoreflectance (TDTR) is a powerful technique for thermal characterizations under nano-to-micro-scales. However, both the kNFs and the interface thermal resistance between layers are generally unknown parameters when analyzing TDTR signals. So that an effective thermal resistance model (ETRM) is often utilized, where the impact of heat capacity of NFs, cNFs, has been always ignored. Previous studies have proposed viewpoints on how to validate this assumption, however, which still needs to be verified further by considering the parameters' sensitivities for TDTR signals. In this work, we have highlighted the significance of sensitivity analysis for the investigation of the impact of cNFs with the example experiments on Ni NFs. The error of ETRM has been quantitatively and systematically studied. We found that the application requirements of ETRM in TDTR data analysis rely on the sensitivity relations between kNFs and interface thermal resistance. Finally, suggestions for future characterization of the kNFs have been discussed for reference. Our results and conclusions deepen the understanding of both the transient thermal transport process of multilayer NFs and the ability of TDTR on characterizing kNFs. [ABSTRACT FROM AUTHOR]

An important strategy for identifying principal causal effects (popular estimands in settings with noncompliance) is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one‐sided noncompliance setting where there are two principal strata, compliers and noncompliers. Under PI, compliers and noncompliers share the same outcome‐mean‐given‐covariates function under the control condition. For sensitivity analysis, we allow this function to differ between compliers and noncompliers in several ways, indexed by an odds ratio, a generalized odds ratio, a mean ratio, or a standardized mean difference sensitivity parameter. We tailor sensitivity analysis techniques (with any sensitivity parameter choice) to several types of PI‐based main analysis methods, including outcome regression, influence function (IF) based and weighting methods. We discuss range selection for the sensitivity parameter. We illustrate the sensitivity analyses with several outcome types from the JOBS II study. This application estimates nuisance functions parametrically – for simplicity and accessibility. In addition, we establish rate conditions on nonparametric nuisance estimation for IF‐based estimators to be asymptotically normal – with a view to inform nonparametric inference. [ABSTRACT FROM AUTHOR]

To achieve smart and sustainable construction goals, machine learning (ML) techniques can serve as a cost-effective and efficient substitute for labour-intensive, laboratory, or in situ approaches in parameter estimation essential for infrastructure design. Of these, soil's shear strength parameters, notably cohesion (c) and friction angle (φ), typically govern the design of geo-structures. For quick and cost-effective estimation of these parameters, the earlier studies proposed ML-based predictive models that were less practical and accurate or consider an excessive number of input variables. To minimize these limitations, our study proposes new models of c and φ using gene expression programming (GEP) based on readily available soil attributes such as sand content (S), depth (D), specific gravity (Gs), liquid limit (LL), plastic limit (PL), and fine content (FC). The newly proposed models show excellent accuracy as the values of R2, RMSE (root mean square error), MAE (mean absolute error), RSE (relative standard error) for c-predictive model were 0.984, 1.13, 0.878, 0.017, respectively, and were 0.927, 1.123, 0.922, 0.072, respectively, for φ-predictive model. Through sensitivity analysis, FC and LL emerged as the most critical parameters influencing c, while Gs and PL proved sensitive for determining φ. In comparison with existing models, the c-predictive model displays R2 enhancements of 11.84–45.87% and RMSE improvements of 65.9–92.03%, while the φ-predictive model showcases R2 gains of 13.16–23.75% and RMSE improvements of 58.79–69.29%. By integrating predictive prowess with sustainable and smart construction principles, our study plots a realistic course for efficient geo-structural design. [ABSTRACT FROM AUTHOR]

Longitudinal clinical trials for which recurrent events endpoints are of interest are commonly subject to missing event data. Primary analyses in such trials are often performed assuming events are missing at random, and sensitivity analyses are necessary to assess robustness of primary analysis conclusions to missing data assumptions. Control‐based imputation is an attractive approach in superiority trials for imposing conservative assumptions on how data may be missing not at random. A popular approach to implementing control‐based assumptions for recurrent events is multiple imputation (MI), but Rubin's variance estimator is often biased for the true sampling variability of the point estimator in the control‐based setting. We propose distributional imputation (DI) with corresponding wild bootstrap variance estimation procedure for control‐based sensitivity analyses of recurrent events. We apply control‐based DI to a type I diabetes trial. In the application and simulation studies, DI produced more reasonable standard error estimates than MI with Rubin's combining rules in control‐based sensitivity analyses of recurrent events. [ABSTRACT FROM AUTHOR]

Sample selection model is a solution to eliminate the nonresponse bias. In some applications nonresponse is a multilevel variable with respect to its reasons of occurring. In these cases, the sample selection model can be extended such that a model to be considered for each of the nonresponse reasons. Also, in many cases, the reasons for nonresponse have priority over each other. In other words, it is not possible to observe all of the nonresponse reasons simultaneously. For example, in a survey with two noncontact and refusal reasons, noncontact has priority over refusal and refusal can be observed if the contact to the respondent can be established. For analyzing such extended model, a Bayesian inference approach with multiple selection rules using multivariate normal, inverse gamma and LKJ distributions as prior distributions for parameters and possibility of priority for nonresponse reasons is presented. Simulation studies are performed and an establishment survey data set is analyzed to demonstrate the performance of the proposed method. For sensitivity analysis of nonresponse on the parameters of interest, posterior displacement is applied. [ABSTRACT FROM AUTHOR]

This study focuses on the improvement of the tracking performance of space observation radar systems. Coherent radar systems usually provide an observation vector including four observables (range, range rate, azimuth, elevation) measured at a given epoch. Moreover, for some coherent radars, such as the TIRA system, it is possible to determine an additional parameter, the range rate, which is extracted from the collected complex amplitude. The tracking performance can be improved by increasing the observation parameter accuracy. The influence of each parameter on the overall tracking performance can be assessed by conducting a sensitivity analysis. The investigation shows that the best way to increase the accuracy of the state vector is by improving the angle estimates. The TIRA system is equipped with a tracking radar and an imaging radar. A powerful solution to improve the tracking performance is the realization of a multi-sensor data fusion between the two radars of the system. [ABSTRACT FROM AUTHOR]

AbstractA statistical model finds application across various engineering and scientific contexts, aiding in the analysis and enhancement of heat transfer processes. Its utility extends to supporting scientists and engineers in gaining deeper insights into the impact of diverse factors on heat transfer and optimizing procedures for enhanced efficiency. Notably, the heat transfer efficiency of hybrid nanofluids containing ferromagnetic nanoparticles outperforms that of conventional fluids. In this study, a statistical model is utilized to predict the increased heat transfer rate in a heated shrinking sensor employing hybrid magnetic nanoparticles with slip restrictions. Incorporating radiative heat flux and heat source/sink components contributes novelty to the study. The model is converted into a non-dimensional form using appropriate similarity rules, and the resulting problem is solved computationally using bvp5c, a built-in function in MATLAB. Using advanced mathematical modeling and simulations, this research evaluates system performance in various scenarios and identifies optimal conditions for maximizing heat transfer rates. The study's main outcomes are; that including the thermal radiation and Prandtl number enhances the heat transfer rate, whereas the heat source decreases it. Further, the increased modified Hartmann number and suction parameter raise the shear rate coefficient. [ABSTRACT FROM AUTHOR]

Transportation systems involve high-density crowds of geographically diverse people with variations in susceptibility; therefore, they play a large role in the spread of infectious diseases like SARS-CoV-2. Dose-response models are widely used to model the relationship between the trigger of a disease and the level of exposure in transmission scenarios. In this study, we quantified and bounded viral exposure-related parameters using empirical data from five transportation-related events of SARS-CoV-2 transmission. Dose-response models were then applied to parametrically analyze the infection spread in generic transportation systems, including a single-aisle airplane, bus, and railway coach, and then examined the mitigating efficiency of masks by performing a sensitivity analysis of the related factors. We found that dose level significantly affected the number of secondary infections. In general, we observed that mask usage reduced infection rates at all dose levels and that high-quality masks equivalent to FFP2/N95 masks are effective for all dose levels. In comparison, we found that lower-quality masks exhibit limited mitigation efficiency, especially in the presence of high dosage. The sensitivity analysis indicated that a reduction in the infection distance threshold is a critical factor in mask usage. [ABSTRACT FROM AUTHOR]

This article provides insights in designing a dielectrically modulated biosensor by adopting high-k stacked gate oxide proposition in a bi-metal hetero-juncture Tunnel Field Effect Transistor (BM-SO-HTFET) with Si0.6Ge0.4 source. The integrated effect of heterojunction and stacked gate oxide leads to enhanced electrical performance of the proposed device in terms of carrier mobility and suppressed leakage current. Nano-cavity engraved beneath the bi-metal gate structure across the source/channel end acts the binding site of the biomolecules to be detected. This Configuration leads to improved control of biomolecules over source/channel tunnelling rate and the same is reflected in the sensing ability of the device while extracting the ON current sensitivity (SON) of the sensor. The reported biosensor is simulated using Silvaco ATLAS calibrated simulation framework. The analysis of the device sensitivity is carried out varying dielectric constants (k) of various biomolecules, both neutral as well as charged. Our study reveals that BM-SO-HTFET with Ge mole fraction composition x = 0.4 exhibits sensitivity as high as 4.1 × 1010 for neutral biomolecules and 3.2 × 1011 for positively charged biomolecules with k = 12. Furthermore, a transient response profile for the drain current with various biomolecules is explored to determine the varying settling time. From the simulation results, it is noted that BM-SO-HTFET exhibits ON current sensitivity of 4.1 × 1010 and 3.2 × 1011 for neutral and charged biomolecules respectively. In addition to this, for highly sensitive and real time detection of biomolecules, the impact of temperature and certain non-ideal factors drifting from ideal case of fully filled cavity have also been considered to analyze its optimum sensing performance. [ABSTRACT FROM AUTHOR]

Retinal detachment (RD) is the separation of the neural layer from the retinal pigmented epithelium thereby preventing the supply of nutrients to the cells within the neural layer of the retina. In vertebrates, primary photoreceptor cells consisting of rods and cones undergo daily renewal of their outer segment through the addition of disc-like structures and shedding of these discs at their distal end. When the retina detaches, the outer segment of these cells begins to degenerate and, if surgical procedures for reattachment are not done promptly, the cells can die and lead to blindness. The precise effect of RD on the renewal process is not well understood. Additionally, a time frame within which reattachment of the retina can restore proper photoreceptor cell function is not known. Focusing on rod cells, we propose a mathematical model to clarify the influence of retinal detachment on the renewal process. Our model simulation and analysis suggest that RD stops or significantly reduces the formation of new discs and that an alternative removal mechanism is needed to explain the observed degeneration during RD. Sensitivity analysis of our model parameters points to the disc removal rate as the key regulator of the critical time within which retinal reattachment can restore proper photoreceptor cell function. [ABSTRACT FROM AUTHOR]

AbstractIn connection to the various applications on energy conservation, fluid-based electronics; the coupling of electro-magnetic along with heat generation presents their significant approach. Therefore, the current investigation aims at analyzing the role of ferrite-water hybrid nanofluid in an Electro-Magnetohydrodynamics (EMHD) system. However, it utilizes the flow over a Riga plate with the variable properties of magnetization with the inclusion of thermal radiation and heat generation. Elucidating the influence of ferrite nanoparticles, the unsteady nature of the system addressed various properties of nanofluid. The system of equations is handled numerically adopting shooting-based fourth-order Runge-Kutta technique followed by similarity rules those are used to transformed the governing phenomena into ordinary. To maximize the EMHD efficiency, an optimization framework is employed using response surface methodology (RSM). However, to validate the work a hypothetical test is conducted with the help of analysis of variance (ANOVA). Through sensitive analysis, the key factors for the response of heat transfer rate are identified. Further, the measure outcomes of the results are; the inclusion of the ferrite nanoparticles with the increasing EMHD parameter the fluid velocity augments in magnitude. Further, increasing radiation the surface of the sheet became cool and it radiate from the surface region for which the fluid temperature boosts up. [ABSTRACT FROM AUTHOR]

Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample‐based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol’ sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three‐dimensional fluid–structure interaction simulations with affordable one‐ and zero‐dimensional reduced‐order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi‐ (1D/0D) and tri‐fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single‐fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol’ indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response. [ABSTRACT FROM AUTHOR]

Cloud computing is a model for allowing suitable, on-demand network access to a shared store of resources such as servers, networks, storage, apps, and services, modified according to specific needs or requirements. The main goal of cloud technology development is to increase the use of resources that work together to achieve reliability at the lowest cost. Cloud service providers (CSPs) have gained popularity in recent years due to their accessibility and availability, as well as the growing quantity of cloud service providers (CSPs) that appear. Choosing (CSPs) has grown to be a challenging decision for many companies. The paper aims to rank a set of cloud service providers based on the multi-criteria decision-making (MCDM) method. The suggested method's applicability is verified by comparing the outcomes with two established methodologies: SWARA and MARCOS methods under the type-2 neutrosophic number set (T2NNS) environment to calculate the importance of evaluation criteria and ranking the alternatives of cloud providers. A sensitivity analysis was executed to check the robustness of this model by examining the effect of criteria weights on the ranking of the alternatives. [ABSTRACT FROM AUTHOR]

A six-axis machining configuration is proposed to improve the surface quality and finishing efficiency of an aeroengine blisk. It flexibly enlarges the accessible interference-free space of the tool for finishing the full-shape surface, including the blade body, root, and hub surfaces. An error model for tool contact posture with respect to the kinematic axis errors is established, revealing the relationship between the kinematic deviation of the axes and resultant profile error. An improved Sobol method is applied for error sensitivity analysis to identify crucial error terms based on the quasi-Monte Carlo algorithm. In particular, an efficient error sensitivity index is proposed by combining a unified objective function to identify crucial error terms, followed by the simulations and cutting experiments for validation. As a result, the blade profile error was successfully suppressed by approximately 40 % in most. This work provides a fundamental framework for elevating the multi-axis machining accuracy of aeroengine blisk. • A six-axis machine tool has been proposed to achieve the full-shape-surface grinding on the blisk. • A constrained error sensitivity index model was proposed to compensate for key error terms. • Sensitivity analysis was conducted using an improved Sobol method incorporated quasi-Monte-Carlo algorithm. • The effectiveness of error sensitivity analysis and corresponding tool-path-based compensation were verified. [ABSTRACT FROM AUTHOR]

Solid oxide fuel cell power generation systems are devices that utilize solid electrolytes to transfer ions for electrochemical energy conversion. A wide range of gases can be used as fuel gas, including hydrogen, natural gas, and carbon monoxide. Considering the high cost of pure hydrogen, hydrogen production from natural gas reforming has become a hot research area. In this study, the 4F-LCA method was employed to construct an evaluation framework, with a particular emphasis on the cost analysis of solid oxide fuel cell power generation systems, and uses a bottom-up approach to build a system economic analysis model to visualize the major costs involved in the system. An economic benefit analysis and sensitivity analysis were carried out for the 2 kW natural gas solid oxide fuel cell as a case by taking the financial net present value (NPV), internal rate of return (IRR) and payback period into account. In this study, the investment cost and payback period of a 2 kW solid oxide fuel cell system are obtained, which can provide a reference for the project construction and operation of solid oxide fuel cell systems. [ABSTRACT FROM AUTHOR]

African swine fever (ASF) is endemic in many African countries, and its control is challenging because no vaccine or treatment is available to date. Nowadays, mathematical modeling is a key tool in infectious disease studies, complementing traditional biological investigations. In this study, we propose and analyze a mathematical model for the transmission dynamics of African swine fever (ASF) in Benin that considers the free-living virus in the environment. We provide the theoretical results of the model. The study of the model is conducted by first proving that the model is well posed by showing the positivity and the boundedness of solutions as well as the existence and uniqueness of the solution. We compute the control reproduction number R c as well as the basic reproduction number R 0 , which helps to analyze the extinction or the persistence of the disease in the pig population. We provide the global attractivity of the disease-free equilibrium and the endemic equilibrium and study their stabilities. After, we estimate some unknown parameters from the proposed model, and the sensitivity analysis is carried out to determine the parameters that influence the control reproduction number. Finally, through numerical simulations, in the current situation, we find that R 0 = 2.78 , which implies that the disease will not die out without any control measures and R c = 1.55 showing that the eradication of the disease highly depends on the control measures taken to reduce disease transmission. [ABSTRACT FROM AUTHOR]

Perturbation bounds are powerful tools for investigating the phenomenon of insensitivity to perturbations, also referred to as stability, for stochastic and deterministic systems. This perspective article presents a focused account of some of the main concepts and results in inequality-based perturbation theory for finite state-space, time-homogeneous, continuous-time Markov chains. The diversity of perturbation bounds and the logical relationships between them highlight the essential stability properties and factors for this class of stochastic processes. We discuss the linear time dependence of general perturbation bounds for Markov chains, as well as time-independent (i.e., time-uniform) perturbation bounds for chains whose stationary distribution is unique. Moreover, we prove some new results characterizing the absolute and relative tightness of time-uniform perturbation bounds. Specifically, we show that, in some of them, an equality is achieved. Furthermore, we analytically compare two types of time-uniform bounds known from the literature. Possibilities for generalizing Markov-chain stability results, as well as connections with stability analysis for other systems and processes, are also discussed. [ABSTRACT FROM AUTHOR]

Microgrids and hybrid renewable energy systems play a crucial role in today's energy transition. They enable local power generation and distribution, reducing dependence on large centralized infrastructures, can operate independently or connected to a grid, and can provide backup power, thus increasing system resilience. In addition, they combine multiple renewable energy sources, such as solar, wind, hydro, and biomass, to maximize the efficiency and reliability of the supply, and are also adaptable to location-specific conditions, taking advantage of locally available energy resources and reducing the need for energy imports. Moreover, they contribute to decarbonization goals by offering a cleaner and more sustainable alternative. In this article, a documentary review is presented on the interaction of Homer Pro software 3.16.2 (July 2023), used for the design of hybrid renewable energy systems (HRES), with other methods of optimization or sizing. Allusion is made to the type of architecture in the most prominent clean and fossil source configurations, the levelized cost, net annual cost, and maintenance and capital investment cost. A comparison is made among the works reported in the last five years regarding the use of this software tool, based on load demand, geographical area, renewable energy sources, fossil sources, and objective functions, applied to the educational, rural, and industrial sectors. It is shown that India is one of the countries that has reported the most number of HRES techno-economic environmental analysis works, and that the case studies have focused approximately 47% on rural areas, 20% on educational agencies, 14% on commerce and industry, and 29% on urban buildings. [ABSTRACT FROM AUTHOR]

Under the challenge of global energy transition, coal-fired cogeneration systems are undergoing a technical revolution towards enhanced efficiency, heating capacity, and flexibility. In this paper, four schemes using a steam ejector integrated into a cogeneration system are designed. Considering operational safety, integrated locations are selected at the front and back of high- and medium-pressure turbines. Subsequently, the thermodynamic and operational characteristics under both design and off-design conditions are analyzed based on a model built in EBSILON Professional. Finally, a sensitivity analysis of the heating process is conducted. The results show that the integration of steam ejectors can increase the waste heat recovery ratio of exhaust steam by 18.42–45.61% under design conditions. The largest waste heat recovery ratio is obtained in System 4, resulting in the power generation efficiency ( η g ) and gross energy utilization efficiency ( η p ) of 81.95% and 65.53%, respectively. Meanwhile, the steam ejector can expand the power-load regulation range of the cogeneration system, and System 4 has the lowest lower power limit among all the systems. The η p values of Systems 1–4 reach extreme values at different mixed steam pressures of the ejector. Increasing the pinch point temperature difference reduces the power load η g and η p of Systems 1–4. The results provide technical solutions for improving the heating capacity and efficient and flexible operation of cogeneration systems. [ABSTRACT FROM AUTHOR]

In this study, a data‐driven model was developed to predict compressive strength of concrete columns after confining with Fiber‐Reinforced Polymer (FRP). Through application of Response Surface Method (RSM), interactions of input parameters are investigated in addition to their individual influence. Moreover, RSM is utilized in conjunction with unification to enhance model's accuracy. Furthermore, sensitivity analysis is employed together with RSM, to determine the effect size of statistically significant parameters. It is revealed that the real behavior of FRP confined concrete cannot be predicted by single‐parameter studies. Therefore, interaction terms should be included in the model to yield a more accurate prediction. Moreover, the model shows the highest sensitivity to FRP failure strength which this sensitivity decreases as corner radius increases. Therefore, regarding rounded‐corner sections, high improvements are achievable even by utilizing low strength FRPs. It is also shown that higher improvement may be limited due to premature debonding which elastic modulus mismatch between different phases is supposed to be responsible for it. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm based on sensitivity analysis and the Wolfe method to solve a sequence of parametric quadratic programming (QP) problems such as those that arise in quadratic model predictive control (QMPC). The Wolfe method, based on Karush–Kuhn–Tucker conditions, has been used to convert parametric QP problems to parametric linear programming (LP) problems, and then the sensitivity analysis is applied to solve the sequence of the parametric LP problems. This strategy obtains sensitivity analysis‐based QMPC (SA‐QMPC) algorithm. It is proved that the computational complexity of SA‐QMPC is O(Nn2)$O(Nn^2)$ for a region of the initial conditions and O(N2n2)$O(N^2n^2)$ for sufficiently small sampling time and all initial conditions, where N$N$ and n$n$ are the horizon time and dimension of the state vector, respectively. Numerical results indicate the potential and properties of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Graphics processing units (GPUs) have become integral to embedded systems and supercomputing centres due to their large memory, cutting-edge technology and high performance per watt. However, their susceptibility to transient errors requires a comprehensive analysis of error sensitivity, as well as the development of error mitigation techniques and fault-tolerant algorithms. This study focuses on evaluating the soft-error sensitivity of two distinct versions of LU decomposition algorithms implemented on two very different GPUs—a low-power SoC embedded GPU and a high-performance massively parallel GPU. Through extensive fault injection campaigns on both GPUs, we examine the vulnerability of the algorithms, identify error causes, and determine critical code components requiring enhanced protection. The experiments reveal that most single bit flip fault injections in the instruction results lead to erroneous outcomes or unrecoverable errors. Notably, efficient GPU resource utilisation can increase the number of masked errors, thereby enhancing error resilience. Additionally, while different parts of the code exhibit similar error occurrence types and rates, the propagation of errors to elements within the result matrix differs significantly. [ABSTRACT FROM AUTHOR]

Influences of hydropower plant water turbine operational parameters on turbine operational economics, reliability and lifetime are ranged with a new type of QFD method that includes parameter distribution densities and probabilities. In our method, each dominant i.e., main parameter coexists with associated side parameters. Their contribution to effects, usually attributed to the main parameter only, is modelled by a) probability density of the side parameter contribution to the main parameter effects, and by b) side parameters' Bernoulli distribution since association of side parameter to the dominant parameter effects is in the realm of probability and not all side parameter effects are associated with the main parameter effects at all times. Analysis results are probability densities of dominant parameters influence measure on the turbine operating attributes. A simulation tool was built to establish relations amongst influential parameters and turbine's economics, reliability and lifetime. We obtained technical data associated with turbine operation attributes from turbine senior designers having led successful projects within last 30 years. Simulation results have been validated with existing turbine-projects maintenance data. [ABSTRACT FROM AUTHOR]

Landslide is one of the most destructive hazards in the Upper Beas valley of the Himalayan region of India. Landslide susceptibility mapping is an important and preliminary task in order to prospect the spatial variability of landslide prone zones in the area. As the use of machine learning algorithms has increased the success rate in susceptibility studies, the performance of the four machine learning models, namely Naïve Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were initially tested for landslide susceptibility mapping in the area. Landslide inventory containing both landslide and non-landslide data and thirteen landslide conditioning factors were considered to train the models. The models were optimized using hyperparameter optimization and input factors selection based on variable importance. Among the four models, Extreme Gradient Boosting (XGBoost), an advanced ensemble-based machine learning algorithm, demonstrated superior performance (AUC = ~ 0.91) followed by RF, NB and KNN with AUC values of ~ 0.88, ~ 0.87, and ~ 0.82. Therefore, XGboost model was selected for detailed study, including sensitivity analysis. The results depict that 44% of the total area falls under high and very high susceptible zones. Southward facing slopes having inclination between 31˚-50˚ located at an elevation of 2001–3000 m in the vicinity of road and drainage network contain most of the landslide susceptible zones. Sensitivity analysis has provided an in-depth understanding of the factors’ relation with the model as the XGBoost model is most sensitive to factors such as slope inclination, distance to thrust and road, elevation, TWI and slope aspect. [ABSTRACT FROM AUTHOR]

Zika virus outbreak is creating a global public health hazard due to its increasing prevalence rate and neurological complication in affected individuals. In this work we have developed and studied an ODE compartmental Zika model with non-linear saturated incidence function and three control measures including prevention control from vector bite, treatment control of infected host and vector killing control. Our theoretical analysis shows that the disease free equilibrium is locally as well as globally asymptotically stable when the basic reproduction number is less than unity. The virus is uniformly persistent when the basic reproduction number is greater than unity. An endemic equilibrium point exists and locally stable when the basic reproduction number is greater than unity. The model is validated by fitting the model to real reported data of 2016 Zika outbreak in Puerto Rico and estimated the model parameters. We also have studied the basic reproduction number and effective reproduction number of the outbreak. Sensitivity analysis has been carried out to find most sensitive parameters. The optimal control and efficiency analysis has been done to find the policy of control variables. Lastly, the manuscript is ended by some conclusions. [ABSTRACT FROM AUTHOR]

This study estimates the effect of between-class ability grouping on Chilean secondary students' academic achievement. We rely on a structural feature of the school system: A considerable number of students who complete primary school must change schools to start their secondary education. Cardinality matching was performed to account for confounding variables. Sensitivity analysis was performed to address the fact that differences in unobserved variables could bias our findings. Results show that attending a school that groups students by ability causes a reduction in the average scores obtained in 10th grade of 0.07 SD for reading and 0.08 SD for mathematics. Students matched by their observed covariates could differ in their odds of attending a school with or without ability grouping by 15% for mathematics and 10% for reading, without altering our conclusions. Disadvantaged students are the most affected, whereas best-performing students do not benefit from this policy either. [ABSTRACT FROM AUTHOR]

Reversible addition‐fragmentation chain‐transfer polymerization, or RAFT, is a method for the controlled synthesis of block copolymers and an alternative to living ionic polymerizations. Although it is less susceptible to impurities, oxygen leads to inhibition of the polymerization. In a previous study, a model was developed to describe the inhibition kinetics of the RAFT polymerization of styrene. Based on this model, a detailed sensitivity analysis is performed with respect to the kinetic coefficients. While the formation of polyperoxides by oxygen addition shows high reaction rates, their further reactions (growth and termination) could be identified as rate‐determining steps. In addition, a proportional dependence of the duration of inhibition on the oxygen concentration was found. Based on the knowledge gained, a new initiation strategy with different thermal initiators to optimize polymerization was successfully tested using simulations. [ABSTRACT FROM AUTHOR]

From a mathematical viewpoint, the frequency domain analysis of vessel motion responses due to wave actions is based on integration of system dynamics idealized in terms of response amplitude operators for six degrees-of-freedom (DOFs) rigid body motions and an input wave spectrum in order to obtain the response spectrum. Various quantities of interest can be deduced from the response spectrum, which are then used for deriving response-based operational limits for marine operations, also including extreme value and fatigue analysis. The variation of such quantities, owing to the uncertainties associated with the vessel system parameters, can be quantified by performing uncertainty propagation and consequent sensitivity analysis. This study emphasizes and proposes a computational-efficient way of assessing the sensitivity of the system model output with respect to the uncertainties residing in the input parameters by operating on a surrogate model representation. In this respect, the global sensitivity analysis was effectively carried out by deploying an efficient nonintrusive polynomial chaos expansion surrogate model built using a point collocation strategy. Successively, Sobol' indices were obtained from the analytical decomposition of the polynomial coefficients. The indices, eventually, are employed to quantitatively measure the effects of input uncertainties on the output 6DOF vessel root-mean-square responses. [ABSTRACT FROM AUTHOR]

This study delves into a complex mathematical model, namely the fractional Chen–Lee–Liu (FCLL) model in birefringent fibers which has two components in the form of vector solitons in optical fiber. The FCLL model has remained a uniform fascination for scientists due to its energetic significance in the study of circuit design, signal processing, secure communications, encryption and decryption of chaotic signals, as well as in plasma physics. For the intellectual curiosity of new precise solutions of the selected model, two innovative norms namely, the modified Sardar-sub equation scheme and the improved F -expansion method have been suggested in the sense of a M-truncated derivative. By employing the aforementioned strategies the bright, dark, bright-dark, periodic, combo, singular, mixed trigonometric, hyperbolic, exponential as well as rational soliton solutions are segregated. Moreover, we examined the sensitivity analysis of selected model. By selecting appropriate parameters, numerical simulations of the attained outcomes are sketched as interesting figures that present the meaning of the obtained results. The generated outcomes are optimistic, demonstrating that the selected approaches are robust, categorical, and efficient in discovering novel solutions to the variety of complex nonlinear models that have arisen in the recent phase of applied sciences. [ABSTRACT FROM AUTHOR]

In this paper, the nonlinear (4 + 1)-dimensional Fokas model is studied and its dynamic character is predicted. This inquiry aims to accomplish two main objectives. First, the Lie symmetries are created and the associated transformation is applied to reduce the model to ordinary differential equations. Plots are used to demonstrate and establish invariant solutions. Second, several methods such as bifurcation, sensitivity analysis, and chaos are used to investigate the dynamic behavior of the model. Bifurcation theories are used to analyse a dynamical system's bifurcation at equilibrium points. The nonperturbed system introduces the perturbation period, which deviates from regular patterns. Different initial conditions are used to investigate the sensitivity analysis the model is found to be highly sensitive. The findings are unique, stimulating and mathematically helpful to comprehend the model. It is essential to comprehend the systems and processes that behave dynamically to investigate new technology, suggest interventions and forecast the future. [ABSTRACT FROM AUTHOR]

This study covers the traveling wave analysis of the two-mode Cahn–Allen (TMCA) equation. The TMCA model plays the role of transmitting information into two different locations and preserving its physical properties. A wave transformation converts the considered model into an ordinary differential equation (ODE). By applying the two analytical techniques, the extended tanh - coth technique and the extended G ′ G 2 -expansion scheme, we have obtained a new type of the wave structures like singular, kink and periodic. To present the significance of the acquired outcomes, we have shown the graphical behaviors of the results by taking some suitable values of the involved parameters. The acquired ODE can be transformed into a dynamical system by applying the Galilean transformation. To examine the sensitive behavior, the dynamical system is expressed in a non-autonomous form, and the effects are analyzed by considering various initial conditions. The considered model admits the two-dimensional Lie algebra. The nonlinear self-adjointness of the considered model is checked using a new conservation theorem. The results show that the considered model is not self-adjoint and is made self-adjoint by computing a new dependent variable. Finally, the conserved quantities corresponding to each symmetry generator are computed. [ABSTRACT FROM AUTHOR]

The changing frequency of flooding in global watersheds, driven by various human and natural factors like land use/cover changes and global warming, necessitates innovative approaches in flood frequency analysis and risk assessment. Nonetheless, the reliability of nonstationary frequency analysis models remains a concern given challenges in accurately measuring the uncertainty introduced by these methods and the impact on design flood values. In this study, deviation-based differential sensitivity indices, including single-parameter (SDDSI) and entire-parameter (EDDSI) measures were developed to assess the influence of parameter uncertainty in nonstationary models using Bayesian statistics and "equivalent reliability" nonstationary design. The Weihe River, the largest tributary of the Yellow River which is experiencing both climate change and heavy impact of human activities, is chosen to be the study area to investigate the impact of precipitation change and land use change on nonstationary flood frequency. Results show that in the One-At-A-Time (OAT) sensitivity analysis under a small uncertainty scenario (SUS) for parameter inputs, the shape parameter stands out as the most influential factor (SDDSI_SUS = 0.347) affecting the 100-year design flood in the Stationary Generalized Extreme Value (SGEV) model. For the Non-Stationary GEV (NGEV) models, the influence of this parameter is less pronounced, with SDDSI_SUS values of 0.095 and 0.093 for the SSP126 and SSP585 scenarios, respectively. Instead, attention turns to the regression coefficient of the grassland area, associated with the GEV scale parameter. In global sensitivity analysis under the posterior uncertainty scenario (PUS) for parameter inputs, the EDDSI_PUS values for SGEV, NGEV_SSP126, and NGEV_SSP585 models were 0.52, 1.41, and 1.30, respectively, inferring heightened sensitivity of NGEV models to perturbations from entire parameters. It is anticipated that incorporating additional evidence, such as historical flood data, is essential for accurate nonstationary hydrological design to mitigating the influence of parameter uncertainty. The sensitivity indices in this study provide significant insights for assessing the robustness of nonstationary hydrological design in flood risk management and applications. [ABSTRACT FROM AUTHOR]

Respondent-driven sampling (RDS) is widely used to sample populations with higher risk of HIV infection for whom no sampling frames exist. However, few studies have been done to assess the reliability of RDS in real world settings. We assessed the reliability of naïve RDS samples using five rounds of the National HIV Behavioral Surveillance – People Who Inject Drugs surveys in Newark, New Jersey from 2005 to 2018. Specifically, we compared the distributions of time-insensitive demographic characteristics in temporally adjacent RDS samples with Monte Carlo Two-Sample Kolmogorov-Smirnov Test with 100,000 replicates. The distributions of time-sensitive demographic characteristics were also compared as sensitivity analyses. The study showed that repeated RDS samples among people who inject drugs in the greater Newark area, New Jersey were reliable in most of time-insensitive demographics and recruitment homophily statistics. Sensitivity analyses of time-sensitive demographics also presented consistencies in most of temporally adjacent samples. In conclusion, RDS has the potential to provide reliable samples, but demographic characteristics of RDS samples may be easily biased by homophily. Future studies using RDS may need to pay more attention to potential homophily bias and consider necessary diagnostic procedures and sample adjustments. [ABSTRACT FROM AUTHOR]

Water huff-n-puff is considered to be an available method to exploit unconventional oil reservoirs, which can significantly promote the reservoir pressure and facilitate water–oil exchange under the imbibition effect. To explore the production characteristics and well performance during water huff-n-puff in the Huanjiang oil field that is a tight oil reservoir, field tests were carried out and production characteristics and well performances were investigated. Moreover, reservoir simulation works were conducted to study the various factors that influence the water huff-n-puff performance. Furthermore, sensitivity analysis for various factors was carried out with the tornado diagram method and the importance of different influencing factors was ranked quantitatively. The results demonstrate that water huff-n-puff has great significance to effectively replenish reservoir pressure as well as promote oil production. Field tests demonstrate that the average liquid rate as well as the oil rate of the horizontal wells increased by 5.94 and 1.18 m3/day , respectively. Considering the different oil production characteristics, the oil production process can be classified into three different stages. The levels importance of different influencing factors were ranked as rock wettability, water huff-n-puff cycles, water injection volume, hydraulic fracturing sections, well position, and soaking time. [ABSTRACT FROM AUTHOR]

The water hammer, resulting from sudden valve closures or similar operations, is a primary cause of leakage and damage in fluid-conveying pipe systems. This work aims to investigate the reliability sensitivity of fluid-conveying pipes under the water hammer. A reliability model for the fluid-conveying straight pipe, involving uncertainty and fluid-structure interaction, is developed based on the stress-strength interference theory. Then the method of characteristics is employed to analyze the nonlinear behaviors of the pipe. To avoid excessive model evaluations, the Kriging surrogate strategy is used to evaluate rapidly the statistics of nonlinear responses. Furthermore, the Kriging model coupled with the single-loop Monte Carlo simulation is presented for estimating reliability sensitivity indices. The accuracy of results is verified by the Monte Carlo simulation. The results reveal that the proposed method is feasible for the reliability sensitivity analysis of fluid-conveying pipe under the water hammer. Moreover, the wall thickness has the most obvious influence on stress failure, followed by the inner radius, while the pipe length, Young's modulus, density of the pipe material, and Poisson ratio show a low effect. This work can not only guide the safety design of pipe but also enrich the theory and application of statistical analysis and reliability sensitivity evaluation for liquid-conveying pipe systems. • A reliability model for water hammer-induced failure of fluid-conveying pipe is developed. • The reliability model is proposed, incorporating uncertainty and fluid-structure interaction. • The Kriging-based strategy is employed to estimate the response and reliability sensitivity of the pipe. • Different input variables have diverse effects on the nonlinear behavior and reliability of the pipe. [ABSTRACT FROM AUTHOR]

Renewable energy technologies (RET) are expanding over the globe to fulfill the world's overall energy requirement. The main issue following the adoption of RET into power system network is an electrical islanding. It causes voltage, frequency, current, phase angle, power and harmonic content deviation outside the allowable limits, which may be hazardous to both connected apparatus and customers. It should be noticed within 2 s, according to Distributed Generation (DG) IEEE 1547 interconnection specifications. This research examines several islanding recognition strategies for improving the solidity of utility-connected DG. It will assist potential scholars in choosing the optimum islanding recognition technique with less non-detection zone (NDZ). The comparative assessment of various detection parameters analyzed in terms of NDZ, power quality issues and number of DG networks used. Finally, the sensitivity analysis of twenty passive methods carried out for low power mismatch cases like load switching and different fault cases, and the best methods for future islanding detection are recommended. [ABSTRACT FROM AUTHOR]

Aim: Spatial assessments of Ecosystem Services (ES) are increasingly used in environmental management, but rarely provide information on the prediction accuracy. Uncertainty estimates are essential to provide confidence in the quality and credibility of ES assessments for informed decision making. In marine environments, the need for uncertainty assessments for ES is unparalleled as they are data scarce, poorly (spatially) defined, with complex interconnectivity of seascapes. This study illustrates the uncertainty associated with a principle‐based method for ES modelling by accounting for model variability, data coverage and uncertainty in thresholds and parameters. Location: Tauranga, New Zealand. Methods: A sensitivity analysis was applied on ES models for marine bivalves (Austrovenus stutchburyi and Paphies australis) and their contribution to Food provision, Water quality regulation, Nitrogen removal and Sediment stabilisation. ES estimates from the sensitivity analysis were compared against baseline ES predictions. Spatial uncertainty patterns were analysed for individual ES through bi‐plots and multiple ES through spatial prioritisation using Zonation. Results: Our study showed spatially explicit differences in uncertainty patterns for ES and between species. Food provision had highest maximum uncertainty (>5 points) but also the largest area of high ES and high certainty conditions. Zonation analysis conducted on baseline and conservative ES values showed overall robust outcomes of top 30% area, but important nuances through shifts in top 5% and 10% areas that allowed for a consistently better representation of ES when accounting for uncertainty. Main Conclusions: The spatial prioritisation in combination with the ES uncertainty biplots provide tools for spatial planning of individual and multiple ES to focus on area of highest value with highest certainty and can thereby help reduce risk and aid decision‐making at acceptable confidence levels. This type of information is urgently needed in marine ES assessments and their management, but likewise extends to other environments to improve transparency. [ABSTRACT FROM AUTHOR]

Group testing is an important element of biosecurity operations, designed to efficiently reduce the risk of introducing exotic pests and pathogens with imported agricultural products. Groups of units, such as seeds, are selected from a consignment and tested for contamination, with a positive or negative test returned for each group. These schemes are usually designed such that the probability of detecting contamination is high assuming random mixing and a somewhat arbitrary design prevalence. We propose supplementing this approach with an assessment of the distribution of the number of contaminated units conditional on testing results. We develop beta-binomial models that allow for between-consignment variability in contamination levels, as well as including beta random effects to allow for possible clustering within the groups for testing. The latent beta distributions can be considered as priors and chosen based on expert judgement, or estimated from historical test results. We show that the parameter representing within-group clustering is, unsurprisingly, effectively non-identifiable. Sensitivity analysis can be conducted by investigating the consequences of assuming different values of this parameter. We also demonstrate theoretically and empirically that the estimated probability of a consignment containing contamination and evading detection is almost perfectly robust to mis-specification of the clustering parameter. We apply the new models to large cucurbit seed lots imported into Australia where they provide important new insights on the level of undetected contamination. Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

The rockmass in the practical engineering is often prone to compression; therefore, how to establish a fracture criterion to reflect the rockmass compression failure is vital. Take the rockmass with one inclined crack for instance, the mechanical behavior of the inclined crack face closure and friction sliding in uniaxial compression is firstly analyzed, and then the Kolosov–Muskhelishvili stress function of the cracked rockmass in uniaxial compression is established based on the complex stress functions. Next, the calculation methods of the stress intensity factor (SIF) K and three T‐stress components at or near the crack tip considering three kinds of crack parameters (e.g., geometry parameter, friction strength parameter and deformation parameter) are obtained. Thirdly, the improved maximum tangential stress (MTS) criterion is obtained considering T‐stress and three kinds of crack parameters. Finally, the test results of the wing‐crack initiation angle in uniaxial compression obtained by other researchers are adopted to verify the improved MTS criterion. Meanwhile, the effect of γ, the crack normal stiffness kn and shear stiffness ks, and the crack friction coefficient f on the wing‐crack initiation angle are discussed with the parametric sensitivity analysis. Highlights: An improved maximum tangential stress (MTS) criterion is proposed.The SIF and T‐stress at or near the crack tip in compression are analytical solved.The crack deformation parameters are not seen as constant in the improved MTS criterion. [ABSTRACT FROM AUTHOR]

The regulatory influence of the parathyroid hormone (PTH) is exerted on bone, which serves as a vital reservoir of calcium within the body. While various aspects of bone growth, turnover, and mechanisms operate independently of gonadal hormones, a crucial role is assumed by sex steroids, particularly estrogen, in maintaining bone equilibrium in adults. In order to unravel the underlying mechanisms of bone remodeling mediated by PTH, a distinguished mathematical model of this intricate process is presented. Subsequently, the temporal effects of Plasma PTH and PTH external dosages are investigated using the proposed mathematical model. Among the limited repertoire of approved and accessible anabolic treatments for severe osteoporosis, daily injections of PTH stand out. This pharmaceutical marvel possesses a unique dual action, capable of acting either anabolically or catabolically, contingent upon the mode of administration. The study aims to accurately predict osteogenic responses to introduced and endogenous PTH, incorporating factors such as T G F - β , R A N K L , and bisphosphonates in osteoblast–osteoclast signaling, as well as considering PTH's influence on the gland and the regulatory roles of R u n x 2 , p C R E B , and B c l 2 in osteoblast apoptosis and bone volume effects. Through diverse methods including illustrative depictions, numerical simulations, sensitivity analysis, and stability analysis, this work seeks to comprehend how PTH therapy impacts bone volume, enhancing its therapeutic relevance. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to conduct an in-depth analysis of the intricacies inherent in a gender-structured model depicting the dynamics of HIV–TB coinfection. The model is articulated through a set of nonlinear fractional-order differential equations. Our focus is on elucidating the stability characteristics around equilibrium points, employing advanced fractional stability techniques. We leverage sensitivity analysis as a powerful tool to discern the specific model parameters exerting significant influence on the transmission and control of the coinfection, as gauged by the basic reproduction number. Through this, we aim to identify key factors that drive the spread or containment of the dual affliction. Furthermore, we delve into the realm of optimization by formulating a fractional optimal control problem. The objective is to design and implement two control strategies that effectively mitigate the prevalence of coinfection, demonstrating a state of perfect protection. Notably, our findings reveal that the implementation of control measures at a rate exceeding 80% suffices to eliminate the infection entirely. We anticipate that the insights garnered from this research will prove instrumental for policymakers in crafting strategic interventions to curtail the tragic impact of HIV–TB coinfection. Our scientifically grounded study serves as a foundation upon which comprehensive plans can be devised to stem the devastating consequences of this dual health challenge. [ABSTRACT FROM AUTHOR]

Leptospirosis is a bacteria infection prevalent in many tropical regions, caused by the genus Leptospira. Humans contract the disease by coming in contact with contaminated environments. This study proposes a deterministic mathematical model that links the within-host and between-host dynamics of leptospirosis and investigates its properties. The model's parameters were estimated by fitting it to real-life data using the "lsqcurvefit" package in MATLAB. The study employs global sensitivity analysis using Latin hypercube sampling with a partial rank correlation coefficient index and uses Pontryagin's maximum principle to identify cost-effective solutions for time-dependent intervention strategies to suppress the bacteria transmission within a specific period. The results of the study showed that bacteria replication within human and rodent hosts is the major driver of the overall dynamics of leptospirosis and therefore controlling the prevalence of the disease by focusing on the epidemiology components is necessary but will not be effective if the intra-host dynamics of the bacteria within rodent hosts and human hosts are not attentively considered. [ABSTRACT FROM AUTHOR]

In this paper, we designed a population model that shows how a prey species defends itself against a generalist predator by exhibiting group defence. A non-monotonic functional response is used to represent the group defence functionality. We have demonstrated the model's local stability in the vicinity of the coexisting equilibrium solution employing a local Lyapunov function. Condition for existence of Hopf bifurcation is obtained along with its normal form. The suggested model has been validated by numerical simulations, which have also been used to verify the acquired analytical results. The parameters are subjected to sensitivity analysis by utilizing partial rank correlation coefficient (PRCC) and Latin hypercube sampling (LHS). The Z-type dynamic method is used to prevent population blow-up. [ABSTRACT FROM AUTHOR]

We propose a random forest based estimation procedure for Quantile-Oriented Sensitivity Analysis—QOSA. In order to be efficient, a cross-validation step on the leaf size of trees is required. Our full estimation procedure is tested on both simulated data and a real dataset. Our estimators use either the bootstrap samples or the original sample in the estimation. Also, they are either based on a quantile plug-in procedure (the R-estimators) or on a direct minimization (the Q-estimators). This leads to 8 different estimators which are compared on simulations. From these simulations, it seems that the estimation method based on a direct minimization is better than the one plugging the quantile. This is a significant result because the method with direct minimization requires only one sample and could therefore be preferred. [ABSTRACT FROM AUTHOR]