Concept Of Sensitivity Analysis Research Articles

Reliability and Sensitivity Analysis of Wireless Sensor Network Using a Continuous-Time Markov Process

A remarkably high growth has been observed in the uses of wireless sensor networks (WSNs), due to their momentous potential in various applications, namely the health sector, smart agriculture, safety systems, environmental monitoring, military operations, and many more. It is quite important that a WSN must have high reliability along with the least MTTF. This paper introduces a continuous-time Markov process, which is a special case of stochastic process, based on modeling of a wireless sensor network for analyzing the various reliability indices of the same. The modeling has been conducted by considering the different components, including the sensing unit, transceiver, microcontroller, power supply, standby power supply unit, and their failures/repairs, which may occur during their functioning. The study uncovered different important assessment parameters like reliability, components-wise reliability, MTTF, and sensitivity analysis. The critical components of a WSN are identified by incorporating the concept of sensitivity analysis. The outcomes emphasize that the proposed model will be ideal for understanding different reliability indices of WSNs and guiding researchers and potential users in developing a more robust wireless sensor network system.

Estimation of local arterial stiffness using non-invasive flow measurements: a study on a stenosed carotid artery

Arterial stiffness is a well-established independent prognostic bio-marker in the human cardiovascular system (CVS). Despite its value as a predictive bio-marker, the inclusion of vascular stiffness into standard clinical procedures or guidelines is limited. This has come about as a result of the abundance of tools created to estimate and evaluate local arterial stiffness in people. This study employs a non-invasive technique to measure local arterial stiffness in the human carotid arteries using proximal flow measurements. Within this work, a viscoelastic lumped-parameter (0D) model of the carotid arteries is developed in complement with the non-invasive flow measurements obtained from the Doppler ultrasound. Further, concepts of sensitivity analysis and parameter estimation are applied to estimate arterial stiffness using the flow measurement taken at the common carotid artery (CCA). After the successful estimation of arterial stiffness in CCA, results show good agreement between model simulations and actual measurement.

A sustainable dynamic optimization model of pricing and advertising in the presence of green innovation investment

Due to the increasing greenhouse effect and global warming, ecological and environmental protection issues are growing tremendously. The Paris Agreement and other international agreements have been enacted to slow down the increase in carbon emissions to prevent it from degrading the ecosystem. With the increasing consumer awareness of environmental protection and environmental regulatory pressures, firms must integrate carbon emissions concerns into decision-making. This paper analyzes a joint dynamic pricing, green innovation investment, and advertising model under the cap-and-price mechanism. The demand rate is multiplicatively related to the selling price and goodwill. The profit-maximizing problem is considered over an infinite horizon. Specifically, we assume that the stock of goodwill grows with green innovation and advertising efforts but depreciates by a constant proportion each period. Numerous theoretical results have been established to expand our comprehension of the problem. Moreover, we present several explications corresponding to these structural properties, characterizing the effects of critical parameters on optimal decisions. Finally, some numerical illustrations and explanations of the concepts of sensitivity analysis are presented to obtain managerial insights, followed by concluding remarks and future research.

Activity of a Public Company in the Stock Market: Sensitivity Analysis of Its Key Indicators in the Context of a Neutral Approach to the Implementation of Dividend Policy

The article is devoted to the consideration of the conceptual foundations for analyzing the sensitivity of key indicators of the activity of a public company in the stock market (market activity) to the main determining factors in the context of a neutral approach to the implementation of its dividend policy. The methodological basis of the study was the concept of a neutral approach to the dividend policy of a public company and the concept of sensitivity analysis, developed earlier by the author of this article. An analysis of the sensitivity of key indicators to the main determining factors in the context of a neutral approach to the implementation of the dividend policy of a public company involves the construction of appropriate elasticity models that allow determining the change in key indicators of market activity depending on changes in the factors that determine them. The constructed elasticity models of the key indicators of market activity listed above can be used in predictive and analytical assessments of their changes.

Analyzing Sensitivity Analysis Techniques: A Comprehensive Study Across Various Fields

Sensitivity analysis. Users of both simulation and mathematical models are given tools by sensitivity analysis to understand how closely a model's output is related to its input and to determine the relative importance of each input. All application fields are included, including engineering, sociology, and theoretical physics. In order to clarify the structure of the entire "Sensitivity Analysis" section of the Springer Handbook, this introduction paper presents the purposes and objectives of sensitivity analysis. A helpful software programmer, the mathematical notation used in the book papers, certain categorization grids to comprehend the application limits of each method, and the fundamental concepts of sensitivity analysis are also covered. Making decisions can benefit from sensitivity analysis in a number of ways. It first serves as a thorough analysis of all the factors. The predictions might be much more accurate because it's more thorough. Second, it enables decision-makers to pinpoint areas where future improvements can be made. The weighted sum technique is a cross decision-making process; as there are numerous possibilities, there are also many more factors that must be considered before choosing the best one. A weighted or weighted collection of sums is a machine learning strategy that combines predictions from various models, with each model's contribution being weighed according to its capacity or level of expertise. Weighted the with mean evening gown voting ensemble related to this method benefits of using it are ease of use, especially when working with convergent problems, such as when disadvantages an all in solution space make it impossible to find solutions and goals a simple way to ascribe weights there is no way. relative deviation ratio (RDR), partial rank correlation coefficient (PRCC), Standardized regression coefficients (SRC), rank regression coefficient (RRC). Beef cow ingestion rate, Atmospheric concentration, Beef transport time, Biological half-life, breathing rat, Feed-to-meat transfer factor, Feed-to-milk transfer factor, Meat consumption rate. from the result it is seen that Biological half-life, breathing rat and is got the first rank whereas is the Beef transport time got is having the lowest rank. Conclusion: The value of the dataset for Sensitivity analysis technique in GRA (Gray-related analysis) method shows that it results in Biological half-life, breathing rat and top ranking.

Sensitivity Analysis-Based Validation of the Modified NERA Model for Improved Performance

Marijuana is an illicit narcotic with multiple negative health effects as it continues to pose a severe danger to the health of people in emerging regions. Marijuana is spread through interactions between users and non-users. This study utilizes first-order non-linear ordinary differential equations to modify the Non-user, Experimental, Recreational, and Addicted (NERA) users’ model by adding a new class known as the hospitalized class of marijuana smokers. Moreover, this paper focuses on validating the NERA model using the sensitivity analysis concept. The NERA model presented in a previous study for marijuana usage among the target population consists of four distinct stages: the first stage is known as the non-user stage and is represented by the letter N; the second stage is known as the experimental stage of marijuana smoking and is represented by the letter E; the third stage is known as the recreational stage of marijuana smoking and is represented by the letter R; the fourth stage is represented by the letter A; and this last stage is known as the addicted stage of marijuana smoking. The suggested mathematical method achieved the reproduction number () for marijuana use. The results of the sensitivity tests indicate how crucial a variety of variables are to the spread of marijuana. The parameters that have a key impact on marijuana consumption were discovered based on sensitivity analysis. All these procedures, including the reproductive number and invariant region, using the sensitivity analysis concept were utilized to validate the updated model. In this study, the NERA model is modified by utilizing first-order non-linear ordinary differential equations. Furthermore, as demonstrated by the graphical and technique sections, the proposed modified models were successfully validated for improved performance.

Modelling and Simulating Automated Vehicular Functions in Critical Situations—Application of a Novel Accident Reconstruction Concept

Our paper introduces new reconstruction techniques of real-life critical road traffic accidents focusing on highly automated functions. The investigation method presented here focuses on the effect of relevant control parameters and environmental factors following the concept of sensitivity analysis. Two reconstruction tools are applied, the choice depending on the relevant causal factor of the accidents. Our measurement proves that the technical parameters of the control process, like time to collision or braking pressure that affects user satisfaction directly, can significantly influence the probability of accident occurrence. Thus, it is reasonable to consider safety with an increased weight compared to the user experience when identifying these parameters’ values. On the other hand, the effects of the investigated environmental factors were also found to be significant. Accordingly, future ADAS applications need to consider the change of environmental factors in the case of increased risk level, and driver-mode should be adapted to the new situation.

Comparing Multi Criteria Decision Making Methods by Evaluation of Batter’s Current Performance in T20 Internationals

Cricket analytics is the field of collecting cricket data and analysing it which can be used to improve player’s or team’s performance. Here in this paper, Multi Criteria Decision Making methods are applied and batters playing T20 Internationals for past ten years are ranked according to their current performances. These rankings are useful not only to predict best batters but also to guide other batters to adapt their techniques. Criteria used are Runs Per Innings (RPI), Strike Rate (SR), Balls Per Boundary (BPB) and Dot Percentage. Analytic Hierarchy Process (AHP) is used to give weights to these criteria and Weighted Sum Model (WSM), Weighted Product Model (WPM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used to rank the batters. Finally, these methods are compared to check robustness of the output based on the concept of Sensitivity Analysis.

Cavities identification in linear elasticity: energy-gap versus L2-gap cost functionals

We study the geometric inverse problem of cavities identification in linear elasticity equation with partially over-determined boundary data. This work proposes a way to compute the topological derivative based on the topological sensitivity analysis concepts, adopting the energy-gap and the -gap as cost functionals. Then, a comparison of both functionals is presented to determine the best choice for numerical studies. Further, several numerical experiences are shown to explore the efficiency of this method.

Global Sensitivity Analysis Based on Entropy: From Differential Entropy to Alternative Measures.

Differential entropy can be negative, while discrete entropy is always non-negative. This article shows that negative entropy is a significant flaw when entropy is used as a sensitivity measure in global sensitivity analysis. Global sensitivity analysis based on differential entropy cannot have negative entropy, just as Sobol sensitivity analysis does not have negative variance. Entropy is similar to variance but does not have the same properties. An alternative sensitivity measure based on the approximation of the differential entropy using dome-shaped functionals with non-negative values is proposed in the article. Case studies have shown that new sensitivity measures lead to a rational structure of sensitivity indices with a significantly lower proportion of higher-order sensitivity indices compared to other types of distributional sensitivity analysis. In terms of the concept of sensitivity analysis, a decrease in variance to zero means a transition from the differential to discrete entropy. The form of this transition is an open question, which can be studied using other scientific disciplines. The search for new functionals for distributional sensitivity analysis is not closed, and other suitable sensitivity measures may be found.

Transmission and epidemiological trends of pine wilt disease: Findings from sensitivity to optimality

In this work, a deterministic model is dedicatedly studied for the infection mechanism of pine wilt disease subject to varying sensitivity and optimality. We include time dependent controls into the pine wilt disease model and then analysed optimal conditions for the control of infection. Explicit form for the reproduction number has been obtained. Ultimate constant levels of infectious vectors and hosts have been discussed by employing the threshold condition. Two most effective techniques namely Lyapunov functional and graph theoretic have been used to find the final endemic level of population. The concept of full eradication of disease and reduction of constant level has been investigated through the utilization of two effective techniques. Using the concept of sensitivity analysis, control policies have been designed to control the disease. Additionally, the robustness of control plans has been shown graphically on the basis of data collected from open literature.

Maximizing perturbation radii for robust convex quadratically constrained quadratic programs

Under the assumption that uncertain coefficients corresponding to each constraint are perturbed in an ellipsoidal set, we consider the problem of maximizing the perturbation radius of the ellipsoidal set associated to a robust convex quadratically constrained quadratic programming problem to maintain some properties of a pre-decision. To this end, a fractional programming problem is first formulated to solve the problem, and then equivalently reformulated into linear conic programs over positive semi-definite, second-order cones that are solvable in polynomial time. Numerical experiments in connection with the robust Markowitz’s portfolio selection problem are provided to demonstrate the proposed concept of sensitivity analysis. Additionally, certain numerical results are also presented to compare the efficiency of direct solutions of the proposed linear conic programs with that of a bisection method for the corresponding fractional programming problem.

Integrated structural-electromagnetic optimization of cable mesh reflectors considering pattern degradation for random structural errors

To alleviate the effects of random structural errors on the radiation performance and directly guide the structural design of cable mesh reflectors, an integrated structural-electromagnetic optimization procedure is proposed considering the radiation pattern degradation for random structural errors. Based on analytical expressions and the structural sensitivity concept, the radiation pattern is directly expressed as two matrix-form functions with respect to the random structural errors. By applying the pattern calculation into the multidisciplinary design and selecting the average boresight directivity as the objective function, an optimum result with better radiation performance compared with the traditional structural design is obtained. To reveal the fundamentals of the optimum result, the concept of sensitivity analysis of the average boresight directivity with respect to the random structural errors is introduced. The effectiveness and benefits of this study are demonstrated via an offset cable mesh reflector.

MACHINE LEARNING FOR TRAJECTORIES OF PARAMETRIC NONLINEAR DYNAMICAL SYSTEMS

We investigate parameter-dependent nonlinear dynamical systems consisting of ordinary differential equations or differential-algebraic equations. A single quantity of interest is observed, which depends on the solution of a system. Our aim is to determine efficient approximations of the trajectories belonging to the quantity of interest in the time domain. We arrange a set of samples including trajectories of this quantity. A proper orthogonal decomposition of this data yields a reduced basis. Consequently, the mapping from the parameter domain to the basis coefficients is approximated. We apply machine learning with artificial neural networks for this approximation, where the degrees of freedom are fitted to the data of the sample trajectories in a nonlinear optimization. Alternatively, we consider a polynomial approximation, which is identified by regression, for comparison. Furthermore, concepts of sensitivity analysis are examined to characterize the impact of an input parameter on the output of the exact mapping or the approximations from the neural networks. We present results of numerical computations for examples of nonlinear dynamical systems.

The Effect of Correlated Kinetic Parameters on (Bio)Chemical Reaction Networks

AbstractExploiting the information provided by (bio)chemical reaction networks has proved beneficial for process analysis and design. To this end, parameter uncertainties have to be included in the analysis and design of (bio)chemical processes to ensure reliable model‐based results. The goal is to investigate the impact of parameter correlations on (bio)chemical reaction networks and parameter sensitivities. An efficient sensitivity analysis concept is demonstrated with two simulation studies, and the results indicate a significant impact of the parameter correlations on the derived parameter sensitivities and the model‐based results in general.

Multi‐objective optimisation of a permanent magnet flux‐switching motor by combined parameter sensitivities analysis with non‐linear varying‐network magnetic circuit method

In this study, a new sensitivity analysis based on non-linear varying-network magnetic circuit (VNMC) method is proposed for an outer-rotor I-shaped permanent magnet flux-switching motor. By integrating concept of sensitivity analysis into the non-linear VNMC method, the whole design efficiency can be improved effectively by using the comprehensive sensitivity method and sequential non-linear programming algorithm. In the optimisation process, two design objectives are selected, including machine output torque and torque ripple. Based on the sensitivity analysis, the parameters possessing the significant influence on the design objectives can be selected purposely, thus the overall amount of calculation is obviously reduced. After the determination of the sensitive parameters, the rest of optimisation can be realised efficiently by the non-linear VNMC method. Finally, a prototype motor is manufactured and tested. Both theoretical analysis and experimental results confirm the effectiveness of the proposed method.

Moment-Independent Sensitivity Analysis of Enzyme-Catalyzed Reactions with Correlated Model Parameters

The dynamic models used for biological and chemical process analysis and design usually include a significant number of uncertain model parameters. Sensitivity analysis is frequently applied to provide quantitative information regarding the influence of the parameters, as well as their uncertainties, on the model output. Various techniques are available in the literature to calculate parameter sensitivities based on local derivatives or changes in dedicated statistical moments of the model output. However, these methods may lead to an inevitable loss of information for a proper sensitivity analysis and are not directly available for problems with correlated model parameters. In this work, we demonstrate the use of a moment-independent sensitivity analysis concept in the presence and absence of parameter correlations and investigate the correlation effect in more detail. Moment-independent sensitivity analysis calculates parameter sensitivities based on changes in the entire probability density distribution of the model output and is formulated independently of whether the parameters are correlated or not. Technically, a single-loop Monte Carlo simulation method in combination with polynomial chaos expansion is implemented to reduce the computational cost significantly. A sampling procedure derived from Gaussian copula formalism is used to generate sample points for arbitrarily correlated uncertain parameters. The proposed concept is demonstrated with a case study of an enzyme-catalyzed reaction network. We observe evident differences in the parameter sensitivities for cases with independent and correlated model parameters.

Sensitivity and uncertainty analysis on water quality modelling of Aguamilpa reservoir

<p>The use of water quality models is determined to a great extent by their ability to accurately reproduce observed data series and by their predictive capability without the need to adjust the calibrated parameters. However, the observed abiotic variables involved in a system are measured with a level of uncertainty. The sensitivity analysis concepts and generalized methodology of uncertainty analysis were used via a computer tool called UNCSIM. As a first step, a parametric optimization model of the Aguamilpa reservoir water quality was accomplished. The aforementioned analysis identified that the wind sheltering coefficient (WSC), Chezy bottom friction solution (FRICC), and coefficient of bottom heat exchange (CBHE) were the parameters of the CE-QUAL-W2 model that significantly influenced the behaviour of temperature and dissolved oxygen concentration in the reservoir. Afterwards, the uncertainty of the water quality model was evaluated through the modification of the hydrological and climatological information, which had a major influence on the simulation of the system. This analysis showed the possible changes in hydrodynamic and water quality characteristics of the reservoir, including an increase in the thermocline due to a possible air temperature increase and a rainfall decrease in the region. The innovative coupling routine of the different modules for the sensitivity and uncertainty analysis developed in this research establishes the basis for the future development of a modelling platform to conduct water quality simulations of the Aguamilpa reservoir in real time through continuous meteorological and water discharge information.<strong><br clear="all" /> </strong><strong></strong></p>

A Teaching Supplement on Sensitivity Analysis for Linear Programming in Undergraduate Business Programs

Sensitivity analysis, a key linear programming (LP) concept, is often explained in text books using complex problem scenarios to which students have difficulty relating. Consequently, many students do not fully comprehend or appreciate the significance of shadow prices or range of optimality for objective coefficients. This adds to the challenges instructors face in promoting critical thinking, a key goal in operations research and management science courses. Limited student-faculty interactions further exacerbate the problem in online learning environments. These issues can be effectively addressed through use of simple real world examples for instruction, followed by discussion of insights and intuition behind results from these mathematical models. This is the motivation of this study, with a special focus on sensitivity analysis concepts and LP formulations. We then demonstrate its effectiveness by comparing student performance before and after exposure to this innovative teaching supplement and as well as results from a student feedback survey.

Modeling nonlinear phenomena in deforming fluid-saturated porous media using homogenization and sensitivity analysis concepts

Homogenization of heterogeneous media with nonlinear effects leads to computationally complex problems requiring updating local microstructures and solving local auxiliary problems to compute characteristic responses. In this paper we suggest how to circumvent such a computationally expensive updating procedure while using an efficient approximation scheme for the local homogenized coefficients, so that the complexity of the whole two-scale modeling is reduced substantially. We consider the deforming porous fluid saturated media described by the Biot model. The proposed modeling approaches are based on the homogenization of the quasistatic fluid–structure interaction whereby differentiation with respect to the microstructure deformation is used as a tool for linearization. Assuming the linear kinematics framework, the physical nonlinearity in the Biot continuum is introduced in terms of the deformation-dependent material coefficients which are approximated as linear functions of the macroscopic response. These functions are obtained by the sensitivity analysis of the homogenized coefficients computed for a given geometry of the porous structure which transforms due to the local deformation. The deformation-dependent material coefficients approximated in this way do not require any solving of local microscopic problems for updated configurations. It appears that difference between the linear and nonlinear models depends significantly on the specific microstructure of the porous medium; this observation is supported by numerical examples.