Degree Of Uncertainty Research Articles

Drug development advances in human genetics-based targets.

Drug development is a long and costly process, with a high degree of uncertainty from the identification of a drug target to its market launch. Targeted drugs supported by human genetic evidence are expected to enter phase II/III clinical trials or be approved for marketing more quickly, speeding up the drug development process. Currently, genetic data and technologies such as genome-wide association studies (GWAS), whole-exome sequencing (WES), and whole-genome sequencing (WGS) have identified and validated many potential molecular targets associated with diseases. This review describes the structure, molecular biology, and drug development of human genetics-based validated beneficial loss-of-function (LOF) mutation targets (target mutations that reduce disease incidence) over the past decade. The feasibility of eight beneficial LOF mutation targets (PCSK9, ANGPTL3, ASGR1, HSD17B13, KHK, CIDEB, GPR75, and INHBE) as targets for drug discovery is mainly emphasized, and their research prospects and challenges are discussed. In conclusion, we expect that this review will inspire more researchers to use human genetics and genomics to support the discovery of novel therapeutic drugs and the direction of clinical development, which will contribute to the development of new drug discovery and drug repurposing.

Abstract TMP56: Accuracy of Dual-Energy Computed Tomography for Differentiation Between Cerebral Hemorrhage and Contrast Extravasation Following Mechanical Revascularization in Acute Ischemic Stroke: A Diagnostic Accuracy Meta-Analysis

Background: Mechanical revascularization has become an essential treatment for acute ischemic stroke patients. Post-treatment imaging frequently displays hyperdense regions, prompting concerns regarding possible cerebral hemorrhage. Dual-energy computed tomography (DECT), as an advanced imaging modality, offers the potential to discern between hemorrhage and harmless contrast leakage. Aim: This study aims to assess the diagnostic precision of DECT in distinguishing cerebral hemorrhage from contrast extravasation post-mechanical revascularization in acute ischemic stroke cases. Methods: A comprehensive search was performed for PubMed, EMBASE, and the Cochrane Library until January 2023. We focused on studies that focused on the diagnostic efficacy of DECT for this specific application. R software (version 4.0.3) coupled with mada package facilitated the analyses pooling metrics such as sensitivity, specificity, false-positive rate estimates, diagnostic odds ratio, and positive and negative Likelihood Ratios. These metrics were reported with a 95% Confidence Interval (CI). Results: Our analysis included 5 studies involving 241 patients. DECT demonstrated a pooled sensitivity of 94.5% (95% CI: 25.9% to 99.9%, I 2 =0%) and a specificity of 99% (95% CI: 77.3% to 100%, I 2 =0%). The false-positive rate was minimal at 1% (95% CI: 0% to 22.7%). The positive likelihood ratio was calculated as 98.94 (95% CI: 3.34 to 2929.22), while the negative likelihood ratio was 0.05 (95% CI: 0.001 to 2.21). The diagnostic odds ratio was substantial at 1795.17 (95% CI: 10.04 to 321055.24), signifying a robust performance of DECT in making the differentiation. Conclusion: While DECT manifests impressive accuracy in differentiating cerebral hemorrhage from contrast extravasation after mechanical revascularization in acute ischemic stroke cases, the wide confidence intervals signify a degree of uncertainty in these estimates. Nevertheless, given its high diagnostic metrics, DECT can substantially influence post-intervention evaluations, paving the way for more informed clinical decisions and potentially reducing patient adverse outcomes. However, further studies with larger sample sizes might help refine these diagnostic estimates.

Child Health Needs and the Pediatric Hospital Medicine Workforce: 2020-2040.

Pediatric hospital medicine (PHM) established a new model of care for hospitalized children in the United States nearly 3 decades ago. In that time, the field experienced rapid growth while distinguishing itself through contributions to medical education, quality improvement, clinical and health services research, patient safety, and health system leadership. Hospital systems have also invested in using in-house pediatricians to manage various inpatient care settings as patient acuity has accelerated. National PHM leaders advocated for board certification in 2014, and the first certification examination was administered by the American Board of Pediatrics in 2019. In this article, we describe the development of the subspecialty, including evolving definitions and responsibilities of pediatric hospitalists. Although PHM was not included in the model forecasting future pediatric subspecialties through 2040 in this supplement because of limited historical data, in this article, we consider the current and future states of the workforce in relation to children's health needs. Expected challenges include potential alterations to residency curriculum, changes in the number of fellowship positions, expanding professional roles, concerns related to job sustainability and burnout, and closures of pediatric inpatient units in community hospitals. We simultaneously forecast growing demand in the PHM workforce arising from the increasing prevalence of children with medical complexity and increasing comanagement of hospitalized children between pediatric hospitalists and other subspecialists. As such, our forecast incorporates a degree of uncertainty and points to the need for ongoing investments in future research to monitor and evaluate the size, scope, and needs of pediatric hospitalists and the PHM workforce.

A collapse risk assessment method for subway foundation pit based on cloud model and improved Dempster–Shafer evidence theory

Collapse is a major engineering hazard in open-cut foundation pit construction, and risk assessment is crucial for considerably reducing engineering hazards. This study aims to address the ambiguity problem of qualitative index quantification and the failure of high-conflict evidence fusion in risk assessment. Thus, a fast-converging and high-reliability multi-source data fusion method based on the cloud model (CM) and improved Dempster–Shafer evidence theory is proposed. The method can achieve an accurate assessment of subway pit collapse risks. First, the CM is introduced to quantify the qualitative metrics. Then, a new correction parameter is defined for improving the conflicts among evidence bodies based on conflict degree, discrepancy degree and uncertainty, while a fine-tuning term is added to reduce the subjective effect of global focal element assignment. Finally, the risk assessment result is obtained according to the maximum affiliation principle. The method is successfully applied to Luochongwei Station, where the difference between the maximum value and the second largest value of the basic probability assignment is 0.624, and the global uncertainty degree is 0.087. Both values satisfy the decision evaluation condition; however, values of other methods only satisfy one or neither condition. In addition, the proposed method requires only four cycles to reach the steady state by fusing data of the same index, which has faster convergence compared with that of other methods. The proposed method has good universality and effectiveness in subway pit collapse risk assessment.

Can dialogic eventness be created?

ABSTRACT Turning educational ideas into meaningful learning requires planning for uncertainty. This article presents Bakhtin’s key concept of eventness as an essential aspect of defining dialogic space. To Bakhtin an event is something happening here and now, with a degree of open-ended uncertainty tied to what will happen next. Acknowledging this uncertainty—or eventness—might help researchers and practitioners grasp and support the spirit of dialogue in activities aiming for genuine student participation, which is a core concept in recent curricular reforms in Norway. This article discusses eventness as a hub for dialogic principles, illustrated by examples from student group conversations on literature in Norwegian lower secondary school. The challenge of creating dialogic space is discussed with reference to Bakhtin’s general idea of authorship.

Unraveling data from an idea management system of 11 radical innovation portfolios: key lessons and avenues for artificial intelligence integration

In strategic and radical innovation, the degree of uncertainty and the amount of complexity is much higher compared to ‘business as usual’. Therefore, idea management systems are often used to support such innovation processes. An interesting question is what we can learn from studying data in such idea management systems and what potential implications we can derive from the innovation management literature. In this study, we were allowed to access and analyze data from the same idea management system used in 11 radical innovation projects from the years 2012–2018. Our analysis unravels 8 findings that in different ways nuance or challenge current research on innovation management. Finally, we discuss how the integration of artificial intelligence (AI) in idea management systems can support innovation team members in increasing the innovation potential of the ideas that are elaborated.

Tracheostomy and long-term invasive ventilation decision-making in children: A scoping review.

An increasing number of children are surviving critical illnesses requiring tracheostomy/long-term ventilation (LTV). This scoping review seeks to collate the available evidence on decision-making for tracheostomy/LTV in children. Systematic searches of electronic databases and websites were conducted for articles and reports. Inclusion criteria included: (1) children 0-18 years old; (2) described use of tracheostomy or tracheostomy/LTV; and (3) information on recommendations for tracheostomy decision-making or decision-making experiences of family-caregivers or health care providers. Articles not written in English were excluded. Of the 4463 records identified through database search and other methods, a total of 84 articles, 2 dissertations, 1 book chapter, 3 consensus statement/society guidelines, and 8 pieces of grey literature were included. Main thematic domains identified were: (1) legal and moral standards for decision-making; (2) decision-making models, roles of decision-makers, and decisional aids towards a shared decision-making model; (3) experiences and perspectives of decision-makers; (4) health system and society considerations; and (5) conflict resolution and legal considerations. A high degree of uncertainty and complexity is involved in tracheostomy/LTV decision-making. There is a need for a standardized decision-support process that is consistent with a child's best interests and shared decision-making. Strategies for optimizing communication and mechanism for managing disputes are needed.

Triangular intuitionistic fuzzy linear system of equations with applications: an analytical approach

This study extended an existing semi-analytical technique, the Homotopy Perturbation Method, to the Block Homotopy Modified Perturbation Method by solving two n × n crisp triangular intuitionistic fuzzy (TIF) systems of linear equations. In the original system, the coefficient matrix is considered as real crisp, while the unknown variable vector and right hand side vector are regarded as triangular intuitionistic fuzzy numbers. The Block Homotopy Modified Perturbation Method is found to be efficient and practical to solve n × n TIF linear systems as it only requires the non-singularity of the n × n TIF linear system's coefficient matrix, whereas the point Homotopy Perturbation Method and other classical numerical iterative methods typically require non-zero diagonal entries in the coefficient matrix. A set of theorems relevant to this study are presented and demonstrated. We solve an engineering application, i.e. a current flow circuit problem that is represented in terms of a triangular intuitionistic fuzzy environment, using the suggested method. The unknown current is then obtained as a triangle intuitionistic fuzzy number. The proposed semi-analytic method is used to solve some numerical test problems in order to validate their performance and efficiency in comparison to other existing techniques. The numerical results of the example are displayed on graphs with different degrees of uncertainty. The efficiency and accuracy of the proposed method are further demonstrated by comparisons to block Jacobi, Adomain Decomposition method, Successive Over-Relaxation method and the classical Gauss-Seidel numerical method.

Decision support based on performance data using the analytic hierarchy process without expert judgement

Goal: This article proposes a decision model based on the Analytic Hierarchy Process that allows carrying out the evaluation of alternatives in a multicriteria problem, without expert judgement. Design / Methodology / Approach: The algorithm is based on AHP. The novelty is the transformation of a performance data matrix into pairwise evaluation matrices, instead of using experts’ judgement. Results: The algorithm was applied in a defense procurement problem for the choice of a light 4x4 vehicle for amphibious operations. The results allowed ranking the 17 models based on catalog data. Limitations of the investigation: the algorithm depends on the availability of catalog data, not always available in open sources in the defense industry. Practical implications: Decision support involves several activities in Operations Management and AHP has been frequently applied to solve problems in this sector. The proposed algorithm allows performing deterministic or probabilistic evaluations, depending on the degree of uncertainty and precision involving the systems’ performance data. These assessments are composed of scenarios to facilitate decision making. Originality / Value: AHP typically uses experts for pairwise judgments. However, human judgment is subject to outcomes that involve bias and cognitive distortions. Few studies have modeled the AHP without experts, even so they used human judgment in some part of the process. The approach proposed here does not require human judgment and returns two different results, based on the database precision. This new approach gives decision makers a different perspective and can alter the final choice.

Mental health in selected MENA countries during COVID-19: an empirical investigation

PurposeThe outbreak of COVID-19 not only had serious negative impacts on the world economy but also on the global mental health because of the psychological disorders associated with the spread of the pandemic, the increased degree of uncertainty and the unprecedented measures taken by different countries to face the pandemic’s spread. This paper analyses the mental health well-being of individuals in selected MENA countries (Jordan, Morocco, Tunisia and Egypt) during the pandemic.Design/methodology/approachThe study employs a pooled OLS model using the Economic Research Forum (ERF) COVID-19 MENA Monitor Survey panel dataset collected during 2020 and 2021.FindingsThe findings show that there is no association between the mental health of individuals in the selected countries and their age, gender, family size, marital status, receipt of social support and participation in care work. Mental health improved at higher levels of education, being employed, being a rural area resident and living in Morocco or Tunisia compared to living in Jordan while it worsened as income declined, food insecurity and anxiety about being infected with Covid-19 increased, being a resident in camps, and during waves 4 and 5. Based on these results, it is recommended that suitable financial, physical and human resources should be directed towards the provision of mental health care services in the region. Also, mental health care services should be accessible to different population groups, with a special focus towards the most vulnerable since they are more prone to mental illnesses, especially during health crises and economic shocks. This should be accompanied by increasing awareness about the provided services and reducing stigma against mental illnesses. Furthermore, introduction of policies targeted towards reducing food insecurity and income instability can play a key role in enhancing mental well-being.Originality/valueAlthough few papers have previously investigated the impact of COVID-19 on mental health in MENA countries, most of them have focused on a country-level analysis and adopted a gender perspective. Hence, this paper aims at exploring the association between mental health well-being and socio-economic factors in selected MENA countries during the pandemic.

Lessons learned and lingering uncertainties after seven years of chronic wasting disease management in Norway

Chronic wasting disease (CWD) is well known among cervids in North America. Nevertheless, management faced different types and degrees of uncertainty when CWD was first detected in reindeer Rangifer tarandus in Nordfjella, Norway in 2016. We present a timeline of the efforts to control CWD, and identify how the process, measurement, environmental, and implementation uncertainties developed from the onset (2016) to the current situation (2023) after seven years of CWD management. In the ‘acute' phase (2016–2019), political ambitions were high and depopulation of the Nordfjella reindeer area involving marksmen aimed at eradicating CWD. Subsequently, increased surveillance and increased male harvest was used to enable early detection or to achieve ‘freedom‐from‐CWD' status of the adjacent populations. The second phase (2020‐now) came when cases were detected in the large reindeer population in Hardangervidda. Management authorities postponed culling using marksmen, signifying an important change, with more emphasis on socio‐political acceptance and consideration of the negative long‐term consequences of conflicts with local stakeholders. The subsequent dialogue processes between scientists and local management ended in joint advice. However, the Ministry set aside all advice in 2022, halting further actions, after pressure and negative media attention. During this period, there was no clear research plan to increase knowledge of CWD to reduce process uncertainty; however, large surveillance investments were made to reduce measurement uncertainty. Despite this, detecting and estimating CWD among reindeer at low prevalence remains a key challenge. Governance challenges have emerged as significant implementation uncertainties, partly due to the uncertain occurrence of CWD.

German Media Discourses and Public Perceptions on Hydrogen Imports: An Energy Justice Perspective

While aiming to become a global pioneer in green hydrogen, import of hydrogen is indispensable for meeting the German hydrogen demand. Public perception and acceptance of a changing energy landscape are crucial for enabling a hydrogen economy in Germany. The societal discourse surrounding hydrogen imports carries significant importance as it provides a lens through which to assess the alignment of these developments with Sustainable Development Goals and justice principles. This study sheds light on potential synergies or conflicts resulting from socioeconomic implications of hydrogen imports. With a multimethod approach, media discourses and public perceptions of hydrogen import are assessed. This study presents results from a media analysis of N = 37 newspaper articles and a representative survey (N = 2752). Media representation of hydrogen imports focuses on discussed motives for imports and conditions for partnerships with potential countries, whereas implications for exporting countries receive little attention. The survey results on public perceptions of Power‐to‐X technologies show support for renewables as a basis for imports. Yet, there is still a high degree of uncertainty in the assessment, expressed by a high number of “don't know” answers within the survey. The study highlights the need for stronger science communication and dialogue on hydrogen import strategies.

Baby cry recognition based on WOA-VMD and an improved Dempster–Shafer evidence theory

Background and objectiveConflict may happen when more than one classifier is used to perform prediction or classification. The recognition model error leads to conflicting evidence. These conflicts can cause decision errors in a baby cry recognition and further decrease its recognition accuracy. Thus, the objective of this study is to propose a method that can effectively minimize the conflict among deep learning models and improve the accuracy of baby cry recognition. MethodsAn improved Dempster–Shafer evidence theory (DST) based on Wasserstein distance and Deng entropy was proposed to reduce the conflicts among the results by combining the credibility degree between evidence and the uncertainty degree of evidence. To validate the effectiveness of the proposed method, examples were analyzed, and applied in a baby cry recognition. The Whale optimization algorithm-Variational mode decomposition (WOA-VMD) was used to optimally decompose the baby cry signals. The deep features of decomposed components were extracted using the VGG16 model. Long Short-Term Memory (LSTM) models were used to classify baby cry signals. An improved DST decision method was used to obtain the decision fusion. ResultsThe proposed fusion method achieves an accuracy of 90.15% in classifying three types of baby cry. Improvement between 2.90% and 4.98% was obtained over the existing DST fusion methods. Recognition accuracy was improved by between 5.79% and 11.53% when compared to the latest methods used in baby cry recognition. ConclusionThe proposed method optimally decomposes baby cry signal, effectively reduces the conflict among the results of deep learning models and improves the accuracy of baby cry recognition.

Extension of a conditional performance score for sample size recalculation rules to the setting of binary endpoints

BackgroundSample size calculation is a central aspect in planning of clinical trials. The sample size is calculated based on parameter assumptions, like the treatment effect and the endpoint’s variance. A fundamental problem of this approach is that the true distribution parameters are not known before the trial. Hence, sample size calculation always contains a certain degree of uncertainty, leading to the risk of underpowering or oversizing a trial. One way to cope with this uncertainty are adaptive designs. Adaptive designs allow to adjust the sample size during an interim analysis. There is a large number of such recalculation rules to choose from. To guide the choice of a suitable adaptive design with sample size recalculation, previous literature suggests a conditional performance score for studies with a normally distributed endpoint. However, binary endpoints are also frequently applied in clinical trials and the application of the conditional performance score to binary endpoints is not yet investigated.MethodsWe extend the theory of the conditional performance score to binary endpoints by suggesting a related one-dimensional score parametrization. We moreover perform a simulation study to evaluate the operational characteristics and to illustrate application.ResultsWe find that the score definition can be extended without modification to the case of binary endpoints. We represent the score results by a single distribution parameter, and therefore derive a single effect measure, which contains the difference in proportions pI-pC\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_{I}-p_{C}$$\\end{document} between the intervention and the control group, as well as the endpoint proportion pC\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_{C}$$\\end{document} in the control group.ConclusionsThis research extends the theory of the conditional performance score to binary endpoints and demonstrates its application in practice.

A Quantum Oscillator Model of Stock Markets

Classical oscillators have long been used to model financial time series, but suffer from the drawback that the underlying system does not behave like a mechanical spring: there are no regular oscillations, and price is not a well-defined mechanical quantity but always has a degree of uncertainty. In recent years a number of authors have attempted to address these problems by basing their models on quantum harmonic oscillators, which always have a non-zero volatility, however the role of other features that are characteristic of quantum systems, such as discrete energy levels, has not been clear. This paper develops a quantum model in which the energy level corresponds to an integer number of transactions. The model is derived by quantizing entropic forces which represent the intentions of buyers and sellers to transact as a function of price. It is shown that the model captures the non-Gaussian nature of financial statistics, and correctly predicts empirical phenomena including the square-root law of price impact, along with its associated variance.

Uncertainty in humanities network visualization

Network visualization is one of the most widely used tools in digital humanities research. The idea of uncertain or “fuzzy” data is also a core notion in digital humanities research. Yet network visualizations in digital humanities do not always prominently represent uncertainty. In this article, we present a mathematical and logical model of uncertainty as a range of values which can be used in network visualizations. We review some of the principles for visualizing uncertainty of different kinds, visual variables that can be used for representing uncertainty, and how these variables have been used to represent different data types in visualizations drawn from a range of non-humanities fields like climate science and bioinformatics. We then provide examples of two diagrams: one in which the variables displaying degrees of uncertainty are integrated/pinto the graph and one in which glyphs are added to represent data certainty and uncertainty. Finally, we discuss how probabilistic data and what-if scenarios could be used to expand the representation of uncertainty in humanities network visualizations.

Local employment multipliers for large publicly subsidized firms: Evidence from a synthetic control approach

AbstractThis paper provides estimates of local employment multipliers from large, publicly subsidized firms. We use a synthetic control weighted difference‐in‐difference estimation procedure that matches treated areas with comparison areas to generate local employment multiplier estimates. We show that local employment multiplier estimates have a high degree of uncertainty, with a wide range of point estimates (both positive and negative) and varying degrees of statistical significance. There is a concentration of positive employment multipliers from manufacturing facilities, but little correlation between estimated multipliers and subsidy value. We demonstrate that our approach produces drastically different results than a traditional difference‐in‐difference approach.

Transformative changes in human resources management practices in the tourism sector amidst the COVID-19 pandemic

The COVID-19 pandemic has introduced a significant degree of uncertainty into the operational landscape of businesses within the tourism sector. These uncertainties have raised questions about how working conditions have evolved and may continue to change in the future. This phenomenon has not only had a widespread impact on the travel industry, but it has also extended its influence to the education sector, consequently affecting numerous other sectors in the broader economy. Within this context, the role of departments such as Human Resources (HR) has gained increasing prominence. These departments are entrusted with the crucial responsibility of monitoring workplace standards, particularly in terms of compliance with hygiene regulations and health procedures. The research was conducted in the accommodation service sector of Northern Cyprus (TRNC), encompassing businesses operating in various regions. A sample size of 20 was selected, with data collected from five different facilities situated across the Northern part of the island. This research seeks to shed light on the evolving landscape of human resources management practices in response to the transformative changes brought about by the COVID-19 pandemic. To achieve this objective, a qualitative research approach was employed. Oral interviews with business executives supplemented this strategy, giving the research findings more depth and context. The culmination of this study is presented in the final section, encapsulating the results and their implications. This research contributes to the ongoing discourse surrounding the impact of the COVID-19 pandemic on human resources, shedding light on the evolving landscape of work in the tourism sector.

Random/Cognitive Hybrid Uncertainty Analysis of Ship Multitasking Cabin Layout

Identifying the optimal cabin layout is an important way to improve the efficiency of ship systems and ensure the efficient circulation of personnel and materials. The ship task state refers to the state maintained by the joint actions of ship machinery and equipment, cargo, and personnel when facing different jobs and tasks during operation. A cabin layout that facilitates multitasking states can improve the efficiency of collaboration between systems and ensure the operation of the ship. The demand for human flow and logistics is different in multitasking states. To express the demand in mathematical form, there is a certain random uncertainty in the numerical quantification of the demand. Thus, to better meet the needs of different states, the coefficient values of each state can be integrated using special methods. Ensuring values’ initial preference to the greatest extent inevitably produces a degree of cognitive uncertainty. Therefore, uncertainty analysis is necessary for cabin layout design to be used for multitasking states. In this paper, a deterministic optimization platform of cabin layout in multitasking states is obtained. Adjacent and circulating strength coefficients are obtained through numerical quantization of the demand for human flow and logistics. The random uncertainty in the input values of two coefficients was represented by random variables, and the cognitive uncertainty was represented by interval variables. In order to solve the problem of two types of variables, a random-interval hybrid uncertainty model was established. Through random intervalization and interval randomization, three cases, of random variables, interval variables, and random variables and interval variables, were studied. The probability distribution of the model function was used to evaluate the influence of different compositions of uncertainty parameters on the robustness of the cabin layout scheme. The necessity and effectiveness of uncertainty analysis in multitasking cabin layout are discussed below.

A Bayesian Kalman filter algorithm for quantifying estimation uncertainty of track irregularity on bridges with randomness in system parameters

Vehicle on-board monitoring methods use responses of running vehicles to identify track irregularity in real time. But the parameters of both the bridges and the vehicles in such processes may have a non-negligible degree of uncertainty or randomness that will inevitably lead to uncertainty in the track irregularity identification. Quantifying such uncertainty is a great challenge as the vehicle-bridge system (VBS) to be treated with is time-dependent and random. This paper proposes an on-board track irregularity identification algorithm that realizes an inverse random dynamic analysis of the VBS to quantify estimation uncertainty by using a Bayesian Kalman filter technology. The proposed algorithm utilizes sigma point sets to simulate the random parameters and the noises, and is therefore capable of implementing high-fidelity probability density propagation in the nonlinear state transfer and observation functions describing the VBS. The proposed algorithm is validated by numerical examples with various running states and parameter uncertainty levels.