Effective Management Of Uncertainty Research Articles

A Semantic Digital Twin for the Dynamic Scheduling of Industry 4.0-based Production of Precast Concrete Elements

Precast concrete construction enhances project efficiency, sustainability, and durability by leveraging a controlled production environment that ensures high-quality outputs. Still, the sequential nature of off-site production, encompassing casting, curing, and storage of the precast elements, is subject to significant uncertainties, including supply chain variability and environmental factors affecting curing times. Dynamic adjustments to the production schedule are essential for aligning with real-time changes, necessitating a robust framework for real-time data acquisition and analysis. Dynamic Scheduling (DS) is a responsive and adaptive approach that accommodates real-time changes, optimizes the production flow, and minimizes downtime or delays. However, the DS approach demands real-time data to quickly and efficiently respond to unforeseen challenges, which requires acquiring and analyzing production-relevant data throughout the production process. The Digital Twin (DT) emerges in Industry 4.0 (I4.0) as a bridge between physical operations and digital capabilities, enabling a seamless flow of information, for which the Asset Administration Shell (AAS) is a reference implementation. This study introduces a DS framework to optimize precast element production, utilizing a DT for real-time data aggregation across the production system. The framework implements a Semantic DT based on the AAS and the Linked Data approach. It employs Resource Description Framework (RDF) serialization of the AAS and an ontological representation of the production system for data integration. The framework leverages a simulation-based scheduler, which exchanges data with the DT in a Service-oriented Architecture (SoA) using SPARQL, a language for querying and updating graph databases. The approach is evaluated through a proof of concept, demonstrating effective uncertainty management in a dynamic production environment.

The challenges of access to innovative medicines with limited evidence in the European Union.

The European Medicines Agency (EMA) fosters access to innovative medicines through accelerated procedures and flexibility in the authorization requirements for diseases with unmet medical needs, such as many rare diseases as well as oncological diseases. However, the resulting increase of medicines being marketed with conditional authorizations and in exceptional circumstances has lead to higher clinical uncertainty about their efficacy and safety than when the standard authorizations are applied. This uncertainty has significant implications for clinical practice and the negotiation of pricing and reimbursement, particularly as high prices are based on assumptions of high value, supported by regulatory prioritization. The burden of clinical development is often shifted towards public healthcare systems, resulting in increased spending budgets and opportunity costs. Effective management of uncertainty, through appropriate testing and evaluation, and fair reflection of costs and risks in prices, is crucial. However, it is important not to sacrifice essential elements of evidence-based healthcare for the sake of access to new treatments. Balancing sensitive and rational access to new treatments, ensuring their safety, efficacy, and affordability to healthcare systems requires thoughtful decision-making. Ultimately, a responsible approach to timely access to innovative medicines that balances the needs of patients with healthcare systems' concerns is necessary. This approach emphasizes the importance of evidence-based decision-making and fair pricing and reimbursement.

Toward Effective Uncertainty Management in Decision-Making Models Based on Type-2 Fuzzy TOPSIS

Over the past century, there has been a dramatic increasing interest in the multi-criteria group decision-making (MCGDM) technique, with a considerable amount of studies published regarding it. One of the well-known approaches in the MCGDM paradigm is Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The integration of the TOPSIS method with fuzzy set theory has proven to be successful in various applications. Recently, a wide array of publications has proposed implementing a type-2 fuzzy set with TOPSIS. However, the additional degree of uncertainty represented by type 2 has largely been ignored, especially in a few specific mathematical operations in the model. We propose constructing interval type-2 fuzzy membership functions (IT2 MFs) using interval-based data gathered from a survey, where this is used to generate a new scale to represent ratings for each alternative. This procedure utilized all information gathered from decision makers. In addition, we present a complete algorithm for TOPSIS based on IT2 fuzzy sets (IT2 FSs) which preserve the interval-based form output. The output in the form of intervals offers decision makers (DMs) with more detailed information, enabling them to make more nuanced decisions. This can include cautious decisions when intervals are wider and overlapping. Although understanding the exact meaning of these intervals and their widths in a decision-making context is challenging, this paper introduces a systematic method for connecting input uncertainty to output uncertainty in the TOPSIS technique. This approach establishes a solid foundation for future research. Thus far, no other researchers have suggested a data-driven method that combines TOPSIS with fuzzification and provides intervals as the final output.

Dynamic adaptive engineering pathways for mitigating flood risks in Shanghai with regret theory

Uncertainty in sea level rise and future extreme climate events presents a great planning challenge for flood defence in coastal mega cities like Shanghai. While academic literature has largely focused on uncertainty analysis, engineering solution design requires effective uncertainty management. Here we incorporate the regret theory of economics and decision science into the dynamic-adaptation-pathways framework and assess the impacts of high rates of changes on the flood defence systems in Shanghai. Specific options are developed to manage flooding on the Huangpu River from tidal water levels, river flows, rainfall, drainage inflows and combinations of these flood sources including sea level rises of up to 3 m. Dynamic adaptation pathways are developed where the timing of tipping points from one intervention to the next depends on the actual changes in sea level, rainfall and other variables that affect the future design. This framework is potentially applicable for planning ‘no regrets’ flood-defence systems in other low-lying coastal cities.

A New Belief-Based Bidirectional Transfer Classification Method.

In pattern classification, we may have a few labeled data points in the target domain, but a number of labeled samples are available in another related domain (called the source domain). Transfer learning can solve such classification problems via the knowledge transfer from source to target domains. The source and target domains can be represented by heterogeneous features. There may exist uncertainty in domain transformation, and such uncertainty is not good for classification. The effective management of uncertainty is important for improving classification accuracy. So, a new belief-based bidirectional transfer classification (BDTC) method is proposed. In BDTC, the intraclass transformation matrix is estimated at first for mapping the patterns from source to target domains, and this matrix can be learned using the labeled patterns of the same class represented by heterogeneous domains (features). The labeled patterns in the source domain are transferred to the target domain by the corresponding transformation matrix. Then, we learn a classifier using all the labeled patterns in the target domain to classify the objects. In order to take full advantage of the complementary knowledge of different domains, we transfer the query patterns from target to source domains using the K-NN technique and do the classification task in the source domain. Thus, two pieces of classification results can be obtained for each query pattern in the source and target domains, but the classification results may have different reliabilities/weights. A weighted combination rule is developed to combine the two classification results based on the belief functions theory, which is an expert at dealing with uncertain information. We can efficiently reduce the uncertainty of transfer classification via the combination strategy. Experiments on some domain adaptation benchmarks show that our method can effectively improve classification accuracy compared with other related methods.

From Offline to Online: Understanding Chinese Single Mothers' Uncertainty Management in Interpersonal and Online Contexts.

Divorced and unwed single motherhood is heavily stigmatized in Chinese cultural context, preventing Chinese single mothers from actively seeking the information and support needed and negatively impacting their wellbeing. Drawing on the theory of motivated information management (TMIM), this study tested how perceived stigma and cultural norms influenced Chinese single mothers’ search for information and social support from families, friends as well as from online communities. Using two-wave data collected from 226 single mothers, findings support the utility of the TMIM in explaining information management and support seeking behaviors and contribute to situating the TMIM process within larger socio-cultural contexts. Practical implications regarding how to facilitate more effective uncertainty management and enhance Chinese single mothers’ wellbeing in interpersonal vs. online contexts are discussed.

Robust analysis for premium-reserve models in a stochastic nonlinear discrete-time varying framework

The effective management of uncertainty and complexity in premium pricing and reserve accumulation processes provide new challenges to the decision- and policy-makers. In this regard, the implementation of complex mathematical tools and advanced statistical techniques is highly acquired. Over the last three decades, methodologies and applications of robust control theory to finance and economics have received strong attention among researchers and practitioners. However, relatively scant research has been carried out so far in relation to insurance. This paper proposes a stochastic, nonlinear time-varying premium-reserve (P-R) system with Lipschitz-type conditions in discrete-time to explore P-R system’s stability, stabilization and H∞-control properties. In our case, as an extension of the classical quadratic condition, the one-side Lipschitz conditions are also considered and a nonconvex feasibility problem is formulated and solved. In addition, both robust stochastic stability and a pre-specified disturbance attenuation level can be guaranteed. Finally, numerical examples are presented to illustrate the applicability of theoretical treatment.

The decision-oriented world: effective management of uncertainty in geomodelling workflows

Given a realistic set of sub-surface data, the number of plausible geological models that can be constructed can run into the millions or tens of millions. It is therefore impractical for a geologist to examine the entire space of possible models and thereby correctly characterize uncertainty. Commonly applied approaches to this problem use simplifications such as experimental design, parameter sensitivity analysis or Monte Carlo simulation to attempt to reasonably account for input uncertainty while balancing computational expenditure and time. Broadly, this is undertaken by assessing which model inputs (for example porosity or permeability) have the most significant impact on response variables of choice, typically volume based or commercial metrics such as net present value (NPV) or estimated ultimate recovery (EUR). Specific parameters are then perturbed to produce an assumed representative subset (in many cases a low, mid and high case) of the range of possible models (see for example Pyrcz and Deutsch, 2014).

Robust Analysis for Premium-Reserve Models in a Stochastic Nonlinear Discrete-Time Varying Framework

The effective management of uncertainty and complexity in premium pricing and reserve accumulation processes provide new challenges to the decision- and policy- makers. In this regard, the implementation of complex mathematical tools and advanced statistical techniques is highly acquired. Over the last three decades, methodologies and applications of robust control theory to finance and economics have received strong attention among researchers and practitioners. However, relatively scant research has been carried out so far in relation to insurance. This paper proposes a stochastic, nonlinear time-varying premium-reserve (P-R) system with Lipschitz- type conditions in discrete-time to explore P-R system’s stability, stabilisation and Hoo-control properties. In our case, as an extension of the classical quadratic condition, the one-side Lipschitz conditions are also considered and a nonconvex feasibility problem is formulated and solved. In addition, both robust stochastic stability and a pre-specified disturbance attenuation level can be guaranteed. Finally, numerical examples are presented to illustrate the applicability of theoretical treatment.

Management of uncertainty in the diagnosis communication of psychogenic nonepileptic seizures in a South African context

The process of communicating a diagnosis of psychogenic nonepileptic seizures (PNES) is an integral part of the treatment process. Many international studies have therefore focused on the PNES diagnosis communication process, but to date, none with a specific focus on the South African context. This current study considered the factors that influence the patient's experience of uncertainty and the strategies employed to manage that uncertainty within the provider–patient communication. This was considered from the healthcare provider's point of view, within the specific context of diagnosis communication. We conducted 13 semi-structured interviews with providers, eliciting their perceptions related to the communication of a PNES diagnosis to patients. Data were analyzed using thematic analysis, and themes were grouped according to the main tenets of the interpersonal health communication theory of uncertainty management, which included, the experience, appraisal, and management of uncertainty.The results revealed medical, personal, and social forms of uncertainty. The social sources of uncertainty, which include challenges related to the South African healthcare system, cultural, and language variability within the South African context, as well as possible stigmatization, seemed to have the biggest influence not only on the other areas of uncertainty, but was also indicated as a barrier to effective uncertainty management by providers.Providers in this study identified the importance of building the provider–patient relationship and ensuring patient understanding, as the main strategies used to reduce uncertainty. There was some evidence to suggest varied emotional appraisals of uncertainty by patients, but because of the subjective nature of this information, further research would be needed to confirm these findings.These findings suggest that as providers, one cannot apply a one-size-fits-all approach when aiding in uncertainty management. Furthermore, it is pertinent to remain cognizant of the social realities of the South African context and its impact on the patient's uncertainty experience. More research is needed to understand patients' perceptions of uncertainty management within the context of PNES diagnosis communication, and how they align with the perceptions of the providers provided here.

Application of Dempster-Shafer theory of evidence model to geoelectric and hydraulic parameters for groundwater potential zonation

The application of a GIS – based Dempster – Shafer data driven model named as evidential belief function EBF- methodology to groundwater potential conditioning factors (GPCFs) derived from geophysical and hydrogeological data sets for assessing groundwater potentiality was presented in this study. The proposed method’s efficacy in managing degree of uncertainty in spatial predictive models motivated this research. The method procedural approaches entail firstly, the database containing groundwater data records (bore wells location inventory, hydrogeological data record, etc.) and geophysical measurement data construction. From the database, different influencing groundwater occurrence factors, namely aquifer layer thickness, aquifer layer resistivity, overburden material resistivity, overburden material thickness, aquifer hydraulic conductivity and aquifer transmissivity were extracted and prepared. Further, the bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training and 30% (9 wells) for model testing. The synthesized of the GPCFs via applying the DS – EBF model algorithms produced the groundwater productivity potential index (GPPI) map which demarcated the area into low – medium, medium, medium – high and high potential zones. The analyzed percentage degree of uncertainty for the predicted lows potential zones classes and mediums/highs potential zones classes are >10% and <10%, respectively. The DS theory model-based GPPI map’s validation through ROC approach established prediction rate accuracy of 88.8%. Successively, the determined transverse resistance (TR) values in the range of 1280 and 30,000 Ω my for the area geoelectrically delineated aquifer units of the predicted potential zones through Dar – Zarrouk Parameter analysis quantitatively confirm the DS theory modeling prediction results. This research results have expand the capability of DS – EBF model in predictive modeling by effective uncertainty management. Thus, the produced map could form part of decision support system reliable to be used by local authorities for groundwater exploitation and management in the area.

Communication and the Appraisal of Uncertainty: Exploring Parents’ Communication with Credible Authorities in the Context of Chronic Childhood Illness

ABSTRACTIndividuals with chronic illnesses must manage long-term uncertainty as they cope with the ways the illness influences their lives. In the context of pediatric illnesses, parents must manage uncertainty during the diagnosis and treatment of their child’s illness. It is common for children with complex chronic illnesses to see multiple specialists for the treatment of their condition. While previous research has explored parents’ uncertainty during a child’s diagnosis and during end-of-life care, less is known about these experiences when the child is referred to a team of specialists for treatment. The aim of the current study was to explore how specialists, as credible authorities, influence parents’ uncertainty during parents’ first visit to a multidisciplinary clinic for the care of their child’s complex chronic illness. Data were collected through semi-structured interviews with 29 parents after their child’s first visit to a vascular anomaly clinic at a large Midwestern children’s hospital. The results suggest parents’ communication with credible authorities facilitates effective uncertainty management primarily through the mechanism of uncertainty reappraisal. The results also suggest that specialists, as credible authorities, are a key mechanism in the appraisal of uncertainty for conditions that are often misdiagnosed and mismanaged.

Supportive culture for efficient project uncertainty management

PurposeThe purpose of this paper is to study the effectiveness of current uncertainty management practice in projects with a special focus on the organization's cultural dimension.Design/methodology/approachThe empirical data were obtained using in‐depth interviews with project management professionals in three project‐oriented organizations in Norway, Statsbygg, Telenor and the Norwegian Defence Logistic Organization. All the respondents from these three organizations are people who actively work with projects and uncertainty management.FindingsThe study results show that a supportive uncertainty management culture is characterized by: positive attitude, commitment of time and resources, openness and respect, understanding of uncertainty management, uncertainty management internalized into daily work, senior managers asking for and using uncertainty information, proactive uncertainty management, a focus on opportunities, clear areas of responsibility, accepted and operationalized policy and terminology, and a holistic uncertainty view. Moreover, the interviews revealed that commitment, knowledge, communication, openness, and trust are factors that contribute to building a supportive uncertainty management culture.Research limitations/implicationsFuture research should investigate other organizations, types of projects, and countries, so that these findings may be generalized.Practical implicationsThis paper concludes that a supportive culture is important for achieving effective uncertainty management in projects. Uncertainty management practice will run more smoothly, there will be less problems and benefits of the uncertainty management activities will be more easily achieved.Originality/valueA supportive organizational culture for creating a well‐performing management of uncertainties in projects is studied in this research paper.

Are you my guest or my child? Mothers' uncertainties in interacting with their returnee children in China

In a rapidly globalizing world that allows freer temporary movement between nations, issues of entry into new national spaces and reentry into previous ones are common adaptation processes for many individuals and families. This study explores the experiences of mothers in China interacting with their returnee children during their short-term reentry after several years of studying abroad. The analysis of data from qualitative interviews shows that mothers experienced uncertainties about how to understand and interact with their “culturally transformed” children and that they tried to manage such uncertainties through discourse. Since reentry experiences are conjointly produced by returnees and their significant others through social interaction, effective uncertainty management through more open and direct communication may be conducive to resolving relational conflicts during reentry. Implications and limitations of the study are discussed.

Bayesian Value of Information Analysis with Linear, Exponential, Power Law Failure Models for Aging Chronic Diseases

The effective management of uncertainty is one of the most fundamental problems in medical decision making. According to the literatures review, most medical decision models rely on point estimates for input parameters. However, it is natural that they should be interested in the relationship between changes in those values and subsequent changes in model output. Therefore, the purpose of this study is to identify the ranges of numerical values for which each option will be most efficient with respect to the input parameters. The Nonhomogeneous Poisson Process(NHPP) was used for describing the behavior of aging chronic diseases. Three kind of failure models (linear, exponential, and power law) were considered, and each of these failure models was studied under the assumptions of unknown scale factor and known aging rate, known scale factor and unknown aging rate, and unknown scale factor and unknown aging rate, respectively. In addition, this study illustrated developed method with an analysis of data from a trial of immunotherapy in the treatment of chronic Granulomatous disease. Finally, the proposed design of Bayesian value of information analysis facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert's opinions and the sampling information which will furnish decision makers with valuable support for quality medical decision making.

Quantification of epistemic and aleatory uncertainties in level-1 probabilistic safety assessment studies

There will be simplifying assumptions and idealizations in the availability models of complex processes and phenomena. These simplifications and idealizations generate uncertainties which can be classified as aleatory (arising due to randomness) and/or epistemic (due to lack of knowledge). The problem of acknowledging and treating uncertainty is vital for practical usability of reliability analysis results. The distinction of uncertainties is useful for taking the reliability/risk informed decisions with confidence and also for effective management of uncertainty. In level-1 probabilistic safety assessment (PSA) of nuclear power plants (NPP), the current practice is carrying out epistemic uncertainty analysis on the basis of a simple Monte-Carlo simulation by sampling the epistemic variables in the model. However, the aleatory uncertainty is neglected and point estimates of aleatory variables, viz., time to failure and time to repair are considered. Treatment of both types of uncertainties would require a two-phase Monte-Carlo simulation, outer loop samples epistemic variables and inner loop samples aleatory variables. A methodology based on two-phase Monte-Carlo simulation is presented for distinguishing both the kinds of uncertainty in the context of availability/reliability evaluation in level-1 PSA studies of NPP.

Communication in the management of uncertainty: The case of persons living with HIV or AIDS

Communication is a means of managing uncertainty. In a state of uncertainty: (a) information seeking can reduce uncertainty by allowing for better discrimination between or among alternatives; (b) information seeking can increase uncertainty by increasing the number of alternatives, or by blurring the distinction between or among alternatives; and (c) information avoidance can maintain uncertainty. Individuals living with HIV or AIDS, like many other chronically‐ill or terminally‐ill individuals, must manage high levels of uncertainty about their illness. Participants in a focus group study of persons with HIV or AIDS reported effective uncertainty management, including managing uncertainty that was challenging, managing uncertainty that was essential for maintaining hope, learning to live with chronic uncertainty, and managing information problems. New information can serve uncertainty management even if it fails to reduce the number or ambiguity of alternatives, because new information can invite a reappraisal of uncertainty. A theory of uncertainty management based on these findings is offered.

The role of artificial intelligence in non-destructive testing and evaluation : Rajagopalan, C.; Raj, B.; Kalyanasundaram, P. Insight, Vol. 38, No. 2, pp. 118–123 (Feb. 1996

The paper defines artificial intelligence (AI) and identifies two primary areas in AI, namely Knowledge-Based Systems (KBS) and Artificial Neural Networks (ANN), that are of immediate relevance to Non-destructive Testing and Evaluation (NDT&E). The drawbacks of present-day AI programmes are presented. A suitable architecture, an effective uncertainty management and learning methodologies are proposed to overcome these limitations, in order to develop versatile and useful KBS for NDT&E.

Uncertainty management for rule-based systems with applications to image analysis

In image analysis, there have been few effective procedures that deal with a wide class of imagery acquired by different sensors under different environmental conditions. The success of a given classification algorithm is dependent upon pattern familiarity, background, and the image acquisition process. Thus, with the inaccuracies in the acquisition process, as well as incomplete or incorrect knowledge about the pattern classes, one cannot place complete confidence in the classifier outcome. There has been increasing success in making decisions under such uncertain conditions by using a rule-based approach with effective uncertainty management, which involves identifying the causes of uncertainty and developing mathematical models for the same. These are incorporated into the rule structure so that the result would be a set of choices or decisions, with a set of associated certainty values or confidences. This paper proposes a "unified" methodology to combine the uncertainties associated with evidence for a given proposition, which is then systematically propagated down the decision tree. The relative importance of propositions as well as the rules themselves have also been considered. Finally, the methodology has been applied to an ATR problem and the results, when compared to some existing methods, show the overall effectiveness of this approach.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

INFORMATION PROCESSING IN STRATEGIC ALLIANCE BUILDING: A MULTIPLE‐CASE APPROACH*

Strategic alliance building has proliferated in many industries in recent years. This research focuses on the dynamic aspects of the alliance building process that have been neglected in previous research. the study examines how organizations process information during strategic alliance building and how the effective management of uncertainty and equivocality is linked to alliance success. Analysis of qualitative and quantitative data from a multi‐case study leads to a proposed model of information processing in strategic alliance building and research propositions. the research propositions suggest that strategic alliance success is facilitated or impeded by a number of factors including the selection of information‐processing mechanisms, the management of alliance building momentum, political activity, and information‐processing structure. These factors collectively form information‐processing environments that are linked to the success or failure of the alliance. Successful alliance building efforts seem to require adaptive information‐processing environments that alter information‐processing mechanisms to match information‐processing needs.