Uncertainty Profile Research Articles

Simultaneous Quantification of Two Neonicotinoids Using QuEChERS-LC-MS/MS in Moroccan Spearmint (Mentha Spicata.L): Qualimetry of the Method by Uncertainty Estimation Using Generalized Pivotal Quantities Approach and Monte Carlo Simulation.

Neonicotinoids (NEOs) are used for the phytosanitary treatment of Mentha Spicata.L crops, and this practice requires precise control of these harmful substances at very low concentrations. The objective of this study is to apply an approach allowing simultaneously validation and evaluation of measurement uncertainty based on total error methodology, in order to accurately quantify the presence of two NEOs in Mentha Spicata.L utilizing a Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS)-LC-MS/MS methodology. The quantification of imidacloprid and acetamiprid employing a QuEChERS extraction method, coupled with LC-MS/MS, ensuring the accuracy of the analytical method and managing the risks associated with its routine use.A complete and exhaustive validation approach based on the "β-content, γ-confidence" tolerance interval was used for the uncertainty assessment, using the generalized pivot quantity (GPQ) concept and Monte Carlo simulation, which avoids the need for additional data while achieving intermediate precision for each concentration level within predetermined acceptable limits. The validation procedure is based on the choice of a quadratic model for the two NEOs, allowing the validation of acetamiprid and imidacloprid by LC-MS/MS assay within the range of working concentration. The flexibility of the uncertainty profile intervals was demonstrated with a variation in β-content values (66.7, 80, and 90%) and risk values (10 and 5%), which remained within the acceptability limits of 20%, and the relative expanded uncertainty did not exceed 15 and 11%. A QuEChERS-LC-MS/MS method for the analysis of two NEOs has been successfully fully validated using the uncertainty profile strategy. Implementation of an overall validation strategy, which involves both the validation and uncertainty assessment known as the uncertainty profile, for the quantification of two important NEOs in Mentha Spicata.L using QuEChERS-LC-MS/MS. This qualimetric approach has been conducted by computing the measurement uncertainty of the method utilizing data from analytical validation under conditions of intermediate precision at each level of concentration without additional effort. After that we have demonstrated the flexibility of this strategy for the LC-MS/MS quantification of acetamiprid and imidacloprid, using a decision tool that enables the choice and modification of β-content and γ-confidence values.

The Flexibility of β-Content, γ-Confidence Tolerance Intervals to Qualimetry a Simultaneous 22 Aromatic Amines Derived from Azo Dyes in Fabric Using a Sensitive GC-MS Technique.

Azo dyes are among the most widely used dyes in the textile industry, releasing a series of carcinogenic aromatic amines that can be absorbed through the skin. This work aims to show that 22 azo dye amines in a textile matrix can be quantified using a GC-MS method. Based on the notion of total error and β-content, γ-confidence tolerance intervals (β,γ-CCTI), a chemometric approach known as the "uncertainty profile" has been used to completely validate a GC-MS method for the simultaneous assay of 22 azo amines in fabrics. According to International Organization for Standardization (ISO) in ISO 17025 guidelines, analytical validation and measurement uncertainty estimates have evolved to be two main principles for ensuring the accuracy of analytical results and controlling the risk associated with their use. The calculated tolerance intervals allowed for the determination of the uncertainty limits at each concentration level. These limits when compared to the acceptable limits show that a significant portion of the expected outcomes is in conformity. Additionally, the relative expanded uncertainty values, calculated with a proportion of 66.7% and a 10% risk, do not exceed 27.7, 12.2, and 10.9% for concentration levels 1, 15, and 30 mg/L, respectively. The capability and flexibility of the β-content, γ-confidence intervals have been established through the use of this innovative approach to carrying out qualimetry of the GC-MS method depending on the behavior, required conformity proportion, and acceptable tolerance limits of each amine. An efficient GC-MS technique for the simultaneous determination of 22 azo amines in a textile matrix has been developed. Analytical validation using a new strategy based on the uncertainty concept is reported, uncertainty associated to measurement results is estimated, and the applicability of our approach to the GC-MS method is investigated.

An overall validation approach based on β-content, γ-confidence tolerance interval and uncertainty profile: Application for LC-MS/MS quantification of Carbendazim in drinking water.

The Carbendazim is a fungicide which can seep into the water supply, presenting a public health risk, therefore the accurate trace determination of this substance is very important. The purpose of the study is to perform a top-down analytical validation approach in order to determine the amount of Carbendazim in drinking water by using an SPE-LC/MS-MS technique. Quantification of Carbendazim using solid-phase extraction coupled with LC/MS-MS in order to ensure the accuracy of the analytical method and to master the risk of its routine application. An overall validation methodology based on two side tolerance interval type β-content, γ-confidence has been applied for the validation and estimation of uncertainty by building a decision graphical tool called "uncertainty profile" by using the statistical process known as the "Satterthwaite approximation" with no recourse to supplementary data by satisfying intermediate precision condition for each concentration level within the acceptance limits fixed in advance. As a result, the process of the validation is based on the selection of a linear weighted 1/X model enabling validation of the Carbendazim dosage using LC/MS-MS in the range of working concentrations as the β γ-CCTI fell inside acceptable limits of ± 10%, and the relative expanded uncertainty didn't surpass 7% regardless of the β values (66.7%, 80%, 90%) and the 1- γ=risk (10%, 5%). The application of the Uncertainty Profile approach for full validation of a SPE-LC/MS-MS assay for the quantification of carbendazim has been successfully achieved.

Use of an Uncertainty Profile to Validate High-Performance Liquid Chromatography (HPLC) for the Simultaneous Determination of Statins in Synthetic Pharmaceutical Products

Here is reported a full validation of a high-performance liquid chromatography (HPLC) method for the determination of statins in a pharmaceutical product using the uncertainty profile. In addition, the uncertainty estimates based on the β-content, γ-confidence tolerance intervals are compared to those using the β-expectation tolerance interval via a graphical comparison. The results are useful in elucidating the applicability and flexibility of the uncertainty profile, which allows the analyst to choose the desired proportion of the results with a well-determined uncertainty. This approach allowed the determination of the validity of the statin assays. Four of the five statin dosages were shown to be accurate by demonstrating that more than 90% of the outcomes were compliant; however, rosuvastatin’s dosage was valid only by ensuring an 80% compliance rate. The uncertainty profile is a widely applicable strategy that may be used to evaluate analytical methods and compute the uncertainty in order to confirm suitability for use while providing reliable results.

The Uncertainty Profile Used for Full Validation of the HPLC Method to Determine 22 Azo Amines in Fabrics.

Azo dyes are synthetic dyes that can degrade to carcinogenic amines, therefore determining their exact concentration is crucial. The purpose of this study is to demonstrate that an HPLC method may be used to quantify 22 azo dye amines in a textile matrix. The HPLC-DAD (Diode Array Detector) technique for measuring 22 amines in a textile matrix was tested using a new graphical statistical strategy based on the building of β-content, γ-confidence tolerance intervals, which allows for good qualimetry of analytical procedures. In this way, we established the approach's ability to perform a full validation of the analytical technique as well as its ability to estimate measurement uncertainty in a simple and uncomplicated way with minimum effort, utilizing only the data collected during the analytical validation. The findings suggest that this technique can properly assess the validity of the HPLC method for simultaneous determination of 22 amines, with 67-90% of compliance results falling within acceptable limits. Using the uncertainty profile methodology, the full validation of an HPLC method for the determination of 22 azo amines in a textile matrix was successfully established. The development and evaluation of an efficient HPLC technique for the simultaneous determination of 22 azo amines in a textile matrix have been completed. The total reliability of the analytical procedure was established by evaluating its measurement uncertainty at each concentration level using the suggested uncertainty profile approach and confirming proportions 67 to 90% of compliance.

Assessing the sources of uncertainty in supply chain management

AbstractSupply chains are prone to unpredictable events that negatively impact their ability to function effectively. The study shows that worldwide rankings may be used to determine the uncertainty profile of the supply chain in a given country or region. Supply chain uncertainties in Kazakhstan and Russia are due to insufficient transport infrastructure, high‐level corruption, and relatively low ICT adoption. Favorable business conditions give rise to fewer uncertainties and affect supply chain management in a better way.

Porosity Prediction With Uncertainty Quantification From Multiple Seismic Attributes Using Random Forest

AbstractInferring porosity of subsurface from seismic data is of profound significance to many fields of Earth science and engineering applications, including but not limited to: hydrocarbon reservoir characterization, underground water flow modeling, geological CO2 storage, and geothermal energy exploitation. Traditional model‐driven approaches confront the problems of strong nonlinearity and geological heterogeneity, while machine learning is good at nonlinear mapping, providing higher efficiency and accuracy as well. We propose a Random Forest (RF) based method using multiple seismic attributes to predict the underground porosity distribution with uncertainty quantification. The standard deviation of base models' predictions is used to quantify the regression uncertainty of RF. The uncertainty can robustly indicate the prediction quality in numerous experiments, where low uncertainty corresponds to relatively precise prediction and high uncertainty gives a possibility of larger errors. Furthermore, we utilize the quantified uncertainty to improve the RF regression accuracy by correcting the originally predicted porosity according to the statistical relationship between the absolute error and the standard deviation. The application of the proposed method on seismic data shows its potential to characterize spatially varying reservoir parameters, and the quantified uncertainty profile offers insights into risk evaluation for hydrocarbon exploration and development.

Cost-effectiveness of brentuximab vedotin in advanced stage Hodgkin\u2019s lymphoma: a probabilistic analysis

BackgroundTreatment with ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) is a well-established therapy for advanced Hodgkin’s lymphoma (HL). However, the recently completed ECHELON-1 trial showed potential net clinical benefit for brentuximab vedotin (BREN+AVD) compared to ABVD as frontline therapy in patients with advanced Hodgkin’s lymphoma. The objective of this analysis is to determine whether, on current evidence, BREN+AVD is cost-effective relative to ABVD as frontline therapy in patients with advanced HL.MethodsWe constructed a probabilistic Markov model with two arms and six mutually exclusive health states, using six-month cycle lengths, and a 15-year time horizon. Time-dependent transition probabilities were calculated from ‘real-world’ data collected by the BC Cancer’s Centre for Lymphoid Cancer database or from the literature for ABVD. Time-dependent transition probabilities for BREN+AVD were taken from the ECHELON-1 trial. We estimated the incremental cost and effects per patient of each therapy and calculated the incremental cost-effectiveness ratio (ICER). Costs were measured in 2018 Canadian dollars and effects measured in quality-adjusted life years (QALYs). A probabilistic analysis was used to generate a cost-effectiveness acceptability curve (CEAC).ResultsThe incremental cost between standard therapy with ABVD and therapy with BREN+AVD was estimated to be $192,336. The regimen of BREN+AVD resulted in a small benefit in terms of QALYs (0.46 QALYs). The estimated ICER was $418,122 per QALY gained. The probabilistic analysis suggests very few (8%) simulations fall below $100,000 per QALY. Even at a threshold of $200,000 per QALY gained, there was only a 24% chance that BREN+AVD would be considered cost-effective. Sensitivity analyses evaluating price reductions for brentuximab showed that these reductions needed to be in excess of 70% for this regimen to be cost-effective at a threshold of $100,000 per QALY.ConclusionsThere may be a clinical benefit associated with BREN+AVD, but on current evidence the benefit is not adequately substantive compared to ABVD therapy given the cost of brentuximab vedotin. Agencies responsible for making decisions about BREN+AVD as frontline therapy for patients with advanced HL should consider whether they are willing to implement this treatment given the current uncertainty and cost-benefit profile, or negotiate substantial price-reductions from the manufacturer should they choose to reimburse.

Online dating profiles, first-date interactions, and the enhancement of communication satisfaction and desires for future interaction

ABSTRACT This study uses an experimental design to examine the uncertainty-reducing effect of online dating profiles on initial first-date interactions. Zero-history dyads (N = 108) participated in a brief first meeting in the lab, where they were randomly assigned to either a dating profile (experimental) condition or a no-profile control group. Results indicated that profile viewing had an amplification effect on initial interactions. Profiles strengthened the actor effects of eagerness to communicate before the interaction, disclosure during the interaction, and uncertainty and similarity after the interaction on an individual’s own communication satisfaction with the first date. Profiles also intensified the partner effect of nonverbal affiliation during the interaction on communication satisfaction. Implications for uncertainty reduction theory and profile design are discussed.

Application of Design Space, Uncertainty, and Risk Profile Strategies to the Development and Validation of UPLC Method for the Characterization of Four Authorized Phosphodiesterase Type 5 Inhibitors to Combat Counterfeit Drugs.

Counterfeit medicines are an increasing scourge that are difficult to identify and they have become industrialized and widespread through highly organized illegal channels. This research aims to develop a robust method to determine four phosphodiesterase type-5 inhibitors in counterfeit drugs based on ultra-performance liquid chromatography. Experimental design methodology (DOE) and design space (DS) recommended by ICH Q8 were used side-by-side in the development phase to define the optimal parameters as well as the robustness of the chromatographic method. Moreover, both the uncertainty and risk profile derived from the β-content and γ-confidence tolerance interval were investigated during the validation phase to examine the performance of this method. Successful chromatographic results, in a high resolution between the four active ingredients and an optimal analysis time of less than 1.6 min, were achieved at the end of the optimization phase. In addition, validation results show a low risk of future measurements outside acceptance limits set at 5%. Our procedure was successfully applied in the routine phase to identify 23 illicit formulations of an erectile dysfunction drug. An efficient method for the characterization of 4 authorized phosphodiesterase in less than 1.6 min was established. A DS approach was applied to test the performance of this analytical method during analytical development. A risk profile was then carried out to approve the validity of the analytical method through the uncertainty profile approach.

Validation and Measurement Uncertainty Assessment of a Microbiological Method Using Generalized Pivotal Quantity Procedure and Monte-Carlo Simulation.

Recently, a novel and effective statistical tool called the uncertainty profile has been developed with the purpose of graphically assessing the validity and estimating the measurement uncertainty of analytical procedures. One way to construct the uncertainty profile is to compute the β-content, γ-confidence tolerance interval. In this study, we propose a tolerance interval based on the combination of the generalized pivotal quantity procedure and Monte-Carlo simulation. The uncertainty profile has been applied successfully in several fields. However, in order to further confirm its universality, this newer approach has been applied to assess the performance of an alternative procedure versus a reference procedure for counting of Escherichia coli bacteria in drinking water. Hence, the aims of this research were to expose how the uncertainty profile can be powerfully applied pursuant to ISO 16140 standards in the frame of interlaboratory study and how to easily make a decision concerning the validity of the procedure. The analysis of the results shows that after the introduction of a correction factor, the alternative procedure is deemed valid over the studied range because the uncertainty limits lie within the acceptability limits set at ±-0.3 log unit/100ml for a β = 66.7% and γ = 90%.

It's a match! Choosing information processing mechanisms to address sustainability-related uncertainty in sustainable supply management

One of the most pressing challenges associated with sustainable supply management (SSM) is that buying firms cannot openly observe social and environmental conduct in their supply networks, as demonstrated by numerous sustainability scandals in recent years. Using eight in-depth case studies across four industries, we explore how firms manage sustainability-related uncertainties and the resulting information deficits within their upstream supply chain. We build on information processing theory and, more specifically, recent research that distinguished three forms of sustainability-related uncertainties in the supply network. Our study shows how sustainability-related uncertainties can be effectively matched by applying a fitting configuration of six information processing mechanisms. The effective choice of information processing mechanisms is contingent upon the type of sustainability-related uncertainty, but it also depends on additional factors such as economies of scale, path dependencies and trigger urgency as presented in this manuscript. We derive propositions how firms create fit between their information processing needs and capacity by means of choosing suitable information processing mechanisms for SSM. The paper can be used as a blueprint for the development of a SSM capability that accommodates a firm's unique sustainability-related uncertainty profile and the resulting information processing needs.

Development of robust surrogate model for economic performance prediction of oil reservoir production under waterflooding process

Geological uncertainty imposes significant challenge in modeling oil reservoirs. In this paper, a new robust modeling methodology is presented to identify a set of robust surrogate models with unstructured uncertainty for economic performance prediction of an uncertain oil reservoir under waterflooding process. The oil reservoir, being treated as a multi-input, multi-output (MIMO) system in terms of injection inputs and production outputs, is identified within a robust surrogate modeling framework using a frequency-based system identification approach. For this purpose, the concept of unstructured uncertainty model is incorporated with the surrogate modeling in the context of a function block having a norm-bounded uncertainty profile. The identified MIMO surrogate model is integrated with a desired nonlinear net present value (NPV) objective function to synthesize a new modified robust surrogate model in a multi-input, single-output (MISO) configuration form. This new modeling strategy enables direct calculation of economic performance prediction for the target oil reservoir. Finally, applicability of the proposed method is evaluated on “Egg Model” as a three-dimensional synthetic oil reservoir with 8 water injection wells and 4 oil production wells. The results clearly demonstrate that economic performance prediction of the oil reservoir, having uncertain permeability field, lies in the bounds corresponding to the worst-case scenarios of the uncertainty model set.

Full validation using β-content, γ-confidence tolerance interval: Application for LC-MS/MS determination of Doxycycline in human plasma

A statistical strategy for full analytical validation has been applied to ensure the reliability of a bioanalytical method and to control the risk associated with future use. To this end, the applicability of this approach, called uncertainty profile, has been demonstrated by evaluating the liquid chromatography–mass spectrometry/mass spectrometry (LC-MS/MS) bioanalysis, for the determination of Doxycycline in human plasma. This innovative procedure combines two main concepts, namely analytical validation and measurement uncertainty; its aim is to guarantee that a known amount of future results obtained with the bioanalytical method will be within the acceptance limits set beforehand. In addition, this approach allows to calculate the measurement uncertainty of the method without any additional effort, by using data coming from the analytical validation when we respect as best as possible the intermediate precision conditions at each concentration level.Thus, the LC-MS/MS method for the determination of Doxycycline in human plasma was found to be valid in the studied concentrations range since the tolerance intervals of type β-content, γ-confidence fell into the acceptable limits of ±15%, and the relative expanded uncertainty did not exceed 11% of the values of β-proportion and α-risk equal to 90% and 5% respectively.

Uncertainty-Wise Cyber-Physical System test modeling

It is important that a Cyber-Physical System (CPS) with uncertainty in its behavior caused by its unpredictable operating environment, to ensure its reliable operation. One method to ensure that the CPS will handle such uncertainty during its operation is by testing the CPS with model-based testing (MBT) techniques. However, existing MBT techniques do not explicitly capture uncertainty in test ready models, i.e., capturing the uncertain expected behavior of a CPS in the presence of environment uncertainty. To fill this gap, we present an Uncertainty-Wise test-modeling framework, named as UncerTum, to create test ready models to support MBT of CPSs facing uncertainty. UncerTum relies on the definition of a UML profile [the UML Uncertainty Profile (UUP)] and a set of UML Model Libraries extending the UML profile for Modeling and Analysis of Real-Time and Embedded Systems (MARTE). UncerTum also benefits from the UML Testing Profile V.2 to support standard-based MBT. UncerTum was evaluated with two industrial CPS case studies, one real-world case study, and one open-source CPS case study from the following four perspectives: (1) Completeness and Coverage of the profiles and Model Libraries in terms of concepts defined in their underlying uncertainty conceptual model for CPSs, i.e., U-Model and MARTE, (2) Effort required to model uncertainty with UncerTum, and (3) Correctness of the developed test ready models, which was assessed via model execution. Based on the evaluation, we can conclude that we were successful in modeling all the uncertainties identified in the four case studies, which gives us an indication that UncerTum is sufficiently complete. In terms of modeling effort, we concluded that on average UncerTum requires 18.5% more time to apply stereotypes from UUP on test ready models.

The Value of “Bespoke”: Demand Learning, Preference Learning, and Customer Behavior

“Bespoke,” or mass customization strategy, combines demand learning and preference learning. We develop an analytical framework to study the economic value of bespoke systems and investigate the interaction between demand learning and preference learning. We find that it is possible for demand learning and preference learning to be either complements or substitutes, depending on the customization cost and the demand uncertainty profile. They are generally complements when the personalization cost is low and the probability of having high demand is large. Contrary to usual belief, we show that higher demand uncertainty does not necessarily yield more complementarity benefits. Our numerical study shows that the complementarity benefit becomes weaker when customers are more strategic. Interestingly, the substitute loss can occur when the personalization cost is small and the probability of having high demand is large, when customers are strategic. The online supplement is available at https://doi.org/10.1287/mnsc.2017.2771 . This paper was accepted by Serguei Netessine, operations management.

Modeling the impacts of speed limits on uncertain road networks

ABSTRACTIn this paper, we examine the network-wide impacts of speed limits on network uncertainty. A multi-dimensional modeling approach is proposed to investigate the impacts of speed limits on link travel time uncertainty profile, travelers' risk-averse route choice decisions, and network flow reallocation under uncertainty. Methodologically, a truncated travel time probability distribution is proposed and its moments are derived to characterize the impact of speed limit on link travel time uncertainty profile. The mean-excess travel time is then adopted as a risk measure to account for travelers' risk-averse route choice adjustment and network flow reallocation induced by speed limits. We find that a speed limit scheme has different impacts on various dimensions of travel time uncertainty. The shift in travel time uncertainty profile of the directly affected links adjusts the traveler's risk-averse route choice decisions, the travel time uncertainty profile of the indirectly affected links, and consequently the network flow reallocation.

Uncertainty in basic short-term macroeconomic models with angel-daemon games

We propose the use of an angel-daemon framework to perform an uncertainty analysis of short-term macroeconomic models. The angel-daemon framework defines a strategic game where two agents, the angel and the daemon, act selfishly. These games are defined over an uncertainty profile which presents a short and macroscopic description of a perturbed situation. The Nash equilibria on these games provide stable strategies in perturbed situations, giving a natural estimation of uncertainty. We apply the framework to the uncertainty analysis of linear versions of the IS-LM and the IS-MP models.

Uncertainty in basic short-term macroeconomic models with angel-daemon games

We propose the use of an angel-daemon framework to perform an uncertainty analysis of short-term macroeconomic models. The angel-daemon framework defines a strategic game where two agents, the angel and the daemon, act selfishly. These games are defined over an uncertainty profile which presents a short and macroscopic description of a perturbed situation. The Nash equilibria on these games provide stable strategies in perturbed situations, giving a natural estimation of uncertainty. We apply the framework to the uncertainty analysis of linear versions of the IS-LM and the IS-MP models.

Overall uncertainty measurement for near infrared analysis of cryptotanshinone in tanshinone extract

This study presented a new strategy of overall uncertainty measurement for near infrared (NIR) quantitative analysis of cryptotanshinone in tanshinone extract powders. The overall uncertainty of NIR analysis from validation data of precision, trueness and robustness study was fully investigated and discussed. Quality by design (QbD) elements, such as risk assessment and design of experiment (DOE) were utilized to organize the validation data. An “I×J×K” (series I, the number of repetitions J and level of concentrations K) full factorial design was used to calculate uncertainty from the precision and trueness data. And a 27–4 Plackett–Burmann matrix with four different influence factors resulted from the failure mode and effect analysis (FMEA) analysis was adapted for the robustness study. The overall uncertainty profile was introduced as a graphical decision making tool to evaluate the validity of NIR method over the predefined concentration range. In comparison with the T. Saffaj's method (Analyst, 2013, 138, 4677.) for overall uncertainty assessment, the proposed approach gave almost the same results, demonstrating that the proposed method was reasonable and valid. Moreover, the proposed method can help identify critical factors that influence the NIR prediction performance, which could be used for further optimization of the NIR analytical procedures in routine use.