Residual Uncertainty Research Articles

Environmental Fate and Sustainable Management of Pesticides in Soils: A Critical Review Focusing on Sustainable Agriculture

Pesticides are inevitable agrochemicals employed as plant protection agents and their application follows good agricultural practice (GAP). Although pesticides are primarily used for plant protection purposes, the residual pesticides may pose a threat to the next crops and/or off-target biota. Another important aspect of applied pesticides is the transformation into toxic metabolites. As a result, misuse or overuse of pesticides can lead to raised residual uncertainty, hidden risk of transformed metabolites, and potential risk to off-target biota. As per pesticide safety guidelines, regulations for the maximum limit of residual pesticides, addressing toxic metabolites derived from parent pesticides, and managing the potential risk of pesticides for off-targets are considered vital components. Despite the countable number of studies that have already been published on pesticide fate, residual risk, and metabolism in soils and plants, several vital research gaps remain untouched. In this study, the vital research gap of pesticide fate and transport is explored through vital keyword searches, followed by sorting of relevant articles using scholarly search engines. According to the study outcomes, residual uncertainty, secondary pollution, diversified fate and transport, and toxic metabolites, including their persistence, were detected as key research pitfalls. Thus, this paper critically addresses the current trends and research gaps and suggests specific recommendations for pesticide fate and potential risk studies.

Energy-energy correlation in the back-to-back region at N3LL+NNLO in QCD

We consider the energy-energy correlation (EEC) function in high-energy electron-positron annihilation to hadrons. In the back-to-back (two-jet) region, we perform the all-order resummation of the logarithmically enhanced contributions in QCD perturbation theory up to next-to-next-to-next-to-leading logarithmic (N3LL) accuracy. Away from the back-to-back region, we consistently combine resummed predictions with the known fixed-order results up to next-to-next-to-leading order (NNLO), and we are able to obtain an accurate fit of the O(αS3) remainder function from the numerical QCD computation of the full spectrum. All perturbative terms up to order αS3 are included in our calculation, and a nontrivial cross-check in the back-to-back region is obtained by comparing the soft collinear effective theory analytic calculation against the corresponding numerical QCD computation. In particular, the values of the O(αS3) resummation coefficients have been numerically verified. We regularize the Landau singularity of the QCD coupling within the so-called minimal prescription, and we discuss the reduction of the perturbative scale dependence of distributions at higher orders, as a means to estimate the corresponding residual perturbative uncertainty. Finally, after introducing within a dispersive approach nonperturbative power corrections, we are able to obtain an accurate description of experimental data at the LEP and SLC accelerators. Published by the American Physical Society 2024

Residual extropy in ranked set sampling: Properties, comparative analysis, and estimation

Ranked set sampling (RSS) has gained significant significance in numerous practical domains, such as environmental and ecological studies, and statistical genetics. This article focuses on investigating the concept of extropy as a measure of residual uncertainty in RSS. It is devoted to discussing various monotone properties and characterization results associated with the residual extropy of RSS. Additionally, a comparative analysis is conducted between the residual extropy of RSS and its counterpart in simple random sampling. Optimal minima and maxima values for the residual extropy of RSS are determined. To further contribute to the field, a consistent estimator for the residual extropy of RSS is proposed. The effectiveness of this estimator is demonstrated through three illustrative examples, highlighting its performance in practical scenarios.

Robust deadbeat predictive current control for unipolar sinusoidal excited SRM with multi-parameter mismatch compensation

In the control of unipolar sinusoidal excited switched reluctance motors (SRMs), deadbeat predictive current controllers (DPCCs) have gained attention for their enhanced dynamic performance. However, periodic disturbances caused by mismatches between the predictive model’s nominal and actual system parameters degrade the control performance of SRMs. To address this issue, a robust DPCC with multi-parameter compensation is proposed to improve dq0-axes current control performance. By analyzing the impact of parameter mismatches, a Kalman filter (KF) is developed to compensate for inductance coefficient mismatches, mitigating periodic disturbances. Additionally, a disturbance estimator with measurement noise suppression is integrated into the DPCC for both state and disturbance estimation to handle residual uncertainties, including winding resistance mismatches, magnetic saturation, and unmodeled dynamics. Compared simulation and experimental results validate the effectiveness of the proposed robust DPCC.

Observer-Based Finite-Time Prescribed Performance Sliding Mode Control of Dual-Motor Joints-Driven Robotic Manipulators with Uncertainties and Disturbances

Considering system uncertainties (e.g., gear backlash, unmodeled dynamics, nonlinear friction and parameters perturbation) coupling disturbances weaken the motion performance of robotic systems, an observer-based finite-time prescribed performance sliding mode control with faster reaching law is proposed for robotic manipulators equipped with dual-motor joints (DMJs). In the case where the backlash information is completely unknown, the backlash is maximally eliminated using a simple but efficient dual-motor adaptive anti-backlash strategy. Thus, the design of position tracking controllers for DMJs can be simplified. Then, to deal with the influence of disturbances and residual uncertainties (excluding backlash), a novel finite-time adaptive sliding mode disturbance observer (ASMDO) is proposed to practically estimate the lumped uncertainties where their upper bounds are assumed to be unknown. Finally, a finite-time composite fast non-singular terminal sliding mode (TSM) controller, integrated with the prescribed performance principle, is proposed in this paper. To enhance the convergence rate, a novel TSM-type reaching law has been developed. The controller ensures that the tracking error is not only stabilized within a finite-time convergence rate but also adheres to a predefined maximum transient-steady-state error. The proposed scheme is implemented through simulation and experimental results, demonstrating its superior performance.

Safety Assessment on Isolated Cannabidiol (CBD) as a Novel Food for Use in Food Supplements (RP427)

An application was submitted to the Food Standards Agency (FSA) and Food Standards Scotland (FSS) in February 2021 from EIHA Projects GmbH (“the applicant”) for the authorisation of isolated cannabidiol (CBD) as described in RP427, as a novel food. This is a joint application from EIHA associated product partners pertaining to a novel food which is to be prepared and used according to the specification set out within this document. It must be noted that any authorisation subsequent to this application pertains solely to the novel food and not to any proposed uses as an ingredient in any other products. The novel food is a >98% pure, hemp-derived CBD isolate which is intended to be used in hemp oil as a food supplement for adults. For CBD a provisional Acceptable Daily Intake (ADI) of 10 mg/day for a healthy 70kg adult has been published by the FSA and was considered in assessing this novel food. The provisional ADI (section 2.7) was recommended, subject to the existing advice that certain populations do not consume CBD, due to remaining data gaps and residual uncertainties concerning the safety of CBD for these groups of consumers. These contraindications would also apply to this novel food. To support the FSA and FSS in their evaluation of the application, the Advisory Committee on Novel Foods and Processes (ACNFP) were asked to review the safety dossier and supplementary information provided by the applicant. The Committee did not consider any potential health benefits or claims arising from consuming the food, as the focus of the novel food assessment is to ensure the food is safe, and not putting consumers at a nutritional disadvantage. The Committee concluded that the applicant had provided sufficient information to assure the novel food, an isolated CBD as detailed in application RP427, was safe under the proposed conditions of use. The anticipated intake levels and the proposed use in food supplements was not considered to be nutritionally disadvantageous.

Investigating motivations to eat: Refining and validating a framework of Eating Behaviour Traits in dieters and the general population

Due to relationships between diet and health including obesity, there is a need to examine the explanatory power of factors that motivate people to (over or under) eat. In a previous investigation, a four-factor subscale-based model of eating behaviour traits (EBTs) was developed which identified individual differences in psychological factors influencing motivations to eat and some residual uncertainties. The current study used a data-driven and theory-driven approach, including individual items to refine and extend previous EBT models. The aim was to examine and validate the domain structure of a framework for EBTs. The analysis used two samples including a representative sample of the UK population (n = 2010, 51% female, 49% male, 18–88 years), and members of a weight management program (n = 2317, 96.6% female, 2.8% male, 21–84 years), who completed 5 questionnaires including 10 EBTs. The results found some support for a 6-factor model, encompassing reactive eating, negative emotional eating, positive emotional eating, restricted eating, homeostatic eating, and body-food choice congruence (data-driven model) or eating for health (theory-driven model). There were differences between the data-driven model and the theory-driven model regarding the 6th factor. Additionally, the data-driven model did not distinguish between eating for pleasure and reactive eating. The models demonstrated that the eating behaviour factors were significantly associated with BMI category. Overall, this research contributes to a more structured understanding of the dimensions of motivation underlying EBTs, emphasising the utility of this framework for identifying at-risk individuals and tailoring interventions to meet specific individual needs.

Safety Assessment: Synthetic Cannabidiol (CBD) as a Novel Food for Use in Food Supplements

“An application was submitted to the Food Standards Agency (FSA) and Food Standards Scotland (FSS) in January 2021 from Chanelle McCoy CBD Ltd (“the applicant”) for the authorisation of synthetic cannabidiol (CBD) as a novel food. The novel food is a synthetic >98% pure form CBD which is intended to be used as food supplements for adults. For CBD a provisional Acceptable Daily Intake (ADI) of 10 mg/day has been published by the FSA and was considered in assessing this novel food. The provisional ADI (section 2.7) was recommended, subject to the existing advice to consumers that pregnant and breastfeeding women and people taking any prescription medication should avoid the consumption of CBD. Consumers on regular medications should seek advice from a medical professional before using any type of CBD food product. In addition, children and prospective parents trying for a baby are advised against consumption of CBD, as are those who are immunosuppressed, due to remaining data gaps and residual uncertainties concerning the safety of CBD for these groups of consumers. These contraindications would also apply to this novel food. To support the FSA and FSS in their evaluations of the application, the Advisory Committee on Novel Foods and Processes (ACNFP) were asked to review the dossier and supplementary information provided by the applicant. Please note the Committee did not consider any potential health benefits or claims arising from consuming the food, as the focus of the novel food assessment is to ensure the food is safe, and not putting consumers at a nutritional disadvantage. The FSA and FSS concluded based on the advice of the ACNFP, that the applicant had provided sufficient information to assure the novel food, synthetic CBD, was safe under the proposed conditions of use. The anticipated intake levels and the proposed use in foods and food supplements was not considered to be nutritionally disadvantageous. The views of the ACNFP have been taken into account in the regulatory assessment which represents the opinions of the FSA and FSS. ”

Safety Assessment: Cannabidiol (CBD) Isolate as a Novel Food for Use in a Range of Food Categories Including Food Supplements

“An application was submitted to the Food Standards Agency (FSA) and Food Standards Scotland (FSS) in February 2021 from Cannaray Brands Ltd (“the applicant”) for the authorisation of cannabidiol (CBD) isolate as a novel food. The novel food is a >98% pure form CBD isolate which is intended to be used as a food ingredient in food supplements, beverages, and confectionary for adults. For CBD a provisional Acceptable Daily Intake (ADI) of 10 mg/day has been published by the FSA and was considered in assessing this novel food. The provisional ADI (section 2.7) was recommended, subject to the existing advice to consumers that pregnant and breastfeeding women and people taking any prescription medication should avoid the consumption of CBD. Consumers on regular medications should seek advice from a medical professional before using any type of CBD food product. In addition, children and prospective parents trying for a baby are advised against consumption of CBD, as are those who are immunosuppressed, due to remaining data gaps and residual uncertainties concerning the safety of CBD for these groups of consumers. These contraindications would also apply to this novel food. To support the FSA and FSS in their evaluations of the application, the Advisory Committee on Novel Foods and Processes (ACNFP) were asked to review the dossier and supplementary information provided by the applicant. Please note the Committee did not consider any potential health benefits or claims arising from consuming the food, as the focus of the novel food assessment is to ensure the food is safe, and not putting consumers at a nutritional disadvantage. The FSA and FSS concluded based on the advice of the ACNFP, that the applicant had provided sufficient information to assure the novel food, CBD isolate, was safe under the proposed conditions of use. The anticipated intake levels and the proposed use in foods and food supplements was not considered to be nutritionally disadvantageous. The views of the ACNFP have been taken into account in the regulatory assessment which represents the opinions of the FSA and FSS. ”

Whose Judgement? Reflections on Elicitation in Bayesian Analysis

Bayesian statistical, risk, and decision analyses require that one addresses many uncertainties and preferences, modelling those that can be with subjective probabilities and utilities, perhaps supported by sensitivity explorations. Subjective probabilities need eliciting either in their entirety or partially via prior distributions that are updated in the light of data during the analysis. Some uncertainties, however, are not easily modelled probabilistically, either because they are deep or because they relate to uncertainties in the modelling process itself. Preferences also require elicitation, a process which in many cases constructs these by contextualising broader values to the issues at hand. We discuss broader issues of elicitation without getting into specific details of the elicitation process. We also briefly discuss communication because elicitation sets the context for all subsequent communications to the problem owners and stakeholders. In particular, we emphasise the need for the problem owners to be fully acquainted with all the residual uncertainties at the end of the analysis, not just those captured quantitatively within the modelling. Moreover, we also consider whose uncertainties and preferences should be elicited and addressed by the analysis, arguing that the answer may be different in the varied contexts of Bayesian statistical, risk, and decision analyses. Moreover, the model may be constructed from a synthesis of several people’s judgements.

Benchmarking proton RBE models

Objective. To biologically optimise proton therapy, models which can accurately predict variations in proton relative biological effectiveness (RBE) are essential. Current phenomenological models show large disagreements in RBE predictions, due to different model assumptions and differences in the data to which they were fit. In this work, thirteen RBE models were benchmarked against a comprehensive proton RBE dataset to evaluate predictions when all models are fit using the same data and fitting techniques, and to assess the statistical robustness of the models. Approach. Model performance was initially evaluated by fitting to the full dataset, and then a cross-validation approach was applied to assess model generalisability and robustness. The impact of weighting the fit and the choice of biological endpoint (either single or multiple survival levels) was also evaluated. Main results. Fitting the models to a common dataset reduced differences between their predictions, however significant disagreements remained due to different underlying assumptions. All models performed poorly under cross-validation in the weighted fits, suggesting that some uncertainties on the experimental data were significantly underestimated, resulting in over-fitting and poor performance on unseen data. The simplest model, which depends linearly on the LET but has no tissue or dose dependence, performed best for a single survival level. However, when fitting to multiple survival levels simultaneously, more complex models with tissue dependence performed better. All models had significant residual uncertainty in their predictions compared to experimental data. Significance. This analysis highlights that poor quality of error estimation on the dose response parameters introduces substantial uncertainty in model fitting. The significant residual error present in all approaches illustrates the challenges inherent in fitting to large, heterogeneous datasets and the importance of robust statistical validation of RBE models.

Multi-Channel Phase-Compensated Active Disturbance Rejection Control with an Improved Backstepping Strategy for Electro-Optical Tracking Systems

A multi-channel phase-compensated active disturbance rejection control (MPADRC) incorporating an improved backstepping strategy is proposed in this paper to handle the phase lag in the extended state observer (ESO) and the residual uncertainty in the system. Firstly, a multi-channel phase-compensated ESO (MPESO) is constructed by adding phase-advanced networks to all output channels of the ESO, which allows disturbances and system states to be compensated and feedback in a more timely manner, respectively. Then, to estimate and offset the residual uncertainty in the system, an improved backstepping control method is employed and a Lyapunov function is designed to verify the convergence of the error between the estimated and actual values of the residual uncertainty. After that, the improved backstepping control is combined with MPADRC, and comparisons with the conventional linear active disturbance rejection control (LADRC) are conducted for a range of cases. Finally, on an inertial stabilization platform in the electro-optical tracking system (ETS), simulation and experimental results verified the effectiveness of the proposed method.

Denoising of Geochemical Data using Deep Learning–Implications for Regional Surveys

Regional geochemical surveys generate large amounts of data that can be used for a number of purposes such as to guide mineral exploration. Modern surveys are typically designed to permit quantification of data uncertainty through data quality metrics by using quality assurance and quality control (QA/QC) methods. However, these metrics, such as data accuracy and precision, are obtained through the data generation phase. Consequently, it is unclear how residual uncertainty in geochemical data can be minimized (denoised). This is a limitation to propagating uncertainty through downstream activities, particularly through complex models, which can result from the usage of artificial intelligence-based methods. This study aims to develop a deep learning-based method to examine and quantify uncertainty contained in geochemical survey data. Specifically, we demonstrate that: (1) autoencoders can reduce or modulate geochemical data uncertainty; (2) a reduction in uncertainty is observable in the spatial domain as a decrease of the nugget; and (3) a clear data reconstruction regime of the autoencoder can be identified that is strongly associated with data denoising, as opposed to the removal of useful events in data, such as meaningful geochemical anomalies. Our method to post-hoc denoising of geochemical data using deep learning is simple, clear and consistent, with the amount of denoising guided by highly interpretable metrics and existing frameworks of scientific data quality. Consequently, variably denoised data, as well as the original data, could be fed into a single downstream workflow (e.g., mapping, general data analysis or mineral prospectivity mapping), and the differences in the outcome can be subsequently quantified to propagate data uncertainty.

A Novel X-ray Diffraction Procedure for Determining Residual Stresses Around Cold Expanded Holes

The continued interest in developing more accurate finite element models of the cold expansion process has driven the need for better measurement methodologies. This work details an x-ray diffraction (XRD) procedure explicitly designed to determine the residual stresses around a cold expanded hole. Aluminum coupons from two different alloys (2024-T351 and 7075-T651), with nominally 12-mm-diameter holes, were cold expanded to two different levels of applied expansion and measured using XRD. The results of a limited interlaboratory study highlight the reproducibility that one may expect to achieve by its application. For the majority of data points, the residual stresses characterized at each of the two laboratories were in agreement within the bounds of the residual stress determination experimental uncertainties, which were generally ± 15 MPa or less. After examining the differences between results obtained at each laboratory as a function of radial distance from the hole, it was found that no significant systematic error exists between data sets. Thus, the proposed novel procedure appears to provide a more precise and accurate determination of the residual stresses around cold expanded holes with a significant reduction in measurement uncertainty compared to conventional XRD measurement procedures. The results obtained using this novel procedure will be useful in calibrating new computational material models of the expansion process.

Port and inland waterway design and operation in the face of climate change uncertainty

Climate change is a key business risk for ports and inland waterways and, therefore, a key consideration for the design and operation of this infrastructure. While risk and vulnerability are typically assessed using historical information, possible future climates now also need to be considered, using quantitative climate datasets whenever possible. The current challenge is the range of availability – and levels of uncertainty – for specific parameters that are important to ports and inland waterways. When quantitative information is available, a scenario-based approach can be used to identify an appropriate range of projections for assessing risks and designing structures. Notwithstanding the availability of quantitative data, however, adaptive solutions should be favoured. This is especially important where residual uncertainty is high and/or where significant or complex investments are involved. The development of adaptation pathways enables the implementation of phased responses as climate-related hazards, such as pluvial flooding or sea-level rise, evolve. The adaptation pathway approach also reduces the risk of maladaptation associated with the assumption of a deterministic future condition for the design life of the asset or operation.

Adaptive Multiple-Surface Sliding Mode Control of Nonholonomic Systems With Matched and Unmatched Uncertainties

The problem of stabilizing a class of nonholonomic systems in chained form affected by both matched and unmatched uncertainties is addressed in this paper. The proposed design methodology is based on a discontinuous transformation of the perturbed nonholonomic system to which an adaptive multiple-surface sliding mode technique is applied. The generation of a sliding mode allows to eliminate the effect of matched uncertainties, while a suitable function approximation technique enables to deal with the residual uncertainties, which are unmatched. The control problem is solved by choosing a particular sliding manifold upon which a second order sliding mode is enforced via a continuous control with discontinuous derivative. A positive feature of the present proposal, apart from the fact of being capable of dealing with the presence of both matched and unmatched uncertainties, is that no knowledge of the bounds of the unmatched uncertainty terms is required. Moreover, the fact of producing a continuous control makes the proposed approach particularly appropriate in nonholonomic applications, such as those of mechanical nature.

Hierarchical Distribution-Based Tightly-Coupled LiDAR Inertial Odometry

LiDAR inertial odometry (LIO) has attracted much attention due to the complementarity of LiDAR and IMU measurements. In the distribution-based LIO, the components related to distribution covariance in the residual and residual uncertainty from the LiDAR measurement noise is neutralized. And the resultant point cloud constraint degeneration problem severely affects the accuracy of pose estimation. In this paper, a hierarchical tightly-coupled LIO based on distribution is proposed. By excluding the eigenvalue elements in the distribution covariance component with the designed loss function, the uncertainty of corresponding residual is rectified. As a result, the degeneration problem is solved. With anti-degeneration point-to-distribution constraints, a LiDAR inertial odometry based on iterated extended Kalman filter and a factor graph optimization are designed and organized in a hierarchical way to achieve coarse-to-fine pose estimation, where LiDAR and IMU measurements are tightly coupled in both layers. In this way, the respective advantages of high efficiency and high accuracy from filtering and optimization are combined, which offers high-fidelity estimation results in real time. The effectiveness of the proposed method is verified through experiments on the public NC and ENC datasets.

Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation

Accurate and reliable capacity estimation is crucial for lithium-ion batteries to operate safely and stably. However, the extraction steps of health indicators (HIs) limit the feasibility and applicability of data-driven methods. This study proposes a novel estimation framework using deep residual shrinkage network (DRSN) and uncertainty evaluation to estimate the lithium-ion battery capacity directly; model inputs are only random fragment charging data. Results on three datasets confirm that accurate capacity estimation is achieved by DRSN through integrated attention mechanisms and soft thresholding (the mean absolute percentage error is below 2 %). Meanwhile, the inherent noise sensitivity in data-driven methods is alleviated. Additionally, uncertainty evaluation implemented by Bayesian neural networks provides valuable metrics for the reliability of estimated results from different voltage ranges. The significant correlation between uncertainty and error proves the potential of uncertainty to assist battery management systems for control and decision-making. The experimental study demonstrates the high accuracy, adaptability, and robustness of the proposed framework, especially in coping with the frequent occurrence of random incomplete charging scenarios.

The Ethical Obligation for Research During Public Health Emergencies: Insights From the COVID-19 Pandemic

In times of crises, public health leaders may claim that trials of public health interventions are unethical. One reason for this claim can be that equipoise—i.e. a situation of uncertainty and/or disagreement among experts about the evidence regarding an intervention—has been disturbed by a change of collective expert views. Some might claim that equipoise is disturbed if the majority of experts believe that emergency public health interventions are likely to be more beneficial than harmful. However, such beliefs are not always justified: where high quality research has not been conducted, there is often considerable residual uncertainty about whether interventions offer net benefits. In this essay we argue that high-quality research, namely by means of well-designed randomized trials, is ethically obligatory before, during, and after implementing policies in public health emergencies (PHEs). We contend that this standard applies to both pharmaceutical and non-pharmaceutical interventions, and we elaborate an account of equipoise that captures key features of debates in the recent pandemic. We build our case by analyzing research strategies employed during the COVID-19 pandemic regarding drugs, vaccines, and non-pharmaceutical interventions; and by providing responses to possible objections. Finally, we propose a public health policy reform: whenever a policy implemented during a PHE is not grounded in high-quality evidence that expected benefits outweigh harms, there should be a planned approach to generate high-quality evidence, with review of emerging data at preset time points. These preset timepoints guarantee that policymakers pause to review emerging evidence and consider ceasing ineffective or even harmful policies, thereby improving transparency and accountability, as well as permitting the redirection of resources to more effective or beneficial interventions.

Reproducibility in cytometry: Signals analysis and its connection to uncertainty quantification.

Signals analysis for cytometry remains a challenging task that has a significant impact on uncertainty. Conventional cytometers assume that individual measurements are well characterized by simple properties such as the signal area, width, and height. However, these approaches have difficulty distinguishing inherent biological variability from instrument artifacts and operating conditions. As a result, it is challenging to quantify uncertainty in the properties of individual cells and perform tasks such as doublet deconvolution. We address these problems via signals analysis techniques that use scale transformations to: (I) separate variation in biomarker expression from effects due to flow conditions and particle size; (II) quantify reproducibility associated with a given laser interrogation region; (III) estimate uncertainty in measurement values on a per-event basis; and (IV) extract the singlets that make up a multiplet. The key idea behind this approach is to model how variable operating conditions deform the signal shape and then use constrained optimization to "undo" these deformations for measured signals; residuals to this process characterize reproducibility. Using a recently developed microfluidic cytometer, we demonstrate that these techniques can account for instrument and measurand induced variability with a residual uncertainty of less than 2.5% in the signal shape and less than 1% in integrated area.