Risk-based imprecise post-remediation soil quality objectives

While risk-based contaminated land management is an essential component of sustainable remediation, uncertainty is an unavoidable aspect of risk assessment, since most of the parameters that influence risk are typically affected by uncertainty. Uncertainty may be of different origins; i.e., stochastic or epistemic. Stochastic (or aleatoric) uncertainty arises from random variability related to natural processes, while epistemic uncertainty arises from the incomplete/imprecise nature of available information. But the latter is rarely considered in risk assessments, with the result that risk-based soil quality objectives are almost invariably presented as precise (unique) threshold values. In this paper it is shown: (i) how the joint treatment of stochastic and epistemic uncertainty in risk assessment can lead to soil quality objectives presented as intervals rather than precise values and (ii) how this provides an upper risk-based safeguard for post-remediation monitoring values. The proposed method is illustrated by a real case of soils contaminated by arsenic located in the North-East of France. At this site steel manufacturers have gradually filled up a small valley with slag and dust, over more than a century. These materials are enriched in various metal(loid)s, including arsenic and lead. As the environmental authority has asked for a conversion of the site to other uses that may involve access by the general public, an investigation of human health risk was performed based on a sampling campaign and chemical characterizations including various types of extractions and an analysis of bioaccessibility. While further investigations are required to improve the bioaccessibility model, the human health risk presented herein shows how partial or imprecise information can be incorporated in the analysis while taking into account underlying uncertainties.

Assessment of long-term dietary cadmium exposure in children in Germany: Does consideration of data from total diet studies reduce uncertainties from food monitoring programmes?

Total diet studies (TDS) and food monitoring programmes are different approaches for collecting occurrence data on substances in food. This case study evaluated the practical applicability of TDS data (BfR MEAL Study) and monitoring data for the assessment of long-term cadmium exposure in children in Germany. Cadmium data from both programmes were combined with food consumption data from the KiESEL study. Uncertainties associated with both assessments were systematically described. Using monitoring data resulted in cadmium intakes approximately 3 times higher than the use of BfR MEAL Study data. Incomplete data and neglect of market shares and consumption weights were considered by conservative data adjustments to the monitoring data and mainly explain the higher estimates. Fewer data adjustments were necessary for BfR MEAL Study data, which covered almost the entire diet and considered consumer behaviour during sample collection and sample preparation. In sum, the use of the BfR MEAL Study data resulted in less uncertainty and more reliable exposure estimates for chronic assessments over the entire diet. However, description of variability and upper tails of substance distributions in food remain essential features of monitoring data. The integration of both programmes into a complementary system further improves food safety.

Site-scale groundwater pollution risk assessment using surrogate models and statistical analysis

Petroleum pollution in soil and groundwater has emerged as a significant environmental concern worldwide. As a sustainable and cost-effective in-situ remediation technique, Monitored Natural Attenuation (MNA) exhibits significant promise in addressing sites contaminated by petrochemicals. This study specifically targets a typical petrochemical-contaminated site in northern China and employs GMS software to establish a comprehensive physical model. The model relies on time-series monitoring data of phenol concentrations spanning from 2018 to 2020, effectively simulating both the leakage and natural attenuation of phenol. Within this study, the adsorption coefficient and maximum adsorption capacity emerge as the foremost influential factors shaping the outcomes of the model. Given the inherent heterogeneity of the site and the variability of hydrochemical conditions, parameters such as dispersion, porosity, and adsorption coefficient exhibit significant uncertainties. Consequently, relying on traditional deterministic models to predict the feasibility of MNA technology is not reliable. Therefore, this study employs machine learning (ML) methods to construct stochastic parameter models based on physical processes. The Random Forest Regression (RFR) algorithm, after trained, demonstrates strong alignment with numerical model output, exhibiting an average Nash-Sutcliffe Efficiency (NSE) >0.96. Using a stochastic approach, RFR iteratively computes phenol concentration across 6000 sets of parameters. Applying probability statistics, the model shows a notable reduction in the likelihood of phenol concentrations exceeding a threshold, dropping from 64.0% to 15.7% before and after natural attenuation. In parameter uncertainty, the stochastic model emphasizes natural attenuation's efficacy in mitigating phenol pollution risk (porosity being the most influential factor). This case study proposed a novel method to quickly assess the pollution risks at petrochemical sites under the influence of the uncertainty of pollutant transport and reaction parameters. The results can provide a reference for the pollution risk assessment at petrochemical sites, especially in sites with high stratigraphic heterogeneity or insufficient transport parameter data.

Regional bioethanol supply chain optimization with the integration of GIS-MCDM method and quantile-based scenario analysis

This study presents a novel decision-support framework for the bioethanol supply chain network planning and management under uncertainties. Under the holistic framework, the most suitable sites for biorefineries are first screened out by adopting a GIS-based multi-criteria decision-making approach. Then, a mixed-integer linear programming model combined with quantile-based scenario analysis is developed to determine the strategic planning (i.e. locations and size of biorefineries) and tactical management (i.e. biomass purchasing, feedstock transportation, bioethanol production, and product delivery) under uncertainties. The model can effectively search for reliable solutions under uncertainties and achieve tradeoff solutions with the consideration of decision makers' risk tolerance. The proposed framework is demonstrated through a case study in China. It is suggested to build seven biorefineries with a capacity of 100 million liters in Zhumadian city. Utilizing 41% of local agricultural residues could satisfy the bioethanol requirement in the transportation sector under the E20 policy. However, the estimated production cost of bioethanol in Zhumadian is very high, about 1.11 $/L, which makes it lose cost advantage in the fuel market. Thus, currently, effective subsidies, mandatory energy substitution policies, along other environmental regulatory measures are desired to promote the bioethanol industry development.

Adaptive robust constraint-following control for underactuated unmanned bicycle robot with uncertainties

Due to the underactuated and unstable nature, the control of unmanned bicycle robots is a challenge. The presence of unknown and time-varying uncertainties in the system adds to the difficulty of control for the unmanned bicycle robot. From the perspective of constraint-following, this paper proposes a new control approach to address uncertainties in the unmanned bicycle robot system. The uncertainties of the unmanned bicycle robot system are constrained within certain limits, but the bounds are unknown, which can pose a challenge for designing a control approach. Through the implementation of a leakage-type adaptive law that modifies the control system in accordance with tracking errors, the potential exists to confine the boundaries of uncertainties. The performance of uniform boundedness and uniform ultimate boundedness is shown by the Lyapunov stability theory. Numerical simulation of a representative case is performed to illustrate the effectiveness of the proposed control.

Further experimental results on modelling and algebraic control of a delayed looped heating-cooling process under uncertainties

The aim of this research is to revise and substantially extend experimental modelling and control of a looped heating-cooling laboratory process with long input-output and internal delays under uncertainties. This research follows and extends the authors' recent results. As several significant improvements regarding robust modelling and control have been reached, the obtained results are provided with a link and comparison to the previous findings. First, an infinite-dimensional model based on mathematical-physical heat and mass transfer principles is developed. All important heat-fluid transport and control-signal delays are considered when assembling the model structure and relations of quantities. Model parameter values optimization based on the measurement data follows. When determining static model parameter values, all variations in steady-state measured data are taken into account simultaneously, which enhances previously obtained models. Values of dynamic model parameters and delays are further obtained by least mean square optimization. This innovative model is compared to two recently developed process models and to the best-fit model that ignores the measured variations. Controller structures are designed using algebraic tools for all four models. The designed controllers are robust in the sense of robust stability and performance. Both concepts are rigorously assessed, and the obtained conditions serve for controller parameter tuning. Two different control systems are assumed: the standard closed-loop feedback loop and the two-feedback-controllers control system. Numerous experimental measurements for nominal conditions and selected perturbations are performed. Obtained results are further analyzed via several criteria on manipulated input and controlled temperature. The designed controllers are compared to the Smith predictor structure that is well-established for time-delay systems control. An essential drawback of the predictor regarding disturbance rejection is highlighted.

Calculation of decision thresholds according to the standard ISO 11929-3 in case of presence of the peaked background

The method of calculation of the decision threshold with the Least Squares Method, described in the standard ISO 11929, is presented for the case when the sources of peaked background contribute to the peak holding the indication. The decision threshold is calculated from spectral data corresponding to the indication zero; therefore, the observed indication must be removed from the spectrum. When the peaked background is present, the indication completely overlaps with the peaked background, so it can't be unfolded directly. Therefore, two steps are needed in the calculation: the unfolding of the peak, housing the indication, from the continuous background and the possible overlapping peaks, and separating of the indication from the peaked background using the background data obtained from separate calculations and measurements. In this article it is shown that the method of least squares is flexible enough to accommodate all sources of uncertainty into the uncertainty matrix of input quantities. Its derivation is presented in detail and the calculation of the indication corresponding to the decision threshold is described. As a proof of the concept an example of calculating the number of counts corresponding to the decision threshold as a function of the indication is presented. The method of calculation and the results of the calculation are briefly discussed.

Pilot and feasibility studies: extending the conceptual framework

In 2016, we published a conceptual framework outlining the conclusions of our work in defining pilot and feasibility studies. Since then, the CONSORT extension to randomised pilot and feasibility trials has been published and there have been further developments in the pilot study landscape. In this paper, we revisit and extend our framework to incorporate the various feasibility pathways open to researchers, which include internal pilot studies. We consider, with examples, when different approaches to feasibility and pilot studies are more effective and efficient, taking into account the pragmatic decisions that may need to be made. The ethical issues involved in pilot studies are discussed. We end with a consideration of the funders' perspective in making difficult resource decisions to include feasibility work and the policy implications of these; throughout, we provide examples of the uncertainties and compromises that researchers have to navigate to make progress in the most efficient way.

Safety evaluation of horizontal curves on two lane rural highways using machine learning algorithms: A priority-based study for sight distance improvements

OBJECTIVE

Current Road geometric design processes disregard stochastic aspects, resulting in inadequate traffic safety considerations. In addition, the primary sources of crash data are obtained from police department, insurance agency and hospitals, where detailed investigation is not carried out from a transportation perspective. So, the data obtained from these sources may or may not be reliable. The main objective of this study is to account uncertainties using reliability as a tool that considers slow down the vehicle while they maneuver the curve and to develop thresholds of reliability index associated with sight distance based on operating speed design consistent measure as a surrogate safety measure rather than using crash data.

METHODOLOGY

This study proposes a thresholds of reliability index associated with sight distance for various operating speed ranges based on design consistent measure. In addition, established the relationship between the consistency levels, geometric characteristics and vehicle characteristics. In this study, classical topography survey was carried out on the field using total station. The data collected comprises speed data and geometric data of 18 horizontal curves (Lane based analysis was carried out). A total of 3042 free flowing vehicle speeds have been extracted from the video graphic survey and used in the analysis.

RESULTS

The threshold values of reliability indices associated with sight distance is higher as the operating speed increases for the section to be a consistent design section. The results from the Binary Logit Model show that the consistency level is significantly affected by deflection angle and operating speed. Deflection angle was negatively correlated with the in-consistency level, and the operating speed was positively correlated with in-consistency level.

CONCLUSIONS

From Binary Logit Model (BLM) results, we can conclude that an increase in the value of deflection angle will significantly decrease the probability of In-Consistent level, which indicates uncertainties that cause drivers to change the path of vehicle or rate of deceleration of the vehicle while maneuvering the curve will be decreased. Whereas, increase in the operating speed will significantly increase the probability of In-Consistency level.

A multi-perspective study of atmospheric urban heat island effect in China based on national meteorological observations: Facts and uncertainties

The atmospheric urban heat island (AUHI) effect, traditionally measured by in-situ sensors mounted on fixed meteorological stations, has been extensively studied by different and imperfect methods. However, facts and uncertainties of the AUHI estimates revealed by the different methods are not well understood at a large scale. Here we examined the spatial-temporal variations of the AUHI effects from multiple perspectives in China's 86 large cities as revealed by national-level meteorological observations at 2-m height from 1981 to 2017. We find relatively consistent patterns of larger urban heating effects in daily minimum temperature, winter, and Northeast China than their counterparts in terms of multiyear mean intensity (AUHII), long-term trend (△AUHII), and contribution to local warming (according to the CTRUMR "urban minus rural" and CTROMR "observation minus reanalysis" methods). Concurrently, a cooling impact or a reduction in the heating effect has been observed in some cities randomly, especially in daily maximum temperature. On average across cities, the AUHII, △AUHI, CTRUMR, and CTROMR for the daily mean temperature amount to 0.33 °C, 0.013 °C 10a-1, 53 %, and 23 % at an annual mean time scale, respectively. Nevertheless, the poor representativeness of weather stations, discrepancies among the quantification methods, nonlinearity of the long-term tendencies, and coupling effects with rural crop land use activities lead to large uncertainties of the AUHI estimates. Our results emphasize the limitations of national-level meteorological stations in characterizing AUHI in China and suggest that the urban heat island remains a "well described but rather poorly understood" phenomenon warranting further investigation by a combined uses of multiple techniques like high-density sensor networks, remote sensing techniques, and high-resolution numerical models.

Do commodity assets hedge uncertainties? What we learn from the recent turbulence period?

This study analyses the impact of different uncertainties on commodity markets to assess commodity markets' hedging or safe-haven properties. Using time-varying dynamic conditional correlation and wavelet-based Quantile-on-Quantile regression models, our findings show that, both before and during the COVID-19 crisis, soybeans and clean energy stocks offer strong safe-haven opportunities against cryptocurrency price uncertainty and geopolitical risks (GPR). Soybean markets weakly hedge cryptocurrency policy uncertainty, US economic policy uncertainty, and crude oil volatility. In addition, GSCI commodity and crude oil also offer a weak safe-haven property against cryptocurrency uncertainties and GPR. Consistent with earlier studies, our findings indicate that safe-haven traits can alter across frequencies and quantiles. Our findings have significant implications for investors and regulators in hedging and making proper decisions, respectively, under diverse uncertain circumstances.

Geometric-based prescribed performance control for unmanned aerial manipulator system under model uncertainties and external disturbances

In this paper, a robust control strategy with a cascade structure is designed for the underactuated unmanned aerial manipulator (UAM) to perform favorable trajectory tracking, in the presence of model uncertainties and time-varying external disturbances (e.g. wind drag). In the position loop, a geometric distance is adopted as the criterion for the thrust design. Besides, both the model uncertainties and external disturbances are compensated by employing a robust term. In the attitude loop, the prescribed performance guarantees are enforced in the controller design to standardize transient performance on attitude errors. In particular, just relying on the necessary feedback states, an auxiliary system, the adaptive control technique, and the disturbance observer are respectively designed to estimate the model uncertainties and external disturbances. Through the Lyapunov stability theory analysis, the proposed approach based on a geometric distance can effectively track the desired trajectory. The results of the comparative simulations study demonstrate the effectiveness and advantages of the proposed approach.

Data-driven adaptive integral terminal sliding mode control for uncertain SMA actuators with input saturation and prescribed performance

This paper focuses on the data-driven adaptive control problem of the shape memory alloy (SMA) actuators subject to uncertainties, input saturation and prescribed performance. Firstly, the uncertainties estimation method, anti-windup technology, and prescribed performance function are introduced to deal with uncertain nonlinearity, input constraint, and prespecified performance, respectively. Meanwhile, a general approach about designing asymmetrical convergence bound is presented to increase the flexibility of creating convergence area. Secondly, taking uncertainties, input saturation, and asymmetrical convergence bound into consideration, we design an integral terminal sliding mode controller to guarantee the prescribed tracking accuracy without using the knowledge of the SMA actuators model. Further, the stability of the controller and the boundedness of the convergence error are proved by rigorous theoretical analysis. Finally, the success and superiority of our controller are verified by the SMA actuator experiments.

Investigating the impacts of COVID-19 safety measures and related uncertainties among socially vulnerable groups in Lagos megacity

The emergence of the unique coronavirus disease (COVID-19), associated safety measures and impacts have been experienced differently across various sociodemographic and livelihood groups. As a result of the impacts of the COVID-19 restrictions, this study examined experiences and livelihood uncertainties from socially vulnerable groups. One hundred and fifty responses (150) were recorded from residents in Iwaya, and Makoko areas within Lagos Mainland Local Government Area of Lagos state. Complete lockdown or stay-at-home orders, compulsory face masks in public spaces, curfews, physical and social distancing and restriction of inter-state movements are some of the precautionary/safety measures introduced by the Government and enforced by security agents. The findings show that curfews and restriction of inter-state movements were two of the safety measures that had no or reduced impact (p-values > 0.01) on the respondents’ means of livelihood. Our results reveal that because a larger percentage of male participants are self-employed and owned personal businesses they were more affected by COVID-19 restrictions than females. 42.7% (64) of females and 57.3% (86) of males reported COVID-19-related anxieties and stress from fear of starvation, and contracting the virus, to impacts on money/finances, slow sales and businesses, food supply, job loss, erratic power supply affecting work from home options. 54.7% of respondents had more than 5 people living together, while 84.7% of housing types (128) are bungalows with several rooms inhabited by an average of three to four people per household. Increased stress, fear of hunger, loss of jobs and source of income were some of the negative impacts resulting from the introduction of the COVID-19 safety measures which adversely affected occupations like traders, people engaged in fishing activities, painters, carpenters, hairdressers and barbers, printers and bricklayers. Our work provides insights into the effects of the COVID-19-safety measures and subjective impact across vulnerable groups and occupations.

Robust min-max regret covering problems

This article deals with two min-max regret covering problems: the min-max regret Weighted Set Covering Problem (min-max regret WSCP) and the min-max regret Maximum Benefit Set Covering Problem (min-max regret MSCP). These problems are the robust optimization counterparts, respectively, of the Weighted Set Covering Problem and of the Maximum Benefit Set Covering Problem. In both problems, uncertainty in data is modeled by using an interval of continuous values, representing all the infinite values every uncertain parameter can assume. This study has the following major contributions: (i) a proof that MSCP is Σ p 2 -Hard, (ii) a mathematical formulation for the min-max regret MSCP, (iii) exact and (iv) heuristic algorithms for the min-max regret WSCP and the min-max regret MSCP. We reproduce the main exact algorithms for the min-max regret WSCP found in the literature: a Logic-based Benders decomposition, an extended Benders decomposition and a branch-and-cut. In addition, such algorithms have been adapted for the min-max regret MSCP. Moreover, five heuristics are applied for both problems: two scenario-based heuristics, a path relinking, a pilot method and a linear programming-based heuristic. The goal is to analyze the impact of such methods on handling robust covering problems in terms of solution quality and performance.

Efficiency and robustness of type-2 fractional fuzzy PID design using salps swarm algorithm for a wind turbine control under uncertainty

An accurate and efficient control of the Maximum Power Point Tracking (MPPT) and pitch angle for the Wind Turbine (WT) system can provide more energy while also protecting the material. Thus, considering these economic aspects, many controllers are proposed to guarantees their performances. The most studied strategy is the classical PID controller. However, its performance is limited due to the nonlinear model and the difficult wind speed form, which inflict many uncertainties. To solve these drawbacks, an advanced controller named Type-2 Fractional Order Fuzzy PID (T2-FOPID) controller is suggested in this paper to improve the exigence controls. Meanwhile, an efficient optimization technique named the Salps Swarm Algorithm (SSA) has been introduced in order obtaining the best controller parameters. The efficiency of the proposed controller is tested to the 10 kW-WT under various wind speed scenarios. The derived results explicitly indicate that the proposed strategy outperforms the PID controller and other controllers, in terms. the minimum of error, the overshoot and the settling time.

Hyperbolic uncertainty estimator based fractional order sliding mode control framework for uncertain fractional order chaos stabilization and synchronization

This paper formulates a new fractional order (FO) integral terminal sliding mode control algorithms for the stabilization and synchronization of N-dimensional FO chaotic/hyper-chaotic systems, which are perturbed with unknown uncertainties. In order to render closed loop robustness, a novel efficient double hyperbolic functions based uncertainty estimator is developed for the estimation and mitigation of unknown uncertainties. Moreover, a double hyperbolic reaching law comprising of tangent hyperbolic and inverse sine hyperbolic functions is incorporated in the presented control techniques for the practical convergence of various chaotic system states and tracking errors to infinitesimally close to equilibrium. Examples such as FO Lu, FO Chen and FO Lorenz systems are taken to investigate robustness, finite time convergence, tracking accuracy and closed loop stability properties of the devised methodologies. Last but not least, comparative analysis is also carried out between the proposed and prior control techniques through various time domain performances such as settling time, error indices and measure of control energy.

The anthropogenic consequences of energy consumption in the presence of uncertainties and complexities: evidence from World Bank income clusters

In environmental management, many studies have examined the energy consumption-emission nexus in detail. However, for the first time in the literature, this study considers how the Economic Complexity Index (ECI) and economic policy uncertainty (EPU) moderate the contribution of energy consumption to emissions for the four World Bank Income clusters. The system generalised methods of moments are applied to data for 109 countries from 1996 to 2016. Based on the main model (grouped clusters) estimations, the result revealed the existence of the environmental Kuznets curve (EKC) hypothesis. Also, an increase in air transport and consumption of energy releases more carbon emissions to the climate. Interestingly, ECI decreases carbon emission significantly while EPU does not have a significant impact. Moreover, the study revealed that ECI moderated the impact of other variables on emission, but EPU is not a significant moderator. Furthermore, a comparative analysis among the four incomes suggests that the EKC hypothesis holds only in the high-income clusters; ECI is a significant predictor of carbon emission in the four clusters, but it only decreases the emission in high-income clusters. This corroborates the debate on climate change and the productive capacity of high-income countries. Given the foregoing, several policy measures were recommended.

The Sydney declaration - Revisiting the essence of forensic science through its fundamental principles

Unlike other more established disciplines, a shared understanding and broad acceptance of the essence of forensic science, its purpose, and fundamental principles are still missing or mis-represented. This foundation has been overlooked, although recognised by many forensic science forefathers and seen as critical to this discipline's advancement. The Sydney Declaration attempts to revisit the essence of forensic science through its foundational basis, beyond organisations, technicalities or protocols. It comprises a definition of forensic science and seven fundamental principles that emphasise the pivotal role of the trace as a vestige, or remnant, of an investigated activity. The Sydney Declaration also discusses critical features framing the forensic scientist's work, such as context, time asymmetry, the continuum of uncertainties, broad scientific knowledge, ethics, critical thinking, and logical reasoning. It is argued that the proposed principles should underpin the practice of forensic science and guide education and research directions. Ultimately, they will benefit forensic science as a whole to be more relevant, effective and reliable.

1D_RnDPM: A freely available 222Rn production, diffusion, and partition model to evaluate confounding factors in the radon-deficit technique

The radon-deficit technique is a powerful tool to detect and delineate sub-surface accumulations of organic contaminants. Field measurements of ²²²Rn in soil air, however, are affected by several confounding factors that can lead to the misinterpretation of results. Among the most influential are: vertical and lateral changes of lithology, fluctuating contaminant saturations with depth, varying water saturation ratios along the soil profile and atmospheric (and, therefore, soil) thermal oscillations. To evaluate and minimize the effect of these confounding factors on the interpretation of the results of the Rn deficit technique, a Matlab® based multi-layer model of ²²²Rn production-partition-diffusion in unsaturated porous media (1D_RnDPM: One-Dimensional ²²²Rn Diffusion and Partition Model) has been developed and is freely available as Supplementary Material in this work. A laboratory protocol has also been proposed to obtain site-specific input parameters for the model, i.e., ²²²Rn equilibrium concentration (as determined by the accumulation chamber method), soil bulk density and soil solid-phase density. The model predictions have been contrasted with field information obtained from successive sampling campaigns in which ²²²Rn in soil air was measured at a site where the vadose zone, consisting of an anthropogenic backfill underlain by a silt layer, is affected by a complex mixture of benzene, phenol, (poly) chlorobenzenes, (poly) chlorophenols and hexachlorocyclohexane isomers, among other compounds. The model has successfully predicted the vertical profile of ²²²Rn concentrations in soil air, including the effect of the oscillations of the water table and of ground-level temperature. The results also underline that ²²²Rn measurements in subsoil air are representative only of local conditions around the sampling point, an expected result given that ²²²Rn maximum effective diffusion length is very limited. As a consequence, the influence of a highly fluctuating water table at the site goes undetected at the sampling depths used in the field campaigns. MAIN FINDINGS: The combination of a numerical model and a laboratory protocol allows to predict the activity of ²²²Rn along the soil profile and to assess the influence of site-specific confounding factors.

New perspectives on the calculation of bioaccumulation metrics for active substances in living organisms

Today, only few ready-to-use and convenient decision-making tools are available in ecotoxicology concerning accumulation and effects of chemical substances on organisms, accounting for exposure situations that are known to be complex (routes of exposure, metabolism, mixtures, etc.). This paper presents new perspectives on the generic calculation of bioaccumulation metrics via the innovative web tool MOSAIC_bioacc (http://mosaic.univ-lyon1.fr/bioacc). MOSAIC_bioacc provides all kinds of bioaccumulation metrics associated with their uncertainty whatever the species-compound combination. MOSAIC_bioacc expects accumulation-depuration data as inputs, even with complex exposure and clearance patterns, to quickly perform their relevant analysis. MOSAIC_bioacc intends to facilitate the daily work of regulators, or any ecotoxicologist, who will freely benefit from a user-friendly online interface that automatically fits toxicokinetic models without need for users to invest in the technical aspects to get bioaccumulation metrics estimates. MOSAIC_bioacc also provides all results in a fully transparent way to ensure reproducibility. Integr Environ Assess Manag 2022;18:10-18. © 2021 SETAC.

Bioassays in workers exposed to long time random intakes

Workers who are occupationally exposed to radioactive aerosols are usually subjected to periodic controls of internal contamination by performing bioassays (whole body or partial body monitoring and measurement of excreta samples). The intakes are also estimated by using Static Air Samples (SAS). These measurements are used to estimate the radioactive intakes of the workers. A typical assumption is the workers are chronically (constant) exposed for long periods of time. However, the intakes are random and there are also periods without any exposure (weekends, holidays, etc.). The method presented here considers both facts. Simulations help to choose the most appropriate method of evaluation to minimize the statistical uncertainties in the intake. It has been applied to evaluate workers exposed to UO2 aerosols for a long time (30 years or more for most of them) in the same working area (sintering). Results of measurements of uranium in urine and daily intakes (from SAS) of these workers have been used. For this evaluation, the new Occupational Intakes of Radionuclides (OIR) biokinetic models of the International Commission on Radiological Protection (ICRP) for uranium have been solved. For some workers the evaluation gives a significative deviation between the intake estimated from urine samples and the intake estimated using the SAS values, supporting the idea that the physiological standard parameters of the reference worker are not always applicable. The computations have been implemented in the BIOKMOD code.

Unknown uncertainties in the COVID-19 pandemic: Multi-dimensional identification and mathematical modelling for the analysis and estimation of the casualties

Insights about the dominant dynamics, coupled structures and the unknown uncertainties of the pandemic diseases play an important role in determining the future characteristics of the pandemic diseases. To enhance the prediction capabilities of the models, properties of the unknown uncertainties in the pandemic disease, which can be utterly random, or function of the system dynamics, or it can be correlated with an unknown function, should be determined. The known structures and amount of the uncertainties can also help the state authorities to improve the policies based on the recognized source of the uncertainties. For instance, the uncertainties correlated with an unknown function imply existence of an undetected factor in the casualties. In this paper, we extend the SpID-N (Suspicious-Infected-Death with non-pharmacological policies) model as in the form of MIMO (Multi-Input-Multi-Output) structure by adding the multi-dimensional unknown uncertainties. The results confirm that the infected and death sub-models mostly have random uncertainties (due undetected casualties) whereas the suspicious sub-model has uncertainties correlated with the internal dynamics (governmental policy of increasing the number of the daily tests) for Turkey. However, since the developed MIMO model parameters are learned from the data (daily reported casualties), it can be easily adapted for other countries. Obtained model with the corresponding uncertainties predicts a distinctive second peak where the number of deaths, infected and suspicious casualties disappear in 240, 290, and more than 300 days, respectively, for Turkey.

Role of uncertainties in protecting ecological resources during remediation and restoration

Cleanup of contaminated waste sites is a National priority to protect human health and the environment, while restoring land to productive uses. While there are uncertainties with understanding risk to individuals from exposure, the aim of this study was to focus on uncertainties and complexities for ecological systems, complicated by hundreds of species occupying any remediation site which participate in multiple-interacting food webs. The ability to better predict the effectiveness of remediation in fostering future ecosystems might facilitate remedy selection and improve strategic environmental management. This investigation examined (1) uncertainties in ecosystem processes, (2) uncertainties in exposure from contamination before remediation, and (3) uncertainties during remediation. Two Department of Energy sites Hanford Site and Savannah River Site were used as case studies to illustrate how the uncertainties affect eco-receptors. Several types of ecological, physical, and human dimension uncertainties are defined. Ecological uncertainties include temporal, spatial, individual, developmental, and exogenous types. Physical uncertainties are weather-related, watershed variations, slope/aspect, soil/sediment structure and form, unforeseen events, and temporal patterns. Human dimension uncertainties include current land use, future land use, extractive and non-extractive recreation. The effects of remedial strategies varied between the two sites because Hanford is a primarily arid shrub-steppe ecotype, while Savannah River is a wet forest ecotype. Defining the associated ecological sensitivities and uncertainties and providing examples might help policy-makers, managers, planners, and contractors to be aware of issues to consider throughout planning, remediation, and restoration. Adding ecological uncertainty analysis to risk evaluations and remediation planning is analogous to using safety factors in human health risk assessment.

An interval chance-constrained programming-based optimization model for carbon capture, utilization, and storage system planning

Carbon capture, utilization, and storage (CCUS) are widely regarded as a crucial technological option for industrial large-scale carbon dioxide (CO₂) emissions reduction. However, high-cost and uncertainties hinder the widespread application of CCUS technology. In this study, an interval-chance-constrained programming-based optimization model was proposed to address random probability distributions, interval values, complex interactions, and the dynamics of capacity expansion issues. The model was applied to a CCUS project in China. A set of violation probability levels (0.01, 0.05, 0.1, and 0.2) were designed to reflect system costs and risk levels. And then the solutions for system costs, capacity expansion, and operating schemes under four violation probability levels (p_i') could be generated. The results revealed that the model could ensure the highest reliability and largest CO₂ storage under p_i' = 0.01. At this probability level, the amount of CO₂ storage would range from 4972.05-5429.75 kilotons per annum (ktpa), the CCUS system cost would be highest at $166.57 million, and the net system benefits would be slightly less at $105.91 million. If policymakers strive to achieve the net system benefits of the project, the highest net system benefits would be achieved under p_i' = 0.05. At this probability level, the net system benefits would increase to $135.45 million, the system cost would reduce to $138.62 million, but the total amount of CO₂ storage would decrease to between 4090.01 and 4653.24 ktpa, which would entail a high risk of system violation. These findings enable policymakers to determine the trade-offs among system reliability, CO₂ reduction, and the benefits of the project. The modeling approach can also address interactions among CCUS activities and the dynamics of facility expansion issues as well as help policymakers develop adaptive operational strategies. This study enriches CCUS research through an interval chance-constrained optimization modeling approach for CCUS system management under multiple uncertainties.

Enhancing the recruitment of postgraduate researchers from diverse countries: managing the application process

International students form an important element of most universities' internationalisation strategies, especially for research and the recruitment of high calibre PhD students (PGRs). Despite the numerous studies of PGRs' post-arrival experiences, there is a major dearth of research into their pre-arrival, application experiences. Given the worldwide competition for high calibre PGRs, along with impact posed by the Covid-19 pandemic and by Brexit for the UK, it is vital for universities to ensure that factors clearly under their control, such as the information on their websites and the way they communicate, are as informative and helpful as possible. In this article, we draw on social media data to examine the challenges and uncertainties that Korean PGR applicants experienced in navigating the process of applying to UK universities. The paper compares their confusions with information available on university websites and recommends a series of points that higher education institutions should check for. It also reveals and discusses issues associated with communication. While the data has been collected from Korean social media websites, we argue that our paper has broader relevance for the following reasons. First, the same fundamental intercultural issues-different educational systems and different background knowledge-apply to PGR applicants from other countries and so their queries are likely to be similar or comparable. Second, the insights gained from social media websites to facilitate the application process and thereby enhance recruitment can usefully be applied to other countries and levels of study, in a way that has rarely been done to date.

A gridded emission inventory of semi-volatile and intermediate volatility organic compounds in China

An emission inventory of precursors is a prerequisite for the simulation of secondary organic aerosol (SOA), which could provide valuable information on the evolution of precursors, formation of SOA, and its influence on fine particle (PM_2.5) abundance, oxidative capacity, and climate change. However, an emission inventory of semi-volatile and intermediate volatility organic compounds (S/IVOCs), the key precursor of SOA, particularly the gridded inventory that is appropriate for input into regional air quality models, remains limited in China, leading to an incomplete understanding of S/IVOCs sources and roles in SOA formation and the atmospheric environment. Therefore, a gridded emission inventory of S/IVOCs in China for 2016 was developed based on ample source-specific measured data on emission ratios of S/IVOCs to primary organic aerosols (POA) from literatures. The total emission of S/IVOCs was estimated to be 9.6 Tg, and industry and residential sectors were major sources of S/IVOCs, with contributions of 48.0% and 30.2%, respectively. The spatial variations suggested that S/IVOC emissions were mainly distributed in the highly industrialized and urbanized regions in China, such as Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Sichuan-Chongqing (SC) regions, though the contributions and temporal patterns varied between different regions. Furthermore, uncertainty of the emission inventory was estimated to be within the range of -66%-153%, which was mainly attributed to emission ratios of IVOCs/POA for industry, transportation, and power plants. The gridded emission inventory developed in this study can be used to estimate the emissions of S/IVOCs in different regions, and can be applied to different models for a better understanding of the environmental effects of S/IVOCs.

Variabilities in µQCT-based FEA of a tumoral bone mice model

A finite element analysis based on Micro-Quantitative Computed Tomography (µQCT) is a method with high potential to improve fracture risk prediction. However, the segmentation process and model generation are generally not automatized in their entirety. Even with a rigorous protocol, the operator might add uncertainties during the creation of the model. The aim of this study was to evaluate a µQCT-based model of mice tumoral and sham tibias in terms of the variabilities induced by the operator and sensitivity to operator-dependent variables (such as model orientation or length). Two different operators generated finite element (FE) models from µCT images of 8 female Balb/c nude mice tibias aged 10 weeks old with bone tumors induced in the right tibia and with sham injection in the left. From these models, predicted failure load was determined for two different boundary conditions: fixed support and spherical joints. The difference between the predicted and experimental failure load of both operators was large (-122% to 93%). The difference in the predicted failure load between operators was less for the spherical joints boundary conditions (9.8%) than for the fixed support (58.3%), p < 0.001, whereas varying the orientation of bone tibia caused more variability for the fixed support boundary condition (44.7%) than for the spherical joints (9.1%), p < 0.002. Varying tibia length had no significant effect, regardless of boundary conditions (<4%). When using the same mesh and same orientation, the difference between operators is non-significant (<6%) for each model. This study showed that the operator influences the failure load assessed by a µQCT-based finite element model of the tumoral and sham mice tibias. The results suggest that automation is needed for better reproducibility.

Quantifying Operator Subjectivity within Flow Cytometry Data Analysis as a Source of Measurement Uncertainty and the Impact of Experience on Results

Flow cytometry is a complex measurement characterization technique, utilized within the manufacture, measurement, and release of cell and gene therapy products for rapid, high-content, and multiplexed discriminatory cell analysis. A number of factors influence the variability in the measurement reported including, but not limited to, biological variation, reagent variation, laser and optical configurations, and data analysis methods. This research focused on understanding the contribution of manual operator variability within the data analysis phase. Thirty-eight participants completed a questionnaire, providing information about experience and motivational factors, before completing a simple gating study. The results were analyzed using gauge repeatability and reproducibility techniques to quantify participant uncertainty. The various stages of the gating sequence were combined through summation in quadrature and expanded to give each participant a representative uncertainty value. Of the participants surveyed, 85% preferred manual gating to automated data analysis, with the primary reasons being legacy ("it's always been done that way") and accuracy, not in the metrological sense but in the clear definition of the correct target population. The median expanded uncertainty was calculated as 3.6% for the population studied, with no significant difference among more or less experienced users. Operator subjectivity can be quantified to include within measurement uncertainty budgets, required for various standards and qualifications. An emphasis on biomanufacturing measurement terminology is needed to help understand future and potential solutions, possibly looking at translational clinical models to engage and enhance better training and protocols within industrial and research settings.

Evaluation of uncertainties in environmental impact assessment of naturally occurring radiation exposure situations on example of undisturbed and legacy NORM sites in the Fen Complex, Norway

The risk from naturally occurring radioactive materials (NORM) has been extensively assessed, and this has led to the integration of specific NORM radiation protection requirements within the latest EU Directive 2013/59. Nevertheless, it has been internationally recognised that remaining NORM knowledge gaps and uncertainties now present similarly significant issues in addressing recent regulatory requirements. The multi-tiered nature of environmental impact assessment (EIA) implies per se possibility for uncertainties, but when EIA at radiation exposure sites includes consideration of sites with multiple radiation and contamination sources, different ecosystem transport pathways, effects and risks by applying different parameters and models, level of overall uncertainty increases. The results of EIA study in the Fen area in Norway, comprised of undisturbed and legacy NORM sites, have been evaluated in this analysis, in order to identify all existing input uncertainties and how they may impact the final conclusions, and thus, influence any subsequent decision-making. The main uncertainties have been identified in the measurement and exposure analysis tier, and were related to the heterogeneous distribution of radionuclides, radionuclide speciation, as well as to generic variability issues in the concepts used for mobility and biota uptake analysis (such as distribution coefficient, transfer factors and concentration ratios) as well as radioecological modelling. The uncertainties in the input values to the calculation of the dose arising from radon exposure in the Fen area led to an overall elevated uncertainty of the magnitude of the radiation exposure dose of humans. It has been concluded that an integrated, ecosystem-based approach with consideration of complexity of prevailing environmental conditions and interconnections must be applied to fully understand possible radiation effects and risks.

Time of PTV is ending, robust optimization comes next

Continuous improvements have been made in the way to prescribe, record and report dose distributions since the therapeutic use of ionizing radiations. The international commission for radiation units and measurement (ICRU) has provided a common language for physicians and physicists to plan and evaluate their treatments. The PTV concept has been used for more than two decades but is becoming obsolete as the CTV-to-PTV margin creates a static dose cloud that does not properly recapitulate all planning vs. delivery uncertainties. The robust optimization concept has recently emerged to overcome the limitations of the PTV concept. This concept is integrated in the inverse planning process and minimizes deviations to planned dose distribution through integration of uncertainties in the planning objectives. It appears critical to account for the uncertainties that are specific to protons and should be accounted for to better exploit the clinical potential of proton therapy. It may also improve treatment quality particularly in hypofractionated photon plans of mobile tumors and more widely to photon radiotherapy. However, in contrast to the PTV concept, a posteriori evaluation of plan quality, called robust evaluation, using error-based scenarios is still warranted. Robust optimization metrics are warranted. These metrics are necessary to compare PTV-based photon and robustly optimized proton plans in general and in model-based NTCP approaches. Assessment of computational demand and approximations of robust optimization algorithms along with metrics to evaluate plan quality are needed but a step further to better prescribe radiotherapy may has been achieved.

[Uncertainties in the current concept of radiotherapy planning target volume]

The planning target volume is an essential notion in radiotherapy, that requires a new conceptualization. Indeed, the variability and diversity of the uncertainties involved or improved with the development of the new modern technologies and devices in radiotherapy suggest that random and systematic errors cannot be currently generalized. This article attempts to discuss these various uncertainties and tries to demonstrate that a redefinition of the concept of planning target volume toward its personalization for each patient and the robustness notion are likely an improvement basis to take into account the radiotherapy uncertainties.

An analysis of the limitations and uncertainties of in vivo developmental neurotoxicity testing and assessment to identify the potential for alternative approaches

Limitations of regulatory in vivo developmental neurotoxicity (DNT) testing and assessment are well known, such as the 3Rs conflict, low throughput, high costs, high specific expertise needed and the lack of deeper mechanistic information. Moreover, the standard in vivo DNT data variability and in the experimental animal to human real life extrapolation is uncertain. Here, knowledge about these limitations and uncertainties is systematically summarized using a tabular OECD format. We also outline a hypothesis how alternative, fit-for-purpose Integrated Approaches to Testing and Assessment (IATAs) for DNT could improve current standard animal testing: Relative gains in 3Rs compliance, reduced costs, higher throughput, improved basic study design, higher standardization of testing and assessment and validation without 3Rs conflict, increasing the availability and reliability of DNT data. This could allow a more reliable comparative toxicity assessment over a larger proportion of chemicals within our global environment. The use of early, mechanistic, sensitive indicators for potential DNT could better support human safety assessment and mixture extrapolation. Using kinetic modelling ideally these could provide - eventually context dependent - at least the same level of human health protection. Such new approaches could also lead to a new mechanistic understanding for chemical safety, permitting determination of a dose that is likely not to trigger defined toxicity traits or pathways, rather than a dose not causing the current apical organism endpoints. The manuscript shall motivate and guide the development of new alternative methods for IATAs with diverse applications and support decision-making for their regulatory acceptance.

A first estimation of uncertainties related to microplastic sampling in rivers

Many studies have been conducted to quantify microplastic contamination, but only a few of them have actually the sampling methodology and associated uncertainties. This study seeks to examine the influence of sampling strategy on the confidence interval of river microplastic estimates. 16 samples are collected in the Gave de Pau River (southwestern France) during a three-hour window with a 330-μm mesh size net. Three different exposure times (3, 5 and 7 min) allow for a respective filtration rate by the net of 35.6 m³ (3 samples), 59.4 m³ (10 samples), and 83.2 m³ (3 samples) of water. Organic matter contained in samples is removed by hydrogen peroxide oxidation. The plastic particles are then counted and classified under a binocular microscope. The microplastic concentrations vary between 2.64 and 4.24 microplastics/m³, with a median value of 3.26 microplastics/m³. Statistical analysis does not show differences in microplastic concentrations for the three exposure times. This result seems to demonstrate that a filtration of approx. 35 m³ of water is sufficient under similar conditions (similar flow condition and degree of microplastic contamination) and can help reduce sampling and sample processing time. Other analyses, based on 10 filtrations of 59.4 m³, show that the higher the number of samples, the lower the confidence interval. For triplicates, the mean confidence interval reaches 15% of the median value. Thus, collecting triplicates would seem to offer a reasonable optimum, in combining an acceptable error percentage and time efficiency. These results might depend on the microplastic load of the river, therefore making it necessary to conduct similar analyses on other rivers. This study reports for the first time uncertainties related to microplastic sampling in rivers. Such findings will serve to set up long term monitoring, highlight spatial differences between sites and improve the accuracy of annual microplastic fluxes in rivers.

Applicability and limitations of the radon-deficit technique for the preliminary assessment of sites contaminated with complex mixtures of organic chemicals: A blind field-test

A blind field test with 136 independent measurements of radon (²²²Rn) in soil air retrieved from a depth of 0.8 m in a decommissioned lindane (γ-hexachlorocyclohexane) production plant was undertaken to evaluate the performance of the ²²²Rn-deficit technique as a screening methodology for the location and delineation of subsurface accumulations of complex mixtures of organic contaminants. Maps of ²²²Rn iso-concentrations were drawn and interpreted before direct analytical information regarding concentrations of hexachlorocyclohexanes, chlorobenzenes and BTEX compounds in soil, groundwater and soil air were disclosed to the authors. The location and extension of pollution hot spots inferred from the ²²²Rn campaigns agrees remarkably well with the analytical data obtained from the intrusive sampling campaigns and with the location of contaminant source zones (chemical reactor and waste-storage area) and geological sinks of those contaminants (paleochannel). Two main limitations to the applicability of the ²²²Rn-deficit technique were identified and assessed: The statistically significant variation of ²²²Rn concentrations with diurnal changes of ground-level air temperature and the maximum depth of investigation in the absence of significant advective and co-advective transport of radon. If the influence of those two factors is accounted for and/or minimized (by averaging replicated measurements during the workday and in different days), the ²²²Rn-deficit technique has the potential to be an efficient technique which delivers information in quasi-real time, with a much higher spatial density than that of intrusive techniques, at a much faster rate and at a significantly lower cost. MAIN FINDINGS: The ²²²Rn-deficit technique is an effective tool for real-time site characterization only limited by diffusion length of radon and diurnal temperature variations.

Robust internal model control of servo motor based on sliding mode control approach

This paper proposes a robust internal model control (IMC) based on sliding mode control (SMC) approach for high-performance motion control of a servo motor subject to uncertainties and/or disturbances. The proposed control strategy considers not only the simplicity and intuition of the IMC-based controller for a prescribed tracking performance but also the effectiveness of the SMC scheme to guarantee the robustness of the servo system. Since the performance of the IMC-based controller can be analyzed via a SMC structure, a robust control law based on the SMC technique is introduced into the IMC scheme to decrease the sensitivity to uncertainties and enhance the resistance to disturbances. Moreover, the 2-degree-of-freedom IMC integrating the robust SMC scheme is developed to further improve the control performance. The stability is analyzed based on Lyapunov theory, and the theoretical results show that a prescribed transient tracking performance and a final tracking accuracy of the servo system can be guaranteed. Comparative simulations and experiments are investigated to verify the high performance nature of the proposed control strategy.

PET and MRI based RT treatment planning: Handling uncertainties

Imaging provides the basis for radiotherapy. Multi-modality images are used for target delineation (primary tumor and nodes, boost volume) and organs at risk, treatment guidance, outcome prediction, and treatment assessment. Next to anatomical information, more and more functional imaging is being used. The current paper provides a brief overview of the different applications of imaging techniques used in the radiotherapy process, focusing on uncertainties and QA. The paper mainly focuses on PET and MRI, but also provides a short discussion on DCE-CT. A close collaboration between radiology, nuclear medicine and radiotherapy departments provides the key to improve the quality of radiotherapy. Jointly developed imaging protocols (RT position setup, immobilization tools, lasers, flat table…), and QA programs are mandatory. For PET, suitable windowing in consultation with a Nuclear Medicine Physician is crucial (differentiation benign/malignant lesions, artifacts…). A basic knowledge of MRI sequences is required, in such a way that geometrical distortions are easily recognized by all members the RT and RT physics team. If this is not the case, then the radiologist should be introduced systematically in the delineation process and multidisciplinary meetings need to be organized regularly. For each image modality and each image registration process, the associated uncertainties need to be determined and integrated in the PTV margin. When using functional information for dose painting, response assessment or outcome prediction, collaboration between the different departments is even more important. Limitations of imaging based biomarkers (specificity, sensitivity) should be known.

Radiobiological dose calculation parameters for cervix cancer brachytherapy: A systematic review

The GEC-ESTRO recommendation in cervical cancer treatment planning, including external beam radiotherapy and brachytherapy boosts, is to use radiobiological dose calculations. Such calculations utilize the linear-quadratic model to estimate the effect of multiple cellular response factors and dose delivery parameters. The radiobiological parameters utilized in these calculations are literature values estimated based on clinical and experimental results. However, the impact of the uncertainties associated with these parameters is often not fully appreciated. This review includes a summary of the radiobiological dose calculation (for both high-dose-rate and pulsed-dose-rate brachytherapy boost treatments) for cervical cancer and a compilation of the reported values of the associated parameters. As discrepancies exist between conventionally recommended and published values, equivalencies between current brachytherapy boosts may be imprecise and could create underappreciated uncertainties in the radiobiological dose calculations. This review highlights these uncertainties by calculating the radiobiological dose delivered by the brachytherapy boost when assuming different radiobiological parameter values (within the range reported by previous research). Furthermore, conventional treatment planning does not consider the effects of proliferation of the tumor over the treatment time, which can significantly decrease its radiobiological dose and can introduce an additional variance of over 7 Gy₁₀. Further investigation of uncertainties in parameter values and modifications of current dose models could improve the accuracy of radiobiological dose calculation.

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given.

Validation of thermophysical data for scientific and engineering applications. J Chem Thermodyn 2019;133:208-222. [PMID: 32165767 PMCID: PMC7067062 DOI: 10.1016/j.jct.2019.01.029]

High quality thermophysical property data are essential to many scientific and engineering applications. These data are produced at a high rate and are affected by a range of experimental and reporting error sources that often exceed stated uncertainties. As a result, critical evaluation is required to establish the limits of reliability in a quantified way. The present work describes reporting recommendations and property data validation methods developed and applied at the Thermodynamics Research Center at NIST through the use of the ThermoData Engine (TDE; SRD 103a/b) software. Examples are provided with an emphasis on various consistency checks, which may include the use of equations of state (EOS).

Radioecology in CONFIDENCE: Dealing with uncertainties relevant for decision making

The CONFIDENCE project is performing research on uncertainties in emergency management and post-accident recovery. It concentrates on the early and transition phases of an emergency, but considers also longer-term decisions made during these phases. To ensure success, the project brings together expertise from four European Radiation Protection Research Platforms (NERIS, MELODI, ALLIANCE and EURADOS) and also from the area of social sciences and humanities. This paper presents an overview of the CONFIDENCE project with a focus on CONFIDENCE's consideration of the radioecology required to support emergency management and post-accident recovery. For instance, operational decisions concerning land and foodchain management rely on radioecological models that are at present mostly based on simple, but highly uncertain, transfer ratios to predict contamination in foodstuffs. CONFIDENCE will investigate if process-based models are better suited to reducing uncertainties associated with empirical ratio based models. Model improvements and uncertainty reduction might be also possible by better evaluating past experience from Chernobyl and Fukushima.

Analyzing the carbon mitigation potential of tradable green certificates based on a TGC-FFSRO model: A case study in the Beijing-Tianjin-Hebei region, China

Contradictions of increasing carbon mitigation pressure and electricity demand have been aggravated significantly. A heavy emphasis is placed on analyzing the carbon mitigation potential of electric energy systems via tradable green certificates (TGC). This study proposes a tradable green certificate (TGC)-fractional fuzzy stochastic robust optimization (FFSRO) model through integrating fuzzy possibilistic, two-stage stochastic and stochastic robust programming techniques into a linear fractional programming framework. The framework can address uncertainties expressed as stochastic and fuzzy sets, and effectively deal with issues of multi-objective tradeoffs between the economy and environment. The proposed model is applied to the major economic center of China, the Beijing-Tianjin-Hebei region. The generated results of proposed model indicate that a TGC mechanism is a cost-effective pathway to cope with carbon reduction and support the sustainable development pathway of electric energy systems. In detail, it can: (i) effectively promote renewable power development and reduce fossil fuel use; (ii) lead to higher CO₂ mitigation potential than non-TGC mechanism; and (iii) greatly alleviate financial pressure on the government to provide renewable energy subsidies. The TGC-FFSRO model can provide a scientific basis for making related management decisions of electric energy systems.

Neural robust stabilization via event-triggering mechanism and adaptive learning technique

The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties.

Uncertainty evaluation of image-based tumour control probability models in radiotherapy of prostate cancer using a visual analytic tool

Functional imaging techniques provide radiobiological information that can be included into tumour control probability (TCP) models to enable individualized outcome predictions in radiotherapy. However, functional imaging and the derived radiobiological information are influenced by uncertainties, translating into variations in individual TCP predictions. In this study we applied a previously developed analytical tool to quantify dose and TCP uncertainty bands when initial cell density is estimated from MRI-based apparent diffusion coefficient maps of eleven patients. TCP uncertainty bands of 16% were observed at patient level, while dose variations bands up to 8 Gy were found at voxel level for an iso-TCP approach.

Stochastic evaluation of annual micropollutant loads and their uncertainties in separate storm sewers

This article describes a stochastic method to calculate the annual pollutant loads and its application over several years at the outlet of three catchments drained by separate storm sewers. A stochastic methodology using Monte Carlo simulations is proposed for assessing annual pollutant load, as well as the associated uncertainties, from a few event sampling campaigns and/or continuous turbidity measurements (representative of the total suspended solids concentration (TSS)). Indeed, in the latter case, the proposed method takes into account the correlation between pollutants and TSS. The developed method was applied to data acquired within the French research project "INOGEV" (innovations for a sustainable management of urban water) at the outlet of three urban catchments drained by separate storm sewers. Ten or so event sampling campaigns for a large range of pollutants (46 pollutants and 2 conventional water quality parameters: TSS and total organic carbon (TOC)) are combined with hundreds of rainfall events for which, at least one among three continuously monitored parameters (rainfall intensity, flow rate, and turbidity) is available. Results obtained for the three catchments show that the annual pollutant loads can be estimated with uncertainties ranging from 10 to 60%, and the added value of turbidity monitoring for lowering the uncertainty is demonstrated. A low inter-annual and inter-site variability of pollutant loads, for many of studied pollutants, is observed with respect to the estimated uncertainties, and can be explained mainly by annual precipitation.

Measurement of soil lead bioavailability and influence of soil types and properties: A review

Lead (Pb) is a widespread heavy metal which is harmful to human health, especially to young children. To provide a human health risk assessment that is more relevant to real conditions, Pb bioavailability in soils is increasingly employed in the assessment procedure. Both in vivo and in vitro measurements for lead bioavailability are available. In vivo models are time- consuming and expensive, while in vitro models are rapid, economic, reproducible, and reliable while involving more uncertainties. Uncertainties in various measurements create difficulties in accurately predicting Pb bioavailability, resulting in the unnecessary remediation of sites. In this critical review, we utilised available data from in vivo and in vitro studies to identify the key parameters influencing the in vitro measurements, and presented uncertainties existing in Pb bioavailability measurements. Soil type, properties and metal content are reported to influence lead bioavailability; however, the differences in methods for assessing bioavailability and the differences in Pb source limit one's ability to conduct statistical analyses on influences of soil factors on Pb bioavailability. The information provided in the review is fundamentally useful for the measurement of bioavailability and risk assessment practices.

Conflicting demands and shifts between policy and intra-scientific orientation during conservation research programmes

Conservation scientists must meet the sometimes conflicting demands of policy and science, but not necessarily at the same time. We analysed the policy and intra-scientific orientations of research projects on effects of stump extraction on biodiversity, and found shifts over time associated with these demands. Our results indicate that uncertainties related to both factual issues and human decisions are often ignored in policy-oriented reports and syntheses, which could give misleading indications of the reliability or feasibility of any conclusions. The policy versus intra-scientific orientation of the scientific papers generated from the surveyed projects varied substantially, although we argue that in applied research, societal relevance is generally more important than intra-scientific relevance. To make conservation science more socially relevant, there is a need for giving societal relevance higher priority, paying attention to uncertainties and increasing the awareness of the value of cross-disciplinary research considering human decisions and values.

Nonlinear integral sliding mode control design of photovoltaic pumping system: Real time implementation

The aim of this paper is to provide high performance control of pumping system. The proposed method is designed by an indirect field oriented control based on Sliding Mode (SM) technique. The first contribution of this work is to design modified switching surfaces which presented by adding an integral action to the considered controlled variables. Then, in order to prevent the chattering phenomenon, modified nonlinear component is developed. The SM concept and a Lyapunov function are combined to compute the Sliding Mode Control (SMC) gains. Besides, the motor performance is validated by numeric simulations and real time implementation using a dSpace system with DS1104 controller board. Also, to show the effectiveness of the proposed approach, the obtained results are compared with other techniques such as conventional PI, Proportional Sliding Mode (PSM) and backstepping controls.

Issues raised by the reference doses for perfluorooctane sulfonate and perfluorooctanoic acid

On 25th May 2016, the U.S. EPA released reference doses (RfDs) for Perfluorooctane Sulfonate (PFOS) and Perfluorooctanoic Acid (PFOA) of 20ng/kg/day, which were much more conservative than previous values. These RfDs rely on the choices of animal point of departure (PoD) and the toxicokinetics (TK) model. At this stage, considering that the human evidence is not strong enough for RfD determination, using animal data may be appropriate but with more uncertainties. In this article, the uncertainties concerning RfDs from the choices of PoD and TK models are addressed. Firstly, the candidate PoDs should include more critical endpoints (such as immunotoxicity), which may lead to lower RfDs. Secondly, the reliability of the adopted three-compartment TK model is compromised: the parameters are not non-biologically plausible; and this TK model was applied to simulate gestation and lactation exposures, while the two exposure scenarios were not actually included in the model structure.

Simulation of images of CDMAM phantom and the estimation of measurement uncertainties of threshold gold thickness

PURPOSE

To demonstrate a method of simulating mammography images of the CDMAM phantom and to investigate the coefficient of variation (CoV) in the threshold gold thickness (tT) measurements associated with use of the phantom.

METHODS

The noise and sharpness of Hologic Dimensions and GE Essential mammography systems were characterized to provide data for the simulation. The simulation method was validated by comparing the tT results of real and simulated images of the CDMAM phantom for three different doses and the two systems. The detection matrices produced from each of 64 images using CDCOM software were randomly resampled to create 512 sets of 8, 16 and 32 images to estimate the CoV of tT. Sets of simulated images for a range of doses were used to estimate the CoVs for a range of diameters and threshold thicknesses.

RESULTS

No significant differences were found for tT or the CoV between real and simulated CDMAM images. It was shown that resampling from 256 images was required for estimating the CoV. The CoV was around 4% using 16 images for most of the phantom but is over double that for details near the edge of the phantom.

CONCLUSIONS

We have demonstrated a method to simulate images of the CDMAM phantom for different systems at a range of doses. We provide data for calculating uncertainties in tT. Any future review of the European guidelines should take into consideration the calculated uncertainties for the 0.1mm detail.