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. THE JOURNAL OF CHEMICAL THERMODYNAMICS 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.