Related Uncertainties Research Articles

Differential effects of physical and financial threats in weight loss advertising: a case of uncertainty

PurposeAdvertisements often use fear appeals to encourage prevention focused behaviors. This approach has been somewhat successful in changing attitudes and behaviors, often encouraging consumers to secede from behaviors such as smoking or to adopt preventative behaviors such as engaging in health screenings. However, health-care marketers have been less successful in efforts to reduce obesity. The obesity crisis has led to an abundance of marketing communications designed to influence weight loss. Many of these focus on fear of physical health risks associated with being overweight which have a certain degree of uncertainty surrounding them. This study aims to examine financial threats that have lower perceptions of uncertainty, and the differential impact this type of threat has on elements of the Extended Parallel Process Model (EPPM).Design/methodology/approachA 2 × 2 experimental design is used to examine the differential impact of messages communicating threat of financial and physical risk on evoked fear, perceived uncertainty, perceived susceptibility, efficacy and intention to lose weight.FindingsOverall results indicate that response to weight loss advertising varies given the type of threat presented. Results indicate that there is a greater level of uncertainty associated with physical health threats than that with financial threats. Moreover, even though individuals were more fearful of and felt more susceptible to physical threats, when they believed that the recommended behavior was feasible, financial threat was more influential.Originality/valueTo encourage weight loss and intentions to lose weight advertising in practice and advertising research primarily focus on the physical health risks associated with being overweight as a motivating factor. Current research explores the impact of financial threats on attitudes and behavioral intention and finds that financial threats are perceived as more certain than physical threats, and the communication of financial threats is more salient in its effect on weight loss intentions. An opportunity for future research is to further explore the impact of uncertainty in relation to components of EPPM and how threats varying in degrees of uncertainty may impact weight loss intentions.

Managing uncertainty in decision-making for conservation science.

Science-based decision-making is the ideal. However, scientific knowledge is incomplete, and sometimes wrong. Responsible science-based policy, planning, and action must exploit knowledge while managing uncertainty. I considered the info-gap method to manage deep uncertainty surrounding knowledge that is used for decision-making in conservation. A central concept is satisficing, which means satisfying a critical requirement. Alternative decisions are prioritized based on their robustness to uncertainty, and critical outcome requirements are satisficed. Robustness is optimized; outcome is satisficed. This is called robust satisficing. A decision with a suboptimal outcome may be preferred over a decision with a putatively optimal outcome if the former can more robustly achieve an acceptable outcome. Many biodiversity conservation applications employ info-gap theory, under which parameter uncertainty but not uncertainty in functional relations is considered. I considered info-gap models of functional uncertainty, widely used outside of conservation science, as applied to conservation of a generic endangered species by translocation to a new region. I focused on 2 uncertainties: the future temperature is uncertain due to climate change, and the shape of the reproductive output function is uncertain due to translocation to an unfamiliar region. The value of new information is demonstrated based on the robustness function, and the info-gap opportuneness function demonstrates the potential for better-than-anticipated outcomes.

A four‐stage framework for optimal scheduling strategy of smart prosumers with vehicle‐to‐home capability under real time pricing based on interval optimization

AbstractWith the emergence of the Smart Grid concept, utility companies require more active participation of home users in the power sector. This changing paradigm is enabled by the wide deployment of multiple home assets such as small renewable‐based generators or storage facilities. In this context, consumers are no longer conceived as pure loads but also active agents that can exchange energy with the grid. To promote this active participation, utility companies promote different price‐based demand response programs to change the consumer patterns on pursuing a more efficient and economic system operation. In this regard, home energy management programs are becoming an essential tool for efficiently managing the different home users while addressing multiple demand response goals at minimum cost. In essence, a home energy management system is a computational optimization tool, which has to handle multiple uncertainties brought by weather forecast or energy pricing. This paper tackles this issue by developing a novel robust home energy management program based on interval optimization. In contrast to other related approaches, the proposal avoids the explicit use of interval arithmetic. Instead, the different uncertain parameters are sequentially incorporated into the scheduling task through different stages and interval‐based formulation. The developed methodology incorporates weather, load, energy pricing and plug‐in electric vehicle related uncertainties. A benchmark case study in a smart prosumer layout serves to prove the effectiveness of the new approach.

ThesProcess and Beyond

Neutron captures produce the vast majority of abundances of elements heavier than iron in the Universe. Beyond the classical slow ( s) and rapid ( r) processes, there is observational evidence for neutron-capture processes that operate at neutron densities in between, at different distances from the valley of β stability. Here, we review the main properties of the s process within the general context of neutron-capture processes and the nuclear physics input required to investigate it. We describe massive stars and asymptotic giant branch stars as the s-process astrophysical sites and discuss the related physical uncertainties. We also present current observational evidence for the s process and beyond, which ranges from stellar spectroscopic observations to laboratory analysis of meteorites.

Laser-driven ramp-compression experiments on the national ignition facility

This report details the analyses and related uncertainties in measuring longitudinal-stress–density paths in indirect laser-driven ramp equation-of-state (EOS) experiments [Smith et al., Nat. Astron. 2(6), 452–458 (2018); Smith et al., Nature 511(7509), 330–333 (2014); Fratanduono et al., Science 372(6546), 1063–1068 (2021); and Fratanduono et al., Phys. Rev. Lett. 124(1), 015701 (2020)]. Experiments were conducted at the National Ignition Facility (NIF) located at the Lawrence Livermore National Laboratory. The NIF can deliver up to 2 MJ of laser energy over 30 ns and provide the necessary laser power and control to ramp compress materials to TPa pressures (1 TPa = 10 × 106 atmospheres). These data provide low-temperature solid-state EOS data relevant to the extreme conditions found in the deep interiors of giant planets. In these experiments, multi-stepped samples with thicknesses in the range of 40–120 µm experience an initial shock compression followed by a time-dependent ramp compression to peak pressure. Interface velocity measurements from each thickness combine to place a constraint on the Lagrangian sound speed as a function of particle velocity, which in turn allows for the determination of a continuous stress–density path to high levels of compressibility. In this report, we present a detailed description of the experimental techniques and measurement uncertainties and describe how these uncertainties combine to place a final uncertainty in both stress and density. We address the effects of time-dependent deformation and the sensitivity of ramp EOS techniques to the onset of phase transformations.

Robust Face Alignment via Inherent Relation Learning and Uncertainty Estimation.

Human tends to locate the facial landmarks with heavy occlusion by their relative position to the easily identified landmarks. The clue is defined as the landmark inherent relation while it is ignored by most existing methods. In this paper, we present Dynamic Sparse Local Patch Transformer (DSLPT), a novel face alignment framework for the inherent relation learning and uncertainty estimation. Unlike most existing methods that regress facial landmarks directly from global features, the DSLPT first generates a rough representation of each landmark from a local patch cropped from the feature map and then adaptively aggregates them by a case dependent inherent relation. Finally, the DSLPT predicts the coordinate and uncertainty of each landmark by regressing their probability distribution from the output features. Moreover, we introduce a coarse-to-fine framework to incorporate with DSLPT for an improved result. In the framework, the position and size of each patch are determined by the probability distribution of the corresponding landmark predicted in the previous stage. The dynamic patches will ensure a fine-grained landmark representation for inherent relation learning so that a rough prediction result can gradually converge to the target facial landmarks. We integrate the coarse-to-fine model into an end-to-end training pipeline and carry out experiments on the mainstream benchmarks. The results demonstrate that the DSLPT achieves state-of-the-art performance with much less computational complexity. The codes and models are available at https://github.com/Jiahao-UTS/DSLPT.

Amblyopia-A novel virtual round table to explore the caregiver perspective.

A survey aimed to capture the caregiver's perspective on the impact of amblyopia and its treatment on the child and family, as well as caregivers' views on the design and feasibility of clinical trials investigating dichoptic binocular therapies for amblyopia. Parents of amblyopic children, patient advocates and healthcare professionals took part in a moderated, structured discussion on a novel virtual advisory-board platform. Seven parents of children with amblyopia, two patient organisation representatives, one ophthalmologist and one optometrist participated in the survey. Atotal of 645 posts were entered on the platform over a 14-day period in September 2021. There was widespread agreement that the management of amblyopia poses more of a burden on the child and family than the condition itself, with treatment burden accentuated when treatment is unsuccessful. Parents expressed uncertainty and frustration in relation to the duration of patching, success of patching and alternative treatment options, and felt there was inadequate readily available, easy-to-understand information on the condition. Parents reported that a new treatment for amblyopia, such as dichoptic binocular therapy using video games, should be safe, non-invasive and engaging compared with an eye patch. Treating at home, potentially for a shorter treatment duration, and with an entertaining game were the main reasons parents would join a clinical study with this type of novel therapy. However, due to a limited critical period treatment window, parents would feel more comfortable joining a clinical trial if traditional therapies were offered in conjunction with those under investigation. Patient perspectives and the role of caregivers in the acceptance of any interventional treatments are increasingly recognised. Understanding how amblyopia and its treatment impacts a child and family should be an important premise to guide therapy and evaluate treatment value, both in clinical trials and in routine medical practice.

Using model-based geostatistics for assessing the elimination of trachoma.

Trachoma is the commonest infectious cause of blindness worldwide. Efforts are being made to eliminate trachoma as a public health problem globally. However, as prevalence decreases, it becomes more challenging to precisely predict prevalence. We demonstrate how model-based geostatistics (MBG) can be used as a reliable, efficient, and widely applicable tool to assess the elimination status of trachoma. We analysed trachoma surveillance data from Brazil, Malawi, and Niger. We developed geostatistical Binomial models to predict trachomatous inflammation-follicular (TF) and trachomatous trichiasis (TT) prevalence. We proposed a general framework to incorporate age and gender in the geostatistical models, whilst accounting for residual spatial and non-spatial variation in prevalence through the use of random effects. We also used predictive probabilities generated by the geostatistical models to quantify the likelihood of having achieved the elimination target in each evaluation unit (EU). TF and TT prevalence varied considerably by country, with Brazil showing the lowest prevalence and Niger the highest. Brazil and Malawi are highly likely to have met the elimination criteria for TF in each EU, but, for some EUs, there was high uncertainty in relation to the elimination of TT according to the model alone. In Niger, the predicted prevalence varied significantly across EUs, with the probability of having achieved the elimination target ranging from values close to 0% to 100%, for both TF and TT. We demonstrated the wide applicability of MBG for trachoma programmes, using data from different epidemiological settings. Unlike the standard trachoma prevalence survey approach, MBG provides a more statistically rigorous way of quantifying uncertainty around the achievement of elimination prevalence targets, through the use of spatial correlation. In addition to the analysis of existing survey data, MBG also provides an approach to identify areas in which more sampling effort is needed to improve EU classification. We advocate MBG as the new standard method for analysing trachoma survey outputs.

A unified model of data uncertainty and data relation uncertainty

Data uncertainty (DU), one of the most interesting topics in information science, inevitably leads to data relation uncertainty (DRU). Moreover, DU greatly affects the results of information processing problems such as sorting and top-k queries with uncertain data. However, studies have not clarified the mechanism, mode, and degree of impact of DU on DRU. Hence, we established a unified mathematical model to reveal the precise relation between DU and DRU. The experimental results showed this model’s high accuracy under various conditions and its relative error and absolute error of less than 0.2%. This model can be used to quantitatively estimate DRU according to a given DU and vice versa. Unexpectedly, the experiments showed that under given conditions, high DU will not necessarily lead to high DRU. This characteristic is highly significant to the design of data sorting and query algorithms with uncertain data.

A generalization of the canonical commutation relation and Heisenberg uncertainty principle for the orbital operators

Abstract In this paper, the orbits of observable physical quantities of position and momentum operators at the states of quantum Hilbert spaces are created. Also the Hilbert space of finite orbits and the Fréchet–Hilbert space of all orbits are created and the orbital operators corresponding to these observable operators in these spaces of orbits are defined and studied. We call this process the orbitization, and the obtained model the orbital quantum mechanics. The orbitization process is compared with the quantization process. The generalization of well-known canonical commutation relations for orbital operators corresponding to the position and momentum operators is established. Also, the Heisenberg uncertainty principle for the orbital operators is proved and the question of achieving equality in the Heisenberg inequality is considered.

An optimized Extreme Learning Machine by Evolutionary Computation for River Flow Prediction and Simulation of Water Pollution in Colorado River Basin, USA

Humanity is witnessing scientific advances and since one of the vital concerns has been accessing water for drinking and agriculture, scientific innovations have been used to solve water challenges. Recently, Machine Learning (ML) is one of the most promising developments, therefore, for utilization of this innovation in simulating the Water Quality-Quantity Assessment (WQA) issues, the author has developed the Extreme Learning Machine (ELM) as the stand-alone model and its combination with evolutionary algorithms (EA) to optimize the modeling of the WQA parameters. So, the Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Imperialist Competitive Algorithm (ICA) are implemented to improve the performance of WQA parameters prediction. Then, the three novel models are developed for this purpose simultaneously with the stand-alone ML model. The novel models are GAELM, PSELM, and ICELM. The study area was the Colorado River basin in the USA, which was the input dataset extracted from the US Geological Survey. The considered parameters were Power of hydrogen (pH), river flow or Debi, Electrical Conductivity (EC), and Dissolved Oxygen (DO). To over- and under-fitting ML models, the input dataset was detrended and randomized by K-fold cross-validation. Furthermore, this study used seven evaluation methods for determining the models' performances. Based on the evaluation metrics, the ICELM model was the best in the EC, DO, and pH modeling. Also, the ELM model was superior in Debi prediction. Additionally, the discrepancy percent charts for all models in simulations were drawn. Moreover, the error percent plots also were drawn for all modeling. Finally, the Wilson method analyzed the prediction errors and related uncertainties.

Applying relational turbulence theory to examine partner (under)accommodation during conversations about relational stressors

AbstractUtilizing the prepositions of relational turbulence theory (RTT), the present study proposes that relational uncertainty and partner interference assessed before the conversation about a relational stressor may polarize perceptions of partner (under)accommodation during the conversation, and those perceptions may further associate with global evaluations of relationships as turbulent measured post conversation. Fifty heterosexual couples (100 individuals) participated in the pre‐survey, lab visit, and post‐survey. Findings uncovered that for men, perceptions of partner (under)accommodation mediated the association between relational uncertainty and relational turbulence, and perceptions of partner accommodation mediated the association between partner interference and relational turbulence. Further, for women, perceptions of partner underaccommodation mediated the association between relational partner interference and relational turbulence. The results extend the utility of RTT in examining perceptions of partners' (under)accommodative behaviors during conversations about relational stressors.

An integrative palaeoenvironmental and chronostratigraphic study of the Lower Miocene in the North Alpine Foreland Basin – Are global climate signals detectable?

Uncertainties in relation to chronostratigraphic correlations have limited our understanding of the possible impact of global-scale climate signals on the epicontinental sediments of the North Alpine Foreland Basin (NAFB). In an effort to resolve the problem, we have carried out a detailed palaeoenvironmental and chronostratigraphic study of an 18-m thick section of the marine, middle Burdigalian Neuhofen Formation (NH Fm) at Mitterdorf (SE Germany). Based on section logging, sampling of microfossils and nannoplankton, and analyses of stable oxygen and carbon isotopes, we demonstrate that significant environmental changes took place during the deposition of the NH Fm at Mitterdorf. In particular, the presence of nannoplankton zones NN3 and NN4 necessitated a re-interpretation of existing magnetostratigraphic data from Mitterdorf. To determine whether the observed environmental changes can be placed in a supra-regional and global context, a new chronostratigraphic framework was established for the study area using a multi-proxy approach based on 3-D modeling, litho-, bio-, and magnetostratigraphy, already available data, and statistical analyses of new and previously published microfossil data. The results reveal that the Mitterdorf section can be correlated with polarity chrons C5Dr.2r and C5Dr.1n and that its age is 17.78–17.65 (vs. 17.65–17.50 Ma or 17.95–17.85 Ma in earlier works). Furthermore, the results show that the deposition of the entire NH Fm took place during the interval from 18.1 to 17.6 Ma, and that the accompanying environmental changes did not occur isochronously across its range. Nevertheless, comparison of literature-based global datasets with our new data indicates that the 3rd order sea level highstand at 17.85 Ma had an impact on the environment of the NH Fm. Furthermore, transition from eutrophic, open marine to mesotrophic, shallow marine conditions at Mitterdorf coincides with minima in orbital eccentricity and global carbon isotope values at 17.67 Ma. Our results are summarized in palaeoenvironmental models of three time-points within the middle Burdigalian in the NAFB.

Assay data of spent nuclear fuel: the lab-work behind the numbers

Computational modelling for spent nuclear fuel (SNF) characterization is already widely used and continuously further developed for a plethora of safety related applications and licensing issues in SNF management. An essential step in the development of these methodologies is the validation: the demonstration that the SNF elemental and isotopic composition is sufficiently accurately predicted by the code calculations. This validation step requires experimentally measured nuclide concentrations in SNF, together with an estimation of related uncertainties. The SFCOMPO 2.0 database of OECD/NEA is a database of such publicly available assay data of SNF. A basic understanding of all analytical steps that finally result in assay data of SNF is important for modelers when assessing the “fit-for-validation” requirement of an experimental dataset. The aim of this article is to explain users of such datasets the complex analytical pathway towards assay data. Points of attention, challenges and pitfalls all along the analytical pathway will be discussed, from sampling, dissolution procedures, necessary dilutions and separations, available analytical techniques, some related uncertainties, up to reporting of the results.

GRAVEL unfolding of gamma-ray spectra of CeBr3 detectors and related uncertainties

Unfolded spectra are needed for the application of some spectrum analysis methods. Frequently, iterative deconvolution methods are used, especially if the spectra have a high resolution. However, the uncertainties on the obtained results are difficult to judge. In general, the user of an unfolding code gets no information on the influence of chosen parameters, especially the choice of the first estimate of the unfolded spectrum. General studies regarding the uncertainties of unfolding algorithms are difficult to perform, if not impossible, and therefore not available. In this work, uncertainties of the results on the iterative GRAVEL algorithm, which is based on SAND II, are closely investigated by using medium resolution spectra recorded by using a CeBr3 detector. Relevant parameters are varied systematically to gain some information on the associated uncertainties on the basis of examples. The uncertainty budget of the calculation of total fluences and doses of unfolded spectra is compared with the uncertainty budget of the results obtained by applying the conversion method. The results allow, at least, the estimation of the magnitude of typical uncertainties.

Uncertainty evaluation of Monte Carlo simulated line scan profiles of a critical dimension scanning electron microscope (CD-SEM)

In recent years, precision and accuracy for a more precise critical dimension (CD) control have been required in CD measurement technology. CD distortion between the measurement by a critical dimension scanning electron microscope (CD-SEM) and a reference tool is the most important factor for a more accurate CD measurement. CD bias varies by a CD-SEM and a pattern condition. Therefore, it is urgently needed to identify, characterize, and quantify those parameters that may or may not affect the CD measurement by a CD-SEM. The sensitivity of the Monte Carlo simulated CD-SEM images with multiple physical modeling components has been studied previously. In this study, we demonstrate that the work function and elastic scattering potential models have a significant impact on secondary electron emission intensity, but their influence on the shape of the linescan profile is small, and other factors like the optical energy loss function and dielectric function models have even smaller effects. We have evaluated the uncertainty in the linescan profiles of Si line structures with different sidewall angles due to several different physical factors. It is found that when the CD is evaluated by a peak/valley method, the uncertainty of the CD is negligible. Therefore, it is concluded that the CD value and its related uncertainty are not critically related to the physical factors of the present Monte Carlo simulation model but rely dominantly on the line structure and electron beam parameters.

The impact of continuing bonds following bereavement: A systematic review

Following bereavement, continuing bonds (CBs) include engaging with memories, illusions, sensory and quasi-sensory perceptions, hallucinations, communication, actions, and belief that evoke an inner relationship with the deceased. To date, the literature has been unable to confirm whether retaining, rather than relinquishing, bonds is helpful. A mixed studies systematic literature search explored how CBs affect grief. Studies on the effect or experience of CBs on adjustment following bereavement were eligible for inclusion. Six computerized databases were searched. A total of 79 of 319 screened studies were included. Three themes were derived from the thematic analysis: (1) comfort and distress, (2) ongoing bonds and relational identity, and (3) uncertainty, conceptualizing, and spirituality. Themes describe the role of CBs for the accommodation of the death story, transformation of the relationship, meaning reconstruction, identity processes, and affirmation of spiritual belief. Results shed light on the adaptive potentials for CBs.

Building energy consumption under occupants’ behavior uncertainty in pre and post-renovation scenarios: a case study in Italy

In Europe, the energy renovation of the existing building stock is a great opportunity to significantly reduce energy consumption and greenhouse gas (GHG) emissions and reach the European sustainability targets. In this framework, building energy simulations (BES) tools are very useful in verifying energy retrofit measures' effectiveness and compliance with national standards. However, an inaccurate numerical prediction, the so-called “performance gap” between measured and numerical performance, is often obtained, mainly due to the inherent uncertainty of model input. Due to its stochastic nature, the occupants’ behavior (OB) is considered among the key contributors to this gap. However, the most recent Building Energy Model (BEM) approaches adopt deterministic hourly-defined profiles for characterizing OB, thus neglecting the related uncertainty. In this work, the impact of OB uncertainties on energy consumption (EC) prediction is evaluated by adopting a Karhunen-Loève Expansion sampling technique, used to randomly perturb OB profiles such as heating setpoint (HS), internal thermal loads (IL), and windows opening (NV). Two BEMs of a typical Italian residential building in pre- and post-renovation scenarios are considered and calibrated on real EC data. The results demonstrated that HS uncertainty has the highest impact on EC in all scenarios. Moreover, the higher the energy performance of the building, the higher the impact of OB, especially for IL and NV patterns.

Crisis Politics and the 2020 “Imposition of Restrictions Act” in Ghana

Around the globe, the Covid-19 pandemic generated uncertainties in relation to the novel virus itself, its treatment and its socio-economic effects on an international scale. In addition, various claims of truth about the pandemic added to the situation’s complexity. In this article, we analyse Ghana’s initial response to Covid-19 to illustrate how complex assemblages of political elites and knowledge production infrastructures intersected in the pursuit of capturing knowledge about an unfolding crisis. With the critical function of knowledge production about the pandemic as a lens, this article narrows in on Ghana’s crisis response and the composition of the contemporary Ghanaian political landscape it revealed, particularly in relation to declining trust in policies and State institutions.

GaiaData Release 3

Context.GaiaData Release 3 (DR3) provides a number of new data products that complement the early DR3 made available two years ago. Among these is the firstGaiacatalogue of eclipsing-binary candidates containing 2 184 477 sources with brightnesses from a few magnitudes to 20 mag in theGaiaG-band and covering the full sky.Aims.We present the catalogue, describe its content, provide tips for its use, estimate its quality, and show illustrative samples.Methods.Candidate selection is based on the results of variable object classification performed within theGaiaData Processing and Analysis Consortium. Candidates are then further filtered using eclipsing-binary-tailored criteria based on theG-band light curves. To find the orbital period, a large ensemble of trial periods is first acquired using three distinct period-search methods applied to the cleanedGlight curve of each source. TheGlight curve is then modelled with up to two Gaussians and a cosine for each trial period. The best combination of orbital period and geometric model is finally selected using Bayesian model comparison based on the BIC. A global ranking metric is provided to rank the quality of the chosen model between sources. The catalogue is restricted to orbital periods larger than 0.2 days.Results.Of ∼600 000 available crossmatches, about 530 000 of the candidates are classified as eclipsing binaries in the literature as well, and 93% of them have published periods compatible with theGaiaperiods. Catalogue completeness is estimated to be between 25% and 50%, depending on the sky region, relative to the OGLE4 catalogues of eclipsing binaries towards the Galactic Bulge and the Magellanic Clouds. The analysis of an illustrative sample of ∼400 000 candidates with significant parallaxes shows properties in the observational Hertzsprung-Russell diagram as expected for eclipsing binaries. The subsequent analysis of a subsample of detached bright candidates provides further hints for the exploitation of the catalogue. We also address the observed lack of short-period (less than a day) systems in the Magellanic Cloud in comparison to Galactic systems. The orbital periods, light-curve model parameters, and global rankings are all published in the catalogue with their related uncertainties where applicable.Conclusions.ThisGaiaDR3 catalogue of eclipsing-binary candidates constitutes the largest catalogue to date in terms of number of sources, sky coverage, and magnitude range.