Low Level Of Uncertainty Research Articles

Measuring thermal properties of thin layers with rough surfaces by using the bidirectional heat flow approach

Abstract Thermophysical properties of materials and the optimization of the heat transfer are becoming more and more important for industrial applications of micro- and nanoelectronic devices. Thin layers in the micrometer to nanometer range are used to give specific functions to the devices. Since the thermophysical properties of thin layers differ from bulk material, this data is required for precise predictions of thermal management. One way to obtain the thermal properties of thin layers is the optical-based Time Domain Thermoreflectance (TDTR) method. To carry out TDTR measurements with a low level of uncertainty, the samples under study must meet requirements related to the surface roughness and a low level of optical scattering. The range of samples analysable by TDTR can be extended by applying the so-called bidirectional heat flow approach. This approach opens the possibility to assess thermal properties of materials with rough surfaces as well. The validity of the implemented model was shown by the characterisation of a test sample with well-known thermal properties fabricated for this purpose out of poly(methyl methacrylate) (PMMA) roughened with acetone:ethanol. The results obtained by TDTR measurements were compared to literature values, demonstrating the applicability of the bidirectional heat flow approach for this setup.

Skill Acquisition and Development Issues with Predictable Badminton Feeding Routines

Badminton practice designs contain feeding routines that allow players to practice technical skills. Optimal skill acquisition and development is gained through practice simulating competitive match play rather than approaches centred around mass repetition with low levels of uncertainty. This study aimed to evaluate the biomechanical differences in twelve youth elite UK badminton players in activities with varying levels of predictability. The three experimental trials were handfeed, multifeed and match play trials. Motion capture cameras were used to record participant biomechanical data. Data analysis revealed several significant biomechanical differences between trials. Results showed that an increase in trial unpredictability led to increased depth of the forward lunge shot and a lower racket shuttlecock strike position with greater forward trunk bend. This study showed that visual search behaviour in badminton is a key component of the forward lunge shot. Predictable practice feeding routines may cause poor skill acquisition and development through maladaptive learning behaviours. Coaches are recommended to implement unpredictable feeding practice routines to simulate the visual search behaviour of competitive badminton match play to enhance skill acquisition and development in youth elite badminton players.

Rola ekonomisty w nowożytnych demokracjach. „Nadstawianie karku” czy „dostarczanie wiedzy”? Między Talebem a Pielke’em

Economists, and more broadly – scientists who want their work to benefit specific communities or all of humanity – must pay attention to the role they play in public debate. An inadequate approach may result in the instrumental use of science and its politicisation. The article analyses two approaches regarding the role of the researcher in contemporary public debate. N. Taleb’s concept of a researcher with “skin in the game” and R. Pielke’s approach promoting the model of the so-called “honest broker”. Since these two approaches are partially contradictory, the aim of the article was to highlight the existing contradictions and to work out a compromise position that can be described as an “honest broker with skin in the game”. A study conducted using content analysis showed that (1) researchers should actively engage in the public popularisation of their works and proactively influence decision-makers related to their area of expertise, (2) in certain social sciences (characterised by a low level of uncertainty) – the responsibility of the researcher should be higher. It means that its exposure to the risk related to the consequences of false recommendations or bad predictions – should be higher. This issue is important in the context of the effectiveness of researchers’ involvement in policymaking, especially in a controversial area such as income distribution.

Reduced-Space Relevance Vector Machine for Adaptive Electrical Capacitance Volume Tomography

We introduce an efficient synthetic electrode selection strategy for use in Adaptive Electrical Capacitance Volume Tomography (AECVT). The proposed strategy is based on the Adaptive Relevance Vector Machine (ARVM) method and allows to successively obtain synthetic electrode configurations that yield the most decrease in the image reconstruction uncertainty for the spatial distribution of the permittivity in the region of interest. The problem is first formulated as an instance of the Quadratic Unconstrained Binary Optimization (QUBO). By noting that the QUBO formulation is an NP-hard problem and thus prohibitive in practice, we then introduce the Reduced ARVM method, corresponding to the application of the ARVM method to a reduced search space. By using the Reduced ARVM method, good image reconstruction and low uncertainty levels can be achieved in AECVT with considerably fewer measurements. To corroborate our analysis, we present simulation results for three representative AECVT scenarios.

Low field NMR as an alternative technique to estimate of density and viscosity in toluene-heavy oil mixtures

The success of low field Nuclear Magnetic Resonance (LF-NMR) to estimate heavy oil properties depends on a good selection of mathematical models and fitting parameters. Since the correlations proposed are not universally applicable, in this study, a NMR published model was chosen and tuned to determine the density and viscosity of several mixtures of a Colombian heavy oil with toluene. The process began by mixing toluene with heavy oil to obtain several measuring points with properties similar to those of heavy oils. Each mixture was taken to a 7.5 MHz spectrometer at 40°C, where NMR parameters were acquired and used in the five pre-selected mathematical models. The reliability of viscosity measurements was analysed with the root mean square error (RMSE) and maximum absolute error (MAE). After the NLS regression process, the most accurate prediction was reached through the Burcaw model, with RMSE values of 40.55 cP. On the other hand, the density was estimated with the Wen correlation with results showing a relative error percentage of less than 1%. According to such error values, the tuned models are considered a starting point to extend the NRM technique use to other Colombian heavy oils with low uncertainty levels.

Deep reinforcement learning based path stretch vector resolution in dense traffic with uncertainties

With the continuous growth in the air transportation demand, air traffic controllers will have to handle increased traffic and consequently, more potential conflicts. This gives rise to the need for conflict resolution advisory tools that can perform well in high-density traffic scenarios given a noisy environment. Unlike model-based approaches, learning-based approaches can take advantage of historical traffic data and flexibly encapsulate environmental uncertainty. In this study, we propose a reinforcement learning approach that is capable of resolving conflicts, in the presence of traffic and inherent uncertainties in conflict resolution maneuvers, without the need for prior knowledge about a set of rules mapping from conflict scenarios to expected actions. The conflict resolution task is formulated as a decision-making problem in a large and complex action space. The research also includes the development of a learning environment, scenario state representation, reward function, and a reinforcement learning algorithm inspired from Q-learning and Deep Deterministic Policy Gradient algorithms. The proposed algorithm, with two stages decision-making process, is used to train an agent that can serves as an advisory tool for air traffic controllers in resolving air traffic conflicts where it can learn from historical data by evolving overtime. Our findings show that the proposed model gives the agent the capability to suggest high quality conflict resolutions under different environmental conditions. It outperforms two baseline algorithms. The trained model has high performance under low uncertainty level (success rate ≥95% ) and medium uncertainty level (success rate ≥87%) with high traffic density. The detailed analysis of different impact factors such as environment’s uncertainty and traffic density on learning performance are investigated and discussed. The environment’s uncertainty is the most important factor which affects the performance. Moreover, the combination of high-density traffic and high uncertainty will be the challenge for any learning models.

The joint stochastic lot sizing and pricing problem

We consider a joint stochastic lot sizing pricing problem where period demands are price dependent and stochastic. Herein, we propose an alternative joint inventory control pricing policy for the profit-optimal (s,S,p) policy. The proposed policy – referred to as (R,S,p) is an order-up-to policy where replenishment schedule and price levels for each period are set before the planning horizon starts. As such, this policy can be considered as rather static as compared to the dynamic (s,S,p) policy. We also provide a quadratic mixed integer programming formulation in order to obtain the optimal (R,S,p) policy parameters. Furthermore, (R,S,p) policy is compared against the profit-optimal (s,S,p) policy on an extensive numerical study where several different benchmark policies are additionally used. The results show that the proposed policy exhibits a competitive profit performance with respect to the profit-optimal policy especially for low or moderate levels of demand uncertainty. The results also reveal that the relative benefit of using dynamic pricing policy, as compared to that of using dynamic inventory control, diminishes as demand uncertainty escalates.

Multi-Criteria Performance Evaluation of Gridded Precipitation and Temperature Products in Data-Sparse Regions

Inadequate climate data stations often make hydrological modelling a rather challenging task in data-sparse regions. Gridded climate data can be used as an alternative; however, their accuracy in replicating the climatology of the region of interest with low levels of uncertainty is important to water resource planning. This study utilised several performance metrics and multi-criteria decision making to assess the performance of the widely used gridded precipitation and temperature data against quality-controlled observed station records in the Lake Chad basin. The study’s findings reveal that the products differ in their quality across the selected performance metrics, although they are especially promising with regards to temperature. However, there are some inherent weaknesses in replicating the observed station data. Princeton University Global Meteorological Forcing precipitation showed the worst performance, with Kling–Gupta efficiency of 0.13–0.50, a mean modified index of agreement of 0.68, and a similarity coefficient SU = 0.365, relative to other products with satisfactory performance across all stations. There were varying degrees of mismatch in unidirectional precipitation and temperature trends, although they were satisfactory in replicating the hydro-climatic information with a low level of uncertainty. Assessment based on multi-criteria decision making revealed that the Climate Research Unit, Global Precipitation Climatology Centre, and Climate Prediction Centre precipitation data and the Climate Research Unit and Princeton University Global Meteorological Forcing temperature data exhibit better performance in terms of similarity, and are recommended for application in hydrological impact studies—especially in the quantification of projected climate hazards and vulnerabilities for better water policy decision making in the Lake Chad basin.

The Curvilinear Relationship Between Collective Team Identification and New Venture Performance: The Moderating Effect of Environmental Uncertainty

Abstract Many new ventures are founded and developed by teams rather than solo entrepreneurs. Therefore, the extent to which entrepreneurs identify with their teams is likely to have an important impact on the process and outcome of new venture creation in new venture teams. However, most of the relevant studies focus on entrepreneurs’ individual identity, and the identity at the team level has been overlooked. This study seeks to fill this gap by exploring the effect of collective team identification on new venture performance. The relationship between collective team identification and new venture performance was examined using a sample of 54 new venture teams in Internet Technology (IT) industry. The results show that the relationship between collective team identification and new venture performance is inverted U-shaped. Moreover, environmental uncertainty may moderate this curvilinear effect, such that this inverted U-shaped relationship is more salient at a low level of environmental uncertainty rather than at a high level of environmental uncertainty.

Traceable Nanoscale Measurements of High Dielectric Constant by Scanning Microwave Microscopy.

The importance of high dielectric constant materials in the development of high frequency nano-electronic devices is undeniable. Their polarization properties are directly dependent on the value of their relative permittivity. We report here on the nanoscale metrological quantification of the dielectric constants of two high-κ materials, lead zirconate titanate (PZT) and lead magnesium niobate-lead titanate (PMN-PT), in the GHz range using scanning microwave microscopy (SMM). We demonstrate the importance of the capacitance calibration procedure and dimensional measurements on the weight of the combined relative uncertainties. A novel approach is proposed to correct lateral dimension measurements of micro-capacitive structures using the microwave electrical signatures, especially for rough surfaces of high-κ materials. A new analytical expression is also given for the capacitance calculations, taking into account the contribution of fringing electric fields. We determine the dielectric constant values εPZT = 445 and εPMN-PT = 641 at the frequency around 3.6 GHz, with combined relative uncertainties of 3.5% and 6.9% for PZT and PMN-PT, respectively. This work provides a general description of the metrological path for a quantified measurement of high dielectric constants with well-controlled low uncertainty levels.

A consistent safety format and design approach for brittle systems and application to textile reinforced concrete structures

Design and verification of structures in modern codes of practice account for a safety format, ensuring that the probability of failure does not exceed a given threshold. Although specific safety formats are proposed in some cases for special types of structures or analyses, most designs and verifications are currently performed on the basis of the Partial Safety Factor Format (PSFF). This format is applied to cover different materials and structural responses, allowing for a uniform methodology to account for reliability. Such consideration greatly simplifies the design process, but raises concerns on its consistency when different structural responses are observed. In the PSFF as considered in fib Model Code and Eurocodes, no explicit distinction is made on the value of the partial safety factors (for actions or materials) depending on whether a structural system has a brittle or a ductile response. This can be potentially inconsistent, as brittle systems have limited or no redistribution capacity of internal forces (which can give rise to premature failures if action effects are poorly estimated), while ductile systems have large potentials to redistribute internal forces and are thus little sensitive to this issue.In this paper, to investigate on the suitability of PSFF for brittle structures, the most suitable manner to determine internal forces for brittle elements failing in bending and the corresponding model uncertainties of action effects are investigated in detail. The concepts are derived from a theoretical perspective and applied to the case of Textile Reinforced Concrete (TRC). This material is a promising development to reduce the footprint of concrete construction and to build lightweight structures, but exhibits a very brittle response in bending (contrary to ordinary reinforced concrete with usual reinforcement ratios). In this paper, by means of an experimental and theoretical investigation, it is shown that following a suitable approach to estimate internal forces for brittle systems as TRC leads to a low level of model uncertainty of action effects. This leads to the conclusion that, compared to standard design of ductile systems, no additional correction is required for safety issues. Following this outcome, the partial factors for TRC structures are calibrated. In addition, due to the significance of geometrical uncertainties, a method for designing TRC on the basis of a design value of the effective depth (a reduced value accounting for construction tolerances instead of its nominal dimension) is eventually discussed, showing that it allows for a more uniform level of safety.

Time-lapse borehole gravity imaging of CO2 injection and withdrawal in a closed carbonate reef

Three borehole gravity (BHG) surveys were performed in 2013, 2016, and 2018 to monitor the changes in gravity/density as a result of the injection and withdrawal of carbon dioxide ([Formula: see text]) into and out of the Dover 33 carbonate reservoir reef in Northern Michigan. The observed gravity changes and inferred density changes have been modeled to determine the flow and storage zones of the injected [Formula: see text] in the reef. The high quality and low level of uncertainty of the data collected make them useful for delineating the [Formula: see text] plume position over time and for identifying the oil sweeping extent and mechanisms in the Dover 33 reef. The time-lapse gravity results indicate the effects of the changing [Formula: see text] mass within the reservoir, consistent with increasing mass from 2013 to 2016 (following [Formula: see text] injection) and a decreasing mass from 2016 to 2018 (after [Formula: see text] withdrawal). Three-dimensional imaging of fluid migrations in the reef has been obtained by coupling the time-lapse BHG results to a 3D porosity and permeability model. This coupled approach allows the evaluation of the volume of the reef affected by the injection of [Formula: see text] between 2013 and 2016, the efficiency of the oil sweeping between 2016 and 2018, and the location of the residual [Formula: see text] plume in the reef after 2018.

Health-related quality of life, uncertainty and coping strategies in solid organ transplant recipients during shielding for the COVID-19 pandemic.

SummaryStrict isolation of vulnerable individuals has been a strategy implemented by authorities to protect people from COVID‐19. Our objective was to investigate health‐related quality of life (HRQoL), uncertainty and coping behaviours in solid organ transplant (SOT) recipients during the COVID‐19 pandemic. A cross‐sectional survey of adult SOT recipients undergoing follow‐up at our institution was performed. Perceived health status, uncertainty and coping strategies were assessed using the EQ‐5D‐5L, Short‐form Mishel Uncertainty in Illness Scale (SF‐MUIS) and Brief Cope, respectively. Interactions with COVID‐19 risk perception, access to health care, demographic and clinical variables were assessed. The survey was completed by 826 of 3839 (21.5%) invited participants. Overall, low levels of uncertainty in illness were reported, and acceptance was the major coping strategy (92%). Coping by acceptance, feeling protected, self‐perceived susceptibility to COVID‐19 were associated with lower levels of uncertainty. Health status index scores were significantly lower for those with mental health illness, compromised access to health care, a perceived high risk of severe COVID‐19 infection and higher levels of uncertainty. A history of mental health illness, risk perceptions, restricted healthcare access, uncertainty and coping strategies was associated with poorer HRQoL in SOT recipients during strict isolation. These findings may allow identification of strategies to improve HRQoL in SOT recipients during the pandemic.

Models of genetic code structure evolution with variable number of coded labels

It is assumed that at the early stage of cell evolution its translation machinery was characterized by high noise, i.e. ambiguous assignment of codons to amino acids in the genetic code, which initially encoded only few amino acids. Next, during its evolution new amino acids were added to this code. Taking into account this facts, we investigated theoretical models of genetic code’s structure, which evolved from a set of ambiguous codons assignments into a coding system with a low level of uncertainty. We considered three types of translational inaccuracies assuming a different number of fixed codon positions. We applied a modified version of evolutionary algorithm for finding the genetic codes that the most effectively reduced the initial uncertainty in the assignment of codons to encoded labels, i.e. amino acids and a stop translation signal. We examined codes with the number of labels from four to 22. Our results indicated that the quality of genetic code structure is strongly dependent on the number of encoded labels as well as the type of translational mechanism. The more strict assignments of codon to the labels was preferred by the codes encoding more number of labels. The results showed that a smaller degeneracy of codes evolved from a more tolerant coding with the stepwise addition of coded amino acids to the genetic code. The distribution of codon groups in the standard genetic code corresponds well to the translation model assuming two fixed codon positions, whereas the six-codon groups can be relics form previous stages of evolution when the code characterized by a greater uncertainty.

Feelings of uncertainty and powerlessness from Covid-19: Implications for advertising appeals in the restaurant industry

Are rational (emotional) advertising appeals more congruent with healthy (indulgent) products? Prior research shows inconsistent results on the congruency effect between appeal type and product category. This study sheds light on this topic by examining the moderating effect of two psychological states (i.e., feelings of uncertainty and powerlessness) caused by Covid-19 on the relative effectiveness of rational vs. emotional appeals in the context of food advertisements. The results suggest that people with low levels of uncertainty respond more favorably to rational (vs. emotional) appeals of a healthy food item, and this effect is attenuated among people with high levels of uncertainty. On the other hand, powerful people find emotional (vs. rational) appeals of an indulgent food item more attractive, and this effect is attenuated among powerless people. Theoretical and managerial implications are discussed. • The congruency effect between advertising appeal and product category depends on feelings of uncertainty and powerlessness. • Individuals with low levels of uncertainty respond more favorably to rational (vs. emotional) appeals of a healthy food item. • Individuals with high levels of uncertainty do not show such a tendency. • Powerful people find emotional (vs. rational) appeals of an indulgent food item more attractive. • Powerless people show no preference.

Textbooks of doubt, tested: the effect of a denialist framing on adolescents’ certainty about climate change

In US school settings and materials, climate change is often framed as an uncertain phenomenon. However, the effect of such denialist representations on youth’s perceptions of climate change has not been empirically tested. To address this gap in the literature, this article reports on a survey-based experiment testing two framings of uncertainty about the causes and effects of climate change—one with a high level of uncertainty and one with a low level of uncertainty—on students’ knowledge, attitudes, and behaviours related to climate change. The experiment was conducted with 453 middle and high school students . Students who read a text portraying climate change with high uncertainty reported lower levels of certainty about human-caused climate change . To explore how the students engaged cognitive resources when reading the experimental texts, regression analyses were used to test two hypotheses. The Knowledge Thesis predicts that youth will use their prior knowledge to evaluate the text, and the Norms Thesis predicts that youth will use the perceived norms of their social group to evaluate the text. Results suggested that students did not respond to the treatment differentially, given their differing levels of prior knowledge nor social norms accepting of climate change . Implications for practice include the necessity of explicit scaffolds to support deep critical engagement with informational, or dis-informational, text about climate change.

Effectives of monetary policy under the high and low economic uncertainty states: Evidence from the major Asian economies

This study examines the monetary policy effectiveness of five major Asian countries (China, Hong Kong, India, Japan, and South Korea) using a quantile vector autoregression (QVAR) model-based spillover estimation approach of Balcilar et al. (2020b) at different quantile paths. To do this, we first obtain the spillover index from interest rate to industrial production and consumer price index under the high and low levels of uncertainty. The full sample results from our analysis provide partial supporting evidence for the economic theory, which asserts that monetary policy efficiency must fall during periods of high economic uncertainty. Furthermore, this approach also allows us to uncover asymmetric effects of economic policy uncertainty and lending rate on macroeconomic indicators. The impacts of interest rate and domestic and foreign (both the EPU of US and EU) uncertainty shocks on major Asian markets present significant asymmetric characteristics. Moreover, our time-varying results suggest that monetary policy shocks are more effective and potent on Asian economies during the low and high uncertainty periods than normal economic periods.

COVID-19 Uncertainty and Temporal Framing in Advertising for Online Experiential Consumption

During the period of uncertainty and lack of control caused by the COVID-19 pandemic, we investigated how consumers evaluate online experiences using messages framed in different temporal perspectives. In the midst of the coronavirus outbreak, companies have been offering their experiential services online either in single long sessions (i.e., proximal framed message) or multiple short sessions over time (i.e., distal framed message). We found significant two-way interactions between temporal frames and levels of uncertainty, such that consumers with high (vs. low) levels of uncertainty respond more favorably when a distal (vs. proximal) framed message is applied. The path from fluency to high-control coping explained the interaction effect between temporal framing and levels of uncertainty on behavioral outcomes toward experience goods.

Classifying ecosystem stressor interactions: Theory highlights the data limitations of the additive null model and the difficulty in revealing ecological surprises.

Understanding how multiple co-occurring environmental stressors combine to affect biodiversity and ecosystem services is an on-going grand challenge for ecology. Currently, progress has been made through accumulating large numbers of smaller-scale empirical studies that are then investigated by meta-analyses to detect general patterns. There is particular interest in detecting, understanding and predicting 'ecological surprises' where stressors interact in a non-additive (e.g. antagonistic or synergistic) manner, but so far few general results have emerged. However, the ability of the statistical tools to recover non-additive interactions in the face of data uncertainty is unstudied, so crucially, we do not know how well the empirical results reflect the true stressor interactions. Here, we investigate the performance of the commonly implemented additive null model. A meta-analysis of a large (545 interactions) empirical dataset for the effects of pairs of stressors on freshwater communities reveals additive interactions dominate individual studies, whereas pooling the data leads to an antagonistic summary interaction class. However, analyses of simulated data from food chain models, where the underlying interactions are known, suggest both sets of results may be due to observation error within the data. Specifically, we show that the additive null model is highly sensitive to observation error, with non-additive interactions being reliably detected at only unrealistically low levels of data uncertainty. Similarly, plausible levels of observation error lead to meta-analyses reporting antagonistic summary interaction classifications even when synergies co-dominate. Therefore, while our empirical results broadly agree with those of previous freshwater meta-analyses, we conclude these patterns may be driven by statistical sampling rather than any ecological mechanisms. Further investigation of candidate null models used to define stressor-pair interactions is essential, and once any artefacts are accounted for, the so-called 'ecological surprises' may be more frequent than was previously assumed.

Associations between uncertainties identified by the European Medicines Agency and national decision making on reimbursement by HTA agencies.

We aimed to determine whether uncertainties identified by the European Medicines Agency (EMA) were associated with negative relative effectiveness assessments (REAs) and negative overall reimbursement recommendations by national health technology assessment (HTA) agencies. Therefore, we identified all HTA reports from Haute Autorité de Santé (HAS; France), National Institute for Health and Care Excellence (NICE; England/Wales), Scottish Medicine Consortium (SMC; Scotland), and Zorginstituut Nederland (ZIN; The Netherlands) for a cohort of innovative medicines that the EMA had approved in 2009 to 2010 (excluding vaccines). Uncertainty regarding pivotal trial methodology, clinical outcomes, and their clinical relevance were combined to reflect a low, medium, or high level of uncertainty. We assessed associations by calculating risk ratios (RRs) and 95% confidence intervals (CIs), and agreement between REA and overall reimbursement recommendation outcomes. We identified 36 medicines for which 121 reimbursement recommendations had been issued by the HTA agencies between September 2009 and July 2018. High versus low uncertainty was associated with an increased risk for negative REAs and negative overall reimbursement recommendations: RRs 1.9 (95% CI 0.9–3.9) and 1.6 (95% CI 0.7–3.5), respectively, which was supported by further sensitivity analyses. We identified a lack of agreement between 33 (27%) REA and overall reimbursement recommendation outcomes, which were mostly restricted recommendations that followed on negative REAs in case of low or medium uncertainty. In conclusion, high uncertainty identified by the EMA was associated with negative REAs and negative overall reimbursement recommendations. To reduce uncertainty and ultimately facilitate efficient patient access, regulators, HTA agencies, and other stakeholders should discuss how uncertainties should be weighed and addressed early in the drug life cycle of innovative treatments.