Low Level Of Uncertainty Research Articles

SENTIMENT MARKET ANALYSIS ON REPLACEMENT ANNOUNCEMENT OF CHIEF EXECUTIVE OFFICER (CEO)

This study examines the market reaction to the announcement of Chief Executive Officer (CEO) measured by abnormal return. The study sample consisted of 55 CEO turnover announcements using the t-test to test information content of the announcement. The author groups the changes into two factors: (1) change process (routine and non-routine) and (2) substitute origin (inside and outside). The market reacts significantly positively to the announcement of CEO turnover routinely with the origin of the replacement from inside (inside) the company. Different reactions occur in the announcement of routine outside and non-routine insidediary CEO turnover where announcements are responded negatively and significantly. The market does not react to the announcement of non routine outside CEO changes. These results show that investors in Indonesia react positively to routine CEO turnover inside because investors believe that new CEOs will continue their strategy and leadership style and have lower levels of uncertainty. Negative reactions to routine outside CEO turnover are caused by the market belief that the successor will not continue the previous CEO's strategy and has a high degree of uncertainty. The cause of a negative market reaction to the announcement of a non-routine inside CEO turnover is that a replacement CEO will continue the old leadership style and jointly be responsible for the company's poor performance.

Evolution of sunspot like behavior in the agent based economies of bank runs

This paper describes evolution and adaptation of an agent-based model in a version of the Diamond-Dybvig, bank-run economy. In addition to pure strategy, run and no-run equilibria, the theoretical model also has a multiplicity of sunspot equilibria where agents’ expectations and subsequent actions are driven by coordination on an extrinsic random variable. In this paper, agents that adapt using the individual evolutionary (IEL) algorithm make decisions on whether or not to follow extrinsic random variable announcement on whether to ‘wait’ (not run) or ‘withdraw’ (run). Their strategies also advise them as what to do if they do not follow the announcements. The results of simulations, that are robust for a number of different parameterizations, show ‘no-run’ and ‘run’ outcomes for low levels of strategic uncertainty. However, for high level of strategic uncertainty, the IEL agents evolve strategies that follow sunspot announcements. These results correspond to those obtained in the experiments with human subjects. Moreover, the results scale-up when the number of agents increases ten-fold and twenty-fold.

The Justifiability of Financial Reporting Preferences in Accounting for Intangibles in Business Combinations

We test whether an impending change in the accounting for goodwill mitigates bias in the initial values that acquirers assign to intangible assets. Results of two experiments with experienced professional participants suggest that the new accounting alters, but does not eliminate, bias in acquisition-date fair values. Specifically, we provide the new insight that acquirers have a preference to minimize the value of recognized goodwill when the initial fair values assigned to intangibles do not affect future earnings, as under the planned accounting change. Further, this preference regarding the composition of the balance sheet appears to be specific to goodwill. Our research also complements previous archival evidence by providing causal evidence that acquirers are more likely to bias initial fair values when there is significant uncertainty in their private information about asset value. In contrast, a low level of uncertainty “psychologically ties acquirers’ hands” such that they do not bias their fair value estimates. Our theory and results provide important input to standard setters and practitioners regarding an unanticipated consequence of the impending change in accounting for intangible assets.

A Bayesian Approach to Modal Acoustic Emission Source Location

Modal Acoustic Emission (MAE) is a branch of Acoustic Emission (AE) with proven capabilities for Structural Health Monitoring (SHM) of plate-like structures. MAE differences from AE in that MAE uses the understanding of the wave propagation to characterize and locate the source. The analysis of the waveform includes the use of time frequency techniques to determine the Time Of Arrival (TOA) of the different modes. This paper proposes the use of Bayesian inference to quantify the uncertainty in the source location for two different MAE location techniques. The first technique uses only the TOA of the extensional (symmetric) mode, while the second technique uses the TOA of both extensional and flexural (antisymmetric) modes. The Morlet wavelet is used to determine the scalogram of the waveform. The scalogram is reassigned and Markov Chain Monte Carlo (MCMC) is used to sample the posterior distribution built through Bayesian inference. Results are presented from location of Pencil Lead Breaks (PLBs) in an aluminum plate of 1/8in of thickness and 36in by 36in. Results show that using the TOA of only the symmetric mode leads to a lower level of uncertainty compared to using both extensional and flexural modes, because of the difficulty in assessing the time of arrival of the flexural mode.

The Effects of Polarization on Ideological Certainty: An Application to Executive Order Issuance

Many standard models of political institutions frame outcomes as a function of the preferences of key decision makers. However, these models, and the empirical analyses they inspire, typically assume decision makers can infer the identities and ideological locations of other decision makers without error. Here, we reveal the substantive importance of this assumption. We show that partisan sorting, a common cause of polarization, can result in reduced uncertainty about the ideologies of key decision makers and the identities of key pivots. When we incorporate estimates of pivot uncertainty into empirical models of executive order issuance, we find lower levels of uncertainty are associated with higher rates of policy-relevant executive order issuance. These results have implications for the study of polarization and the use of models of institutions in political science.

An optimized fuzzy-genetic algorithm for metal foam manufacturing process control

The present investigation deals with the proposal of a combined fuzzy-genetic algorithm (F-GA) model able to describe the inherent uncertainties related to the manufacture of open-cell aluminum foams by using the dissolution and sintering process (DSP). The use of the F-GA method allows to take into account, within the same model, both the uncertainty related to the model and the statistical manufacturing process variability. The developed model is aimed at controlling the capability of this material at absorbing energy in compressive deformation, for a different set of process parameters. In particular, the use of genetic algorithms allows the optimization of the support of the fuzzy numbers defined in the model in order to take into account most of the experimental data in combination with the smallest uncertainty. Then, the input uncertainty, related to both the process variability and the chosen model, is propagated to the output variables by the Transformation Method. The fuzzy results are then compared with the measured data and the membership level of the dataset to the uncertain model is evaluated. The process maps generated allow to select the operational parameters in order to obtain a desired process output, in combination with the lowest uncertainty level, providing, as additional information, how much the uncertainty of the model and the process varies by changing those operational parameters.

Hydrologic Observation, Model, and Theory Congruence on Evapotranspiration Variance: Diagnosis of Multiple Observations and Land Surface Models

AbstractThis paper reconciles the state‐of‐the‐art observations and simulations of evapotranspiration (ET) temporal variability through a diagnostic framework composed of an observation‐model‐theory triplet. Specifically, a confirmed theoretical tool, Evapotranspiration Temporal VARiance Decomposition (EVARD), is used as a benchmark to estimate ET monthly variance ( ) across the contiguous United States (CONUS) with inputs including hydroclimatic observations, Gravity Recovery and Climate Experiment‐based terrestrial water storage, four observation‐based products (ETRSUW by the University of Washington, ETRSMOD16 from MOD16 Global Terrestrial ET Data Set, ETFLUXNET upscaled from of fluxtower observations, and ETGLEAM from Global Land Evaporation Amsterdam Model), and four operational land surface models (LSMs: MOSAIC, NOAH, NOAH‐MP, and VIC). Five experiments are systematically designed to evaluate and diagnose possible errors and uncertainties in ET temporal variance estimated by the four observation‐based ET products and the four LSM simulations. Based on the results of these experiments, the following diagnostic hypotheses regarding the uncertainty of the observation‐based ET products are illustrated: ETRSUW captures the high signals in the Midwest with negligible bias and moderate uncertainty over the contiguous United States; ETFLUXNET systematically underestimates over CONUS but with the lowest level of uncertainty; ETRSMOD16 has medium bias with the highest level of uncertainty, and the spatial distribution of high signal from ETRSMOD16 is different from other estimates; ETGLEAM has slight negative bias and medium uncertainty, and in the West Coast is smaller than that from ETVARD. Regarding the LSMs, it is found that any of the four LSMs can be the best depending on a certain set of reference observations. The study reveals that LSMs have shown a reasonably worthy, though not perfect, capability in estimating ET and its variability in regions/aquifers with limited human interference. However, RS‐based observations and theoretical estimates suggest that all the four LSMs examined in this study are not able to accurately predict the ET variability in regions/aquifers heavily influenced by human activities like Central Valley and High Plains aquifers; they all underestimate ET variability along the West Coast due to seasonal vegetation responses to Mediterranean climate and human water use. In addition, LSMs underestimate intraannual ET variance in California and the High Plains with underestimated terrestrial storage change components in ET variance, due to the inappropriate representation of groundwater pumping and its impact on ET and other hydrologic processes. This paper urges advancing hydrologic knowledge by finding congruence among models, data, and theories.

Parental uncertainty about transferring their adolescent with congenital heart disease to adult care

AimsTo study parent's levels of uncertainty related to the transfer from pediatric to adult care in adolescents with congenital heart disease (CHD) and to identify potentially correlating factors.BackgroundParents acknowledge that during transition they struggle with finding ways of feeling secure in handing over the responsibility and letting go of control. Well‐prepared and informed parents who feel secure are most likely better skilled to support their adolescent and to hand over the responsibility.DesignA cross‐sectional study.MethodsOverall, 351 parents were included (35% response rate). Parental uncertainty was assessed using a Linear Analogue Scale (0–100). Data were collected between January ‐ August 2016. Potential correlates were assessed using the readiness for transition questionnaire and sociodemographic data.ResultsThe mean parental uncertainty score was 42.5. Twenty‐four percent of the parents had a very low level of uncertainty (score 0–10) and 7% had a very high level (score 91–100). Overall, 26% of the mothers and 36% of the fathers indicated that they had not started thinking of the transfer yet. The level of uncertainty was negatively associated with the level of perceived overall readiness. Adolescents' age, sex, CHD complexity, and parental age were not related to uncertainty.ConclusionA wide range in the levels of uncertainty was found. Parents who were less involved in the care, or perceived their adolescent as readier for the transition, felt less uncertain. Still, thirty percent of the parents had not started to think about the transfer to adult care.

Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds

Real-time health diagnostics/prognostics and predictive maintenance/control of lithium-ion (Li-ion) batteries are essential to reliable and safe battery operation. This paper presents a physics-based (or mechanistic) approach to Li-ion battery prognostics, which enables online prediction of remaining useful life (RUL) with consideration of multiple concurrent degradation mechanisms. In the proposed approach, robust online prediction of RUL is achieved by employing a non-linear least squares method with dynamic bounds that traces the evolution of individual degradation parameters. The novelty of this approach lies in its ability to incorporate mechanistic degradation analysis results into RUL predictions using nonlinear models. Results from a simulation study with eight Li-ion battery cells demonstrate that the mechanistic prognostics approach produces more accurate RUL predictions than a traditional capacity-based prognostics approach in 78 of the 80 cases considered (97.5% of the time). Additionally, it is shown that the use of dynamic bounds ensures a low level of uncertainty in the prediction throughout the entire life of a cell.

Renewable energy projections for climate change mitigation: An analysis of uncertainty and errors

Failures of countries to set and achieve renewable energy targets are prevalent, producing uncertainty as to the possibility of renewable energy contributing to a reduction in global emissions. Lack of policy and incorrect modelling analyses are among the sources of these failures and understanding these two sources is crucial for improving confidence in renewables. We assess errors in projections pertaining to the capacity and production of renewable energy in the United States and those countries of the European Union that have strong commitments to green energy supply. Our results show that solar energy has the lowest level of uncertainty as it has the most achievable capacity projections. However, other renewables entail more attractive policies and further research is needed for the advancement of reliable technology and accurate weather predictions. Our findings also provide ranges for the projection uncertainties for six renewable energy technologies, drawing attentions to ways that the dominant errors in these renewable energy projections may be rectified.

Investigations of interpolation errors of angle encoders for high precision angle metrology

Interpolation errors at small angular scales are caused by the subdivision of the angular interval between adjacent grating lines into smaller intervals when radial gratings are used in angle encoders. They are often a major error source in precision angle metrology and better approaches for determining them at low levels of uncertainty are needed. Extensive investigations of interpolation errors of different angle encoders with various interpolators and interpolation schemes were carried out by adapting the shearing method to the calibration of autocollimators with angle encoders. The results of the laboratories with advanced angle metrology capabilities are presented which were acquired by the use of four different high precision angle encoders/interpolators/rotary tables. State of the art uncertainties down to 1 milliarcsec (5 nrad) were achieved for the determination of the interpolation errors using the shearing method which provides simultaneous access to the angle deviations of the autocollimator and of the angle encoder. Compared to the calibration and measurement capabilities (CMC) of the participants for autocollimators, the use of the shearing technique represents a substantial improvement in the uncertainty by a factor of up to 5 in addition to the precise determination of interpolation errors or their residuals (when compensated). A discussion of the results is carried out in conjunction with the equipment used.

Using non-exceedance probabilities of policy-relevant malaria prevalence thresholds to identify areas of low transmission in Somalia

BackgroundCountries planning malaria elimination must adapt from sustaining universal control to targeted intervention and surveillance. Decisions to make this transition require interpretable information, including malaria parasite survey data. As transmission declines, observed parasite prevalence becomes highly heterogeneous with most communities reporting estimates close to zero. Absolute estimates of prevalence become hard to interpret as a measure of transmission intensity and suitable statistical methods are required to handle uncertainty of area-wide predictions that are programmatically relevant.MethodsA spatio-temporal geostatistical binomial model for Plasmodium falciparum prevalence (PfPR) was developed using data from cross-sectional surveys conducted in Somalia in 2005, 2007–2011 and 2014. The fitted model was then used to generate maps of non-exceedance probabilities, i.e. the predictive probability that the region-wide population-weighted average PfPR for children between 2 and 10 years (PfPR2–10) lies below 1 and 5%. A comparison was carried out with the decision-making outcomes from those of standard approaches that ignore uncertainty in prevalence estimates.ResultsBy 2010, most regions in Somalia were at least 70% likely to be below 5% PfPR2–10 and, by 2014, 17 regions were below 5% PfPR2–10 with a probability greater than 90%. Larger uncertainty is observed using a threshold of 1%. By 2011, only two regions were more than 90% likely of being < 1% PfPR2–10 and, by 2014, only three regions showed such low level of uncertainty. The use of non-exceedance probabilities indicated that there was weak evidence to classify 10 out of the 18 regions as < 1% in 2014, when a greater than 90% non-exceedance probability was required.ConclusionUnlike standard approaches, non-exceedance probabilities of spatially modelled PfPR2–10 allow to quantify uncertainty of prevalence estimates in relation to policy relevant intervention thresholds, providing programmatically relevant metrics to make decisions on transitioning from sustained malaria control to strategies that encompass methods of malaria elimination.

Psychological Impact of Learning CDKN2A Variant Status as a Genetic Research Result

Objectives: Little is known about genetic research participants’ responses to receiving individual research results (IRR) from cancer genetic research. We examined the immediate and delayed psychological impact of returning a CDKN2A variant result that is associated with increased risk of pancreatic cancer and melanoma. Methods: One hundred and thirty-three pancreas research registry enrollees whose samples were tested in a research laboratory for the CDKN2A variant were invited by mail to learn the result by telephone and participate in a study about the disclosure process. Self-rated health, quality of life, and emotional responses were surveyed before and 6 months after disclosure. Genetic testing-specific distress, uncertainty, and positive experiences were assessed 6 months after disclosure. Results: Eighty individuals agreed to participate; 63 completed the study. Both carriers and noncarriers showed no change over time in self-rated health, quality of life, or anxiety levels. Carriers reported more sadness than noncarriers before and 6 months after disclosure. Both carriers and noncarriers showed more hopefulness 6 months after than before disclosure. Carriers experienced greater test-specific distress and uncertainty than noncarriers, but levels were low. ­Conclusions: Return of IRR in conjunction with cancer prevention counseling led to low levels of test-specific distress and uncertainty among carriers. No other adverse psychological outcomes were observed.

The ontogenetic allometry of body morphology and chemical composition in dairy goat wethers

We studied the ontogenetic growth of goat wethers (castrated male goats) of the Saanen and Swiss Alpine breeds based on a large range of intraspecific body mass (BM). The body parts and the chemical constituents of the empty body were described by the allometric function by using BM and the empty body mass (EBM) as the predictors for morphological traits and chemical composition, respectively. We fitted the allometric scaling function by applying the SAS NLMIXED procedure, but to evaluate assumptions regarding variances in morphological and compositional traits, we combined the scaling function with homoscedastic (MOD1), and the heteroscedastic exponential (MOD2) and power-of-the-mean (MOD3) variance functions. We also predicted the ontogenetic growth by using the traditional log-log transformation and back-transformed results into the arithmetic scale (MOD4). We obtained predictions from MOD4 in the arithmetic scale by a two-step process, and evaluated MOD1, MOD2 and MOD3 by a model selection framework, and compared MOD4 with MOD1, MOD2 and MOD3 based on goodness-of-fit measures. Based on information criteria for model selection, heterogeneous variance functions were more likely to describe 10 over 36 traits with a low level of model selection uncertainty. One trait was predicted by averaging the MOD1 and MOD2 variance functions; and nine traits were better described by averaging the MOD2 and MOD3 variance functions. The predictions for other 16 traits were averaged from MOD1, MOD2 and MOD3. However, MOD4 better described 11 traits according to the goodness-of-fit measures. Depending on the variable being analyzed, the body parts and the chemical amounts exhibited the three types of allometric behavior with respect to BM and EBM, that is, positive, negative and isometric ontogenetic growth. Reference BMs, that is, 20, 27, 35 and 45 kg, were used to compute the net protein and energy requirements based on the first derivative of the scaling function, and the results were presented in reference to the EBM and EBM0.75. Both the net protein and energy requirements scaled to EBM0.75 increased from 20 to 45 kg of BM.

Effect of creatinine and cystatin C measurement variations on calculated estimated glomerular filtration rate variations and the theoretical impact on kidney disease stage classification: A retrospective study

BackgroundThe estimated glomerular filtration rate (eGFR) is an important parameter in the management of patients with kidney diseases, yet eGFR quality goals are lacking. We examined the uncertainties in eGFR determinations and assessed their impact on patient classifications, aiming to set eGFR quality goals. MethodsAdult patients undergoing creatinine concentration assessments at our hospital, between June 2014 and October 2016, were included (N=285,982). Using 7 eGFR equations, we calculated the imprecisions in the eGFR, based on the imprecisions in determining creatinine and/or cystatin C concentrations. The uncertainties in the eGFR were expressed as functions of creatinine and/or cystatin C concentration uncertainties. Subsequently, the number of ambiguous cases was assessed, based on the calculated uncertainty in the eGFR. ResultsUncertainties in the eGFRs varied according to the eGFR calculation equation used. Although a 0.8% expanded uncertainty in the eGFR caused a 3.5% ambiguous case rate, a 10% expanded uncertainty resulted in a 42.3% rate of ambiguous cases. To meet minimal quality requirements, creatinine imprecision should be ≤3.0%. ConclusionsEven a low level of uncertainty in the eGFR may cause noticeable impacts on patient classifications. Laboratory physicians should be aware, and cautious, of the uncertainties in eGFR calculations.

Application of artificial neural networks to estimate soil organic carbon in a high-organic-matter Mollisol

Soil organic carbon (SOC) has a key role in the global carbon (C) cycle. The complex relationships among the components of C cycle makes difficult the modelling of SOC variation. Artificial neural networks (ANN) are models capable to determine interrelationships based on information. The objective was to develop and evaluate models based on the ANN technique to estimate the SOC in Mollisols of the Southeastern of Buenos Aires Province, Argentina (SEBA). Data from three long term experiments was used. Management and meteorological variables were selected as input. Management information included numerical variables (initial SOC (SOCI); number of years from the beginning of the experiment (Year), proportion of soybean in the crop sequence; (Prop soybean); crop yields (Yield), proportion of cropping in the crop rotation (Prop agri), and categorical variables (Crop, Tillage). In addition, two meteorological inputs (minimum (Tmin) and mean air temperature (Tmed)), were selected. The ANNs were adequate to estimate SOC in the upper 0.20 m of Mollisols of the SEBA. The model with the best performance included six management variables (SOCI, Year, Prop soybean, Tillage, Yield, Prop agri) and one meteorological variable (Tmin), all of them easily available and with low level of uncertainty. Soil organic C changes related to soil use in the SEBA could be satisfactorily estimated using an ANN developed with simple and easily available input variables. Artificial neural network technique appears as a valuable tool to develop robust models to help predicting SOC changes.

The Effect of Assortment Pricing on Choice and Satisfaction: The Moderating Role of Consumer Characteristics

Although consumer research has extensively examined the effect of product assortment on consumption choices, relatively little has been done on assortment pricing. To bridge that gap, we demonstrate that consumers react differently to assortments using parity versus differentiation pricing. Study 1, a field experiment, shows that the impact of assortment pricing on choice satisfaction is contingent on the level of uncertainty preference. For individuals with a low level of preference uncertainty, their choice satisfaction was significantly higher when all the menu items were priced at parity. Conversely, choice satisfaction was higher with varied pricing among people with high levels of preference uncertainty. In Study 2, we examine the moderating role of health consciousness on consumer reactions to parity versus differentiation pricing. The findings of Study 2 indicate that health consciousness influences consumer satisfaction with assortment pricing in a context of restaurant menus. Furthermore, findings from a moderated mediation analysis show that choice confidence is the psychological mechanism that underlies these effects. Taken together, these findings add to the relatively scant literature on assortment pricing.

What makes a manufacturing firm effective for service innovation? The role of intangible capital under strategic and environmental conditions

While an increasing body of research addresses the importance of service innovation to manufacturing firms, little work has examined what makes a manufacturing firm effective for service innovation, and under what strategic and environmental conditions. This article investigates (1) what types of intangible capital are necessary for a manufacturing firm to develop new services, (2) whether a focus on an intangible capital will have the greatest effect on new service success, and (3) whether the effectiveness of a specific intangible capital varies from the combined conditions of strategic flexibility and environmental uncertainty. Building on service-dominant logic, this study theorizes that the four types of intangible capital (market, service delivery, interaction, and learning) are necessary for manufacturing firms to create superior new services. The authors test their hypotheses with a sample of 293 matched responses collected from leading manufacturing firms. The results indicate that all four types of intangible capital have significant positive relationships with new service success. However, not all intangible capital is equally effective as to which learning capital has the strongest effect across four combined conditions of both high and low levels of strategic flexibility and environmental uncertainty. This study contributes to the manufacturing literature by shedding light on a neglected construct (intangible capital), and offering valuable insights for manufacturing managers seeking to determine when (high/low environmental uncertainty) and how (high/low strategic flexibility) firms' intangible capital should be emphasized, in order to achieve the greatest effectiveness of service innovation.

Structural failure investigations through probabilistic nonlinear finite element analysis: Methodology and application

Abstract Structural failure investigations can be strongly influenced by high levels of uncertainty in modelling parameters, particularly in the case of historical constructions. This suggests forensic analysts to perform probabilistic simulations, allowing a risk-informed diagnosis and prognosis of structural failures. In this study, a failure investigation methodology including uncertainty characterisation, modelling and propagation is presented and applied to a historic piperno stone balcony, the collapse of which caused four casualties. High uncertainty in physical and mechanical properties of piperno stone, which has been widely used for a long time in the architectural heritage of Naples and Southern Italy, motivated stochastic finite element (SFE) simulations to account for spatial variability of material properties throughout the balcony. Based on field inspections, laboratory surveys and experimental testing, a three-dimensional finite element (FE) model with four alternative restraint conditions was developed and material properties were statistically characterised. Experimental data were found to be in agreement with those available in the literature for similar piperno stones. Deterministic nonlinear FE simulations with mean material properties showed a major influence of restraint conditions, providing an initial identification of the most realistic model that was able to reproduce the observed damage. Then, SFE simulations were performed on structural models having random fields of material properties. It is shown that the selected SFE model of the balcony had a mean load capacity very close to the total load expected at the time of collapse, allowing the lowest uncertainty level in the output of forensic analysis.

Penalizing on the Basis of the Severity of the Offence: A Sophisticated Revenue-Based Cartel Penalty

In Katsoulacos, Motchenkova and Ulph (2015) we examined the welfare properties of a number of monetary penalty regimes for tackling cartels, including revenue-based penalties (the most widely used regime), illegal gains-based penalties, and overcharge-based penalties. We showed that the latter regime welfare-dominates the others. However it is subject to criticism on the grounds of legal uncertainty and high implementation costs. In this paper we focus on analysing an alternative regime: a sophisticated revenue-based penalty regime in which the penalty base is the revenue of the cartel but the penalty rate depends on (and increases with) the cartel overcharge rate. Thus, in contrast to the currently employed simple revenue-based regime, the proposed regime penalises cartels taking also into account the severity of their offence in terms of the height of their overcharge. We show that this hybrid regime can effectively replicate the desirable welfare properties of an overcharge-based penalty structure while having very low levels of legal uncertainty and implementation costs.