Measurement Uncertainty Research Articles

Optimal control technique applied to the minimization of uncertainty measurements in surveying instruments

The objective of this study was to develop an optimal control approach by numerical calculus to predict how to reduce the overall uncertainty of survey instruments unable to directly measure inaccessible points. To reach our goal, two approaches were used to attain the objective. The first was inspired by mathematical models related to three methods appropriately selected and contained in Zhuo’s work proposed in 2012. These were Remote Elevation Measurement (REM), Remote Elevation Dual Measurement (REDM), and Front-to-Back Measurement (FBM) methods whose uncertainties on the measurements of points were deduced using error propagation equations. Optimal control technique helps us to show that for the REM, the height h of the prism contributed more than 70% compared to the global uncertainty for ranges [Formula: see text] from the prism. For the REDM, when the distance between two consecutive stations increases, the weight of the contribution of the two zenith angles [Formula: see text] and [Formula: see text] tends to 50% each for [Formula: see text] close to [Formula: see text], which is to be avoided. For the FBM, the weight of the contribution during the front measurement process before is negligible. The second approach used the Swedish regulation of SIS-TS 21143:2009 which classified total stations according to types of uncertainty to compare the results given by the total station of class T3 unable to directly measure inaccessible points with the more sophisticated class T1 station with direct measurements. Thus, for small spans at the rear measurements [Formula: see text], the height [Formula: see text] of the front prism has the greatest relative contribution more than 90% for zenithal differences [Formula: see text]. This results of our analysis were convincing and provided designers with the data to minimize the overall uncertainties essential in the conception of total stations.

Validation and measurement uncertainty of analytical methods for various azo dye adulterants in Curcuma longa L.

Food adulteration involving the illegal addition of dyes to foodstuffs has become an alarming issue in recent years. This study developed and validated a high-performance liquid chromatography (HPLC)-DAD (diode array detector) method for the simultaneous determination of nine azo dyes (Butter Yellow, Sudan Orange G, Para Red, Sudan I, Sudan II, Sudan III, Sudan IV, Sudan Red 7B, and Scarlet 808). Moreover, a qualitative analysis method using liquid chromatography-tandem mass spectrometry was developed to more accurately identify peaks detected in HPLC-DAD. The calibration curve represented good linearity (r2≥0.9998) over the measured concentration range of 0.5-25mg/kg. limit of detection and limit of quantification were 0.01-0.04 and 0.04-0.12mg/kg, respectively. Accuracy and precision were 96.0-102.6 and 0.16-2.01 (relative standard deviation%), respectively. Additionally, the measurement uncertainty and HorRat value were estimated. Several Curcuma longa L. distributed in Korea were collected and monitored for azo dye contaminants. PRACTICAL APPLICATION: The proposed HPLC-DAD method represents a significant advancement in the field, offering a reliable means of quantifying azo dyes and identifying their presence even at trace levels in adulterated turmeric. This not only contributes to ensuring the safety and integrity of turmeric products but also establishes precedent for robust analytical techniques in addressing food safety challenges.

Approaching the emissivity of CMAS at high temperatures by convenient thermocouple-based infrared thermography

It remains a grand challenge to probe the emissivity of calcium-magnesium-alumina-silicates (CMAS) at high temperatures. Herein, an innovative thermocouple-based infrared thermography (TBIT) method that combines a double hot thermocouple system and infrared (IR) camera is proposed to enable convenient measurement for CMAS emissivity at temperature >800 °C. To improve the measurement accuracy, a heat transfer model accounting for the possible uncertainty in temperature measurement is also developed to correct the real sample temperature on the hot thermocouple. On this basis, the feasibility of the TBIT method is verified by using the platinum-rhodium alloy as the benchmark. As a proof-of-principle demonstration, four different sets of CMAS samples, i.e., blast furnace (BF) slag are considered with their temperature-dependent emissivities characterized experimentally. It is found that the BF-like CMAS features an emissivity of 0.5–0.9 at 800–1500 °C. This research provides a fast, accurate and low-cost experiment effort to probe CMAS emissivity at high temperatures, which enriches the thermophysical properties of the CMAS materials.

Interference with Signaling Track Circuits Caused by Rolling Stock: Uncertainty and Variability on a Test Case

The demonstration of compliance of rolling stock against disturbance limits for railway signaling, and in particular track circuits, is subject to a large deal of variability, caused by the diverse values of the electrical parameters of the railway line and resulting transfer functions, as well as the operating conditions of the rolling stock during tests. Instrumental uncertainty is evaluated with a type B approach and shown to be much less than the experimental variability. Repeated test runs in acceleration, coasting, cruising, and braking conditions are considered, deriving both max-hold (spread) and sample (or experimental) standard deviation curves compared to the respective mean values (type A approach to the evaluation of uncertainty, as defined in of the Guide to the Uncertainty in Measurement. The major source of variability affecting a significant portion of the spectrum is caused by the superposed oscillations of the onboard LC filter, for which different choices of the transformation window duration are discussed. The test runs and the acquired data covered, overall, 1 day of tests along about 300 km of the Italian 3 kV DC railway network.

Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach.

An accurate understanding of uncertainty is needed to properly interpret methane emission estimates from upstream oil and gas sources in a variety of contexts, from component-level measurements to yearly jurisdiction-wide inventories. To characterize measurement uncertainty, we examine controlled release (CR) data from five different technology providers including quantitative gas imaging (QOGI), tunable diode laser-absorption spectroscopy (TDLAS); and airborne near-infrared hyperspectral (NIR HS) imaging. We introduce a novel empirical method to develop probability distributions of measurements given a true emission rate using the CR data. The approach includes flexible likelihoods which capture complex relationships in the data. An algorithm which provides the distribution of the true emission rate given a measurement is also developed, which synthesizes the measurement with the CR data and external information about the possible true emission rate. The results show that flexible models that accommodate complex nonlinear behavior are needed to adequately model measurement error. We also show that measurement error can vary under different conditions. We demonstrate that measurement uncertainty can be reduced by performing repeated measurements. A limitation of the study is that the collected CR data is collected under controlled conditions that may differ from those in industrial settings. As new CR data become available, the models presented in this paper can be refit to consider more diverse scenarios. The methodology can be extended to explicitly model different conditions to improve performance.

A Morphological Approach to Development of a Process for Measurement Uncertainty Estimation

The problem of increasing the reliability of uncertainty estimation of measurement results is considered. The purpose of this work was to justify the application of the process approach to the formation of the algorithm of uncertainty estimation on the basis of morphological analysis. It is theoretically substantiated that from the standpoint of the system approach to achieving an acceptable degree of reliability of measurement uncertainty estimates it is necessary to implement a process approach to the formation of the estimation method as an algorithm of actions. The main stages of the estimation process are defined. It is established that each stage of the estimation process can be realized by alternative methods. The morphological box method as a realization of morphological analysis is proposed as a basis for its solution. A morphological box design of the uncertainty estimation process with an open architecture is presented, based only on commonly accepted methods and approaches for realizing each step of the process. Two aspects of the application of the morphological box method are identified. On the one hand, the morphological box allows to form an algorithm of the uncertainty assessment process, maximally acceptable for the laboratory conditions, as a combination of process steps, based on the task at hand, combining different variants of realization of these steps. On the other hand, the morphological box acts as a tool for development of new methods of realization of various stages of the uncertainty assessment process. Examples of using the morphological box method to develop alternative algorithms of the uncertainty estimation process of the same measurement method and to develop new methods of realization of different stages of the estimation process are considered.

On exploiting nonparametric kernel-based probabilistic machine learning over the large compositional space of high entropy alloys for optimal nanoscale ballistics

The large compositional space of high entropy alloys (HEA) often presents significant challenges in comprehensively deducing the critical influence of atomic composition on their mechanical responses. We propose an efficient nonparametric kernel-based probabilistic computational mapping to obtain the optimal composition of HEAs under ballistic conditions by exploiting the emerging capabilities of machine learning (ML) coupled with molecular-level simulations. Compared to conventional ML models, the present Gaussian approach is a Bayesian paradigm that can have several advantages, including small training datasets concerning computationally intensive simulations and the ability to provide uncertainty measurements of molecular dynamics simulations therein. The data-driven analysis reveals that a lower concentration of Ni with a higher concentration of Al leads to higher dissipation of kinetic energy and lower residual velocity, but with higher penetration depth of the projectile. To deal with such conflicting computationally intensive functional objectives, the ML-based simulation framework is further extended in conjunction with multi-objective genetic algorithm for identifying the critical elemental compositions to enhance kinetic energy dissipation with minimal penetration depth and residual velocity of the projectile simultaneously. The computational framework proposed here is generic in nature, and it can be extended to other HEAs with a range of non-aligned multi-physical property demands.

Uncertainty quantification via localized gradients for deep learning-based medical image assessments

Objective. Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tasks, measures that indicate the reliability of these models are less established. Increasingly, uncertainty quantification (UQ) methods are being introduced to inform users on the reliability of model outputs. However, most existing methods cannot be augmented to previously validated models because they are not post hoc, and they change a model’s output. In this work, we overcome these limitations by introducing a novel post hoc UQ method, termed Local Gradients UQ, and demonstrate its utility for deep learning-based metastatic disease delineation. Approach. This method leverages a trained model’s localized gradient space to assess sensitivities to trained model parameters. We compared the Local Gradients UQ method to non-gradient measures defined using model probability outputs. The performance of each uncertainty measure was assessed in four clinically relevant experiments: (1) response to artificially degraded image quality, (2) comparison between matched high- and low-quality clinical images, (3) false positive (FP) filtering, and (4) correspondence with physician-rated disease likelihood. Main results. (1) Response to artificially degraded image quality was enhanced by the Local Gradients UQ method, where the median percent difference between matching lesions in non-degraded and most degraded images was consistently higher for the Local Gradients uncertainty measure than the non-gradient uncertainty measures (e.g. 62.35% vs. 2.16% for additive Gaussian noise). (2) The Local Gradients UQ measure responded better to high- and low-quality clinical images (p < 0.05 vs p > 0.1 for both non-gradient uncertainty measures). (3) FP filtering performance was enhanced by the Local Gradients UQ method when compared to the non-gradient methods, increasing the area under the receiver operating characteristic curve (ROC AUC) by 20.1% and decreasing the false positive rate by 26%. (4) The Local Gradients UQ method also showed more favorable correspondence with physician-rated likelihood for malignant lesions by increasing ROC AUC for correspondence with physician-rated disease likelihood by 16.2%. Significance. In summary, this work introduces and validates a novel gradient-based UQ method for deep learning-based medical image assessments to enhance user trust when using deployed clinical models.

Evaluation of the measurement accuracy and uncertainty of a solid-state detector under diagnostic x-ray beam conditions.

An accurate measurement of x-ray beams is expected to reduce the uncertainties associated with estimating radiation risk to patients in clinical settings. To perform assessment tasks based on the readings of a solid-state detector (SSD) using semiconductor technology, the characteristics of the detector should be elucidated. In this study, we evaluated the measurement accuracy of a new SSD under diagnostic x-ray beam conditions in terms of air kerma, tube voltage, and half-value layer (HVL). The performance of the SSD was then compared with those of reference instruments. The tube voltage was varied within the range of 50-120kV in steps of 10kV and the thickness and materials of additional filters were concurrently changed (several combinations were tested). In addition, the dose rate and energy dependence of the SSD were also investigated. These effects were analyzed based on statistical significance tests. Furthermore, the expanded uncertainties in the series of measurements were meticulously calculated. The results showed average relative differences of -3.26±1.33%, 0.44±1.01%, and -2.60±3.31% for air kerma, tube voltage, and HVL, respectively. Furthermore, air kerma did not exhibit any dependence on dose rate and energy, in contrast to tube voltage and HVL measurements. The measurement values of the SSD fall within the acceptable range of uncertainty, highlighting its measurement accuracy and reliability. Furthermore, based on the characteristics elucidated by this study, valuable insights are provided concerning the assurance of appropriate measurement values in clinical settings.

New chloromethane preparation line for the stable chlorine isotope composition analysis*

ABSTRACT Stable isotopes of chlorine have great application potential due to the widespread occurrence of the chlorine anion in water, minerals, living organisms and the environment. In most studies, the chloride ion from the samples is converted to chloromethane, which is then analysed isotopically using a mass spectrometer. In the present study, a new design for a chloromethane preparation line is proposed. In particular, the new chloromethane preparation line uses a new system for injecting iodomethane into the preparation system, as well as ampoules with Teflon valves and the u-shaped freezers. These improvements reduced the preparation time to about 1 h, and also achieved a decent measurement uncertainty of 0.05 permil.

Validation of Acuros for total body irradiation at extended distance.

Standardized and accurately reported doses are essential in conventional total body irradiation (TBI), especially lung doses. This study evaluates the accuracy of the Acuros algorithm in predicting doses for extended-distance TBI. Measurements and calculations were done with both 6 and 18 MV. Tissue Maximum Ratio (TMR), output and off axis ratios (OAR) were measured at 200 and 500cm source to detector distance and compared to Acuros calculated values. Two end-to-end tests were carried out, one with an in-house phantom (solid water and Styrofoam) with inserted ion chambers and the other was with the Imaging and Radiation Oncology Core (IROC) TBI anthropomorphic phantom equipped with TLDs. The end-to-end test was done for 6 and 18 MV both with and without lung blocks. The source to midplane distance for both phantoms were at 518 and 508cm respectively. Lung blocks were placed at the phantom surface and a beam spoiler was positioned 30cm from the surface of the phantoms as per our clinical set up. The agreement between measured and calculated TMR, output and off axis ratios for both 6 and 18 MV were within 2%. Ion chamber measurements in both the Styrofoam and solid water for both energies carried out with and without lung blocks were within 2% of calculated values. TLD measured doses for both 6 and 18 MV in the IROC phantom were within 5% of calculated doses which is within the uncertainty of the TLD measurement. The results indicate that the clinical beam model for Acuros 16.1 commissioned at standard clinical distances is capable of calculating doses accurately at extended distances up to 500cm.

National culture and corporate investment: does uncertainty matter?

PurposeOur purpose is to explore how culture’s impact on investment is depending on uncertainty levels.Design/methodology/approachThis study investigates the interplay between national culture, uncertainty, and corporate investment decisions. Focusing on the uncertainty avoidance dimension (HUA) from Hofstede’s culture framework and utilizing the World Uncertainty Index (WUI) as a measure of uncertainty, this research explores how culture’s impact on investment is depending on uncertainty levels.FindingsOur results reveal that high HUA countries lower long-term investment during periods of heightened uncertainty, particularly in riskier investments like R&D, rather than capital expenditure. This relation is more pronounced for smaller firms. The findings suggest that HUA is associated with less risk taking, primarily when uncertainty is high. Furthermore, we demonstrate that the interaction between HUA and uncertainty exerts more significant and consistent effects on corporate investment than other cultural dimensions, religion, and various formal institutions, contrary to prevailing literature.Originality/valueThis study looks at the relationship between national culture and corporate investment under ambiguity, and what are the implications for risk taking. If national culture is related to riskier investments, such as R&D, relative to safer investments, like capital expenditure this would imply that risk taking is explaining the relationship between national culture and corporate investment. This relation should be clear during uncertain times. This is the first study to include the moderating effects of the level of uncertainty on the relation between national culture and corporate investment (or financial decisions in general).

HYPSTAR: a hyperspectral pointable system for terrestrial and aquatic radiometry

Optical Earth observation satellites provide vast amounts of data on a daily basis. The top-of-atmosphere radiance measured by these satellites is usually converted to bottom-of-atmosphere radiance or reflectance which is then used for deriving numerous higher level products used for monitoring environmental conditions, climate change, stock of natural resources, etc. The increase of available remote sensing data impacts decision-making on both regional and global scales, and demands appropriate quality control and validation procedures. A HYperspectral Pointable System for Terrestrial and Aquatic Radiometry (HYPSTAR®) has been designed to provide automated, in-situ multiangular reflectance measurements of land and water targets. HYPSTAR-SR covers 380–1020 nm spectral range at 3 nm spectral resolution and is used at water sites. For land sites the HYPSTAR-XR variant is used with the spectral range extended to 1680 nm at 10 nm spectral resolution. The spectroradiometer has multiplexed radiance and irradiance entrances, an internal mechanical shutter, and an integrated imaging camera for capturing snapshots of the targets. The spectroradiometer is mounted on a two-axis pointing system with 360° range of free movement in both axes. The system also incorporates a stable light emitting diode as a light source, used for monitoring the stability of the radiometric calibration during the long-term unattended field deployment. Autonomous operation is managed by a host system which handles data acquisition, storage, and transmission to a central WATERHYPERNET or LANDHYPERNET server according to a pre-programmed schedule. The system is remotely accessible over the internet for configuration changes and software updates. The HYPSTAR systems have been deployed at 10 water and 11 land sites for different periods ranging from a few days to a few years. The data are automatically processed at the central servers by the HYPERNETS processor and the derived radiance, irradiance, and reflectance products with associated measurement uncertainties are distributed at the WATERHYPERNET and LANDHYPERNET data portals.

Determination of β‐Hydroxy‐β‐Methylbutyrate Content in Food Raw Materials by High‐Performance Liquid Chromatography

ABSTRACTAn analytical method based on high‐performance liquid chromatography (HPLC) method was developed for the determination of β‐hydroxy‐β‐methylbutyrate (HMB) content in food raw materials. The quantitative detection was performed by HPLC on diode array detector mode under external standard method. The limits of quantification, calculated as 10 times the standard deviation, were 2.0 g/100 g. The average recoveries were in the range of 92.3%–101%, with relative standard deviations below 3.87%, measured at three concentration levels. This method was used to determine the purity of HMB in 10 commercially available food raw materials. The real sample analysis results showed that the content and measurement uncertainties of HMB were 77.1–81.8 g/100 g and 4.51–4.68 g/100 g respectively, which met the requirement of HMB content in the range of 77–82 g/100 g in the HMB announcement. There are no studies on the detection of HMB in food raw materials in the previous reports. The established method provided reference for the rapid quantification and purity analysis of β‐hydroxy‐β‐methylbutyrate in food raw materials.

Accurate thickness evaluation of thermal barrier coatings using microwave resonator sensor

Thermal barrier coatings (TBC) are frequently employed in aircraft engines and turbine blades for protection against high temperatures. Constant exposure to in-service stresses, however, leads to a variety of anomalies in TBC that could prove to be potentially catastrophic for the assemblies they protect. This raises the need for inspection using non-destructive techniques (NDT) to ensure the reliability and structural integrity of the coated assemblies. Thickness evaluation of the topcoat of the TBC is paramount to determining structural integrity of the TBC. Microwaves are particularly suited for such applications since they readily penetrate low loss TBC. However, the performance of previous microwave-based TBC thickness evaluation techniques was limited in accuracy to 15 μm. In this paper, accurate thickness evaluation of thermal barrier coating (TBC) is performed using a microwave resonator sensor operating at a relatively low frequency (<1 GHz). TBC thicknesses as low as 100 μm are evaluated based on shift in resonant frequency of the probe. Overall, practically relevant thicknesses ranging from 100 μm to 600 μm are evaluated herein and multiple trials are performed to ascertain repeatability and assess uncertainty in measurements. For a 250 μm thick TBC sheet, a mean error of 0.9 μm was accomplished which translates to 0.36 % error with a standard deviation of 1.5 μm. Compared to other microwave sensors reported in the literature, a better estimation accuracy is demonstrated in this work, in particular for thicknesses less than <250 μm.

Estimating uncertainty when providing individual cardiovascular risk predictions: a Bayesian survival analysis

BackgroundCardiovascular disease (CVD) risk scores provide point estimates of individual risk without uncertainty quantification. The objective of the current study was to demonstrate the feasibility and clinical utility of calculating uncertainty surrounding individual CVD-risk predictions using Bayesian methods. Study Design and SettingIndividuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort—Secondary Manifestations of ARTerial disease (UCC-SMART). In 8,355 individuals, followed for median of 8.2 years (IQR 4.2–12.5), a Bayesian Weibull model was derived to predict the 10-year risk of recurrent CVD events. ResultsModel coefficients and individual predictions from the Bayesian model were very similar to that of a traditional (‘frequentist’) model but the Bayesian model also predicted 95% credible intervals (CIs) surrounding individual risk estimates. The median width of the individual 95%CrI was 5.3% (IQR 3.6–6.5) and 17% of the population had a 95%CrI width of 10% or greater. The uncertainty decreased with increasing sample size used for derivation of the model. Combining the Bayesian Weibull model with sampled hazard ratios based on trial reports may be used to estimate individual estimates of absolute risk reduction with uncertainty measures and the probability that a treatment option will result in a clinically relevant risk reduction. ConclusionEstimating uncertainty surrounding individual CVD risk predictions using Bayesian methods is feasible. The uncertainty regarding individual risk predictions could have several applications in clinical practice, like the comparison of different treatment options or by calculating the probability of the individual risk being below a certain treatment threshold. However, as the individual uncertainty measures only reflect sampling error and no biases in risk prediction, physicians should be familiar with the interpretation before widespread clinical adaption.

Deformation-induced CPT violation in entangled pairs of neutral kaons

In this paper we consider description of kaon-antikaon interference in the context of a theory with deformed CPT symmetry. In the case of such theoretical models, deviations from the standard CPT invariance is related to the momentum carried by the particles; in particular the rest masses of particles and antiparticles are equal. We find that the decay intensity of kaon-antikaon pair has three contributing terms: the correct-parity, the wrong-parity, and the interference between them, all of which are affected by deformation. Using the fact that the presence of such terms was not observed we estimate, under plausible assumptions, the magnitude of deformation parameter κ≳1017 GeV at the LHC energy. This limit can be possibly improved to values closer to the expected Planck mass scale κ≳1018 GeV using a next-generation collider with energy larger by an order of magnitude. This raises hopes that the effect, if it exists, could be detected in future accelerator experiments. Uncertainties of the energy measurement and time resolution are important for accuracy of these predictions. Published by the American Physical Society 2024

Effective Noise Reduction Filters for Precise Temperature Measurement Using Brillouin Distributed Optical Fiber Sensors

This study explores the effectiveness of various noise reduction filters in accurately measuring temperature distributions over a 38.2 km single-mode fiber (SMF). Brillouin optical time-domain analysis (BOTDA) sensor is utilized to gather Brillouin gain spectra (BGSs), which are denoised using bilateral filter (BF), guided filter (GF), adaptive Wiener filter (AWF), non-local means filter (NLMF), average filter (AF) and disc filter (DF). The temperature distributions over the SMF are then determined by applying least-squares curve fitting (LSCF). The study assesses the efficacy of noise reduction filters considering signal-to-noise ratio (SNR), uncertainty in temperature measurement (UTM), experimental spatial resolution (ESR) and signal processing speed (SPS). Among six different filters, NLMF outperforms other filters which can provide SNR improvement of 10.22 dB for which the UTM can be improved by 58.93% without deteriorating the ESR of the sensor. The noise reduction using such filter can also provide 6.2% faster SPS. Therefore, NLMF can be considered as an effective noise reduction filter for the precise temperature measurement using BOTDA sensors. DUJASE Vol. 8 (1) 42-50, 2023 (January)

Probing the quantum nature of gravity using a Bose-Einstein condensate

The effect of noise induced by gravitons has been investigated using a Bose-Einstein condensate. The general complex scalar field theory with a quadratic self-interaction term has been considered in the presence of a gravitational wave. The gravitational wave perturbation is then considered as a sum of discrete Fourier modes in the momentum space. Varying the action and making use of the principle of least action, one obtains two equations of motion corresponding to the gravitational perturbation and the time-dependent part of the pseudo-Goldstone boson. Coming to an operatorial representation and quantizing the phase space variables via appropriately introduced canonical commutation relations between the canonically conjugate variables corresponding to the graviton and bosonic part of the total system, one obtains a proper quantum gravity setup. Then we obtain the Bogoliubov coefficients from the solution of the time-dependent part of the pseudo-Goldstone boson and construct the covariance metric for the bosons initially being in a squeezed state. The entries of the covariance matrix now involves a stochastic contribution which results in an operatorial stochastic structure of the quantum Fisher information. Using the stochastic average of the Fisher information, we obtain a lower bound on the amplitude parameter of the gravitational wave. As the entire calculation is done at zero temperature, the bosonic system, by construction, will behave as a Bose-Einstein condensate. For a Bose-Einstein condensate with a single mode, we observe that the lower bound of the expectation value of the square of the uncertainty in the amplitude measurement does not become infinite when the total observational term approaches zero. It always has a finite value if the gravitons are initially in a squeezed state with high enough squeezing. In order to sum over all possible momentum modes, we next consider a noise term with a suitable Gaussian weight factor which decays over time. We then obtain the lower bound on the final expectation value of the square of the variance in the amplitude parameter. Because of the noise induced by the graviton, there is a minimum value of the measurement time below which it is impossible to detect any gravitational wave using a Bose-Einstein condensate. Finally, we consider interaction between the phonon modes of the Bose-Einstein condensate which results in a decoherence. We observe that the decoherence effect becomes significant for gravitons with minimal squeezing. Published by the American Physical Society 2024

The role of uncertainty and sentiment for intraday volatility connectedness between oil and financial markets

We quantify intraday volatility connectedness between oil and key financial assets and assess how it is related to uncertainty and sentiment measures. For that purpose, we integrate the well-known spillover methodology with a TVP VAR model estimated on a unique, vast dataset of roughly 300 thousand 5 min quotations for most heavily traded financial assets: crude oil, the US dollar, S&P 500 index, gold and US treasury bonds. This distinguishes our investigation from previous studies, which usually employ relatively short samples of daily or weekly data and focus on connectedness between two asset classes. We contribute to the literature across three margins. First, we document that market connectedness at intraday frequency presents a different picture on markets co-movement compared to the estimates obtained using daily data. Second, we show that at 5 min frequency volatility is mostly transmitted from the stock market and absorbed by the bond and dollar markets, with oil and gold markets being occasionally important for volatility transmission. Third, we present evidence that daily averages of intraday connectedness measures respond to changes in sentiment and market-specific uncertainty. Interestingly, our results contrast with earlier findings, as they show that connectedness among markets decreases in periods of high volatility owing to market-specific factors. Our study points to the importance of using high-frequency data in order to better understand financial and commodity markets dynamics.