Bank asset allocation and finance structure under uncertainty in Vietnam

PurposeThe paper investigates the link between uncertainty and banks' balance sheet reactions.Design/methodology/approachThe study employs bank-level data in Vietnam during 2007–2019 to measure micro uncertainty in banking through the dispersion of bank-level shocks. Empirical regressions are performed by the two-step system generalized method of moments (GMM) estimator and then verified using the least squares dummy variable corrected (LSDVC) technique.FindingsBanks tend to reduce risky loans, hoard more liquidity and decrease financial leverage in response to higher uncertainty. The relationship between uncertainty and banks' balance sheet reactions is more pronounced for banks that suffer more credit risk and overall risk, thus supporting the precautionary motive of banks. Additionally, uncertainty also leads to a decline in the Net Stable Funding Ratio (NSFR) under Basel III, implying that banks may fail to find a more stable source of funding and be more subject to maturity mismatch during periods of higher uncertainty.Originality/valueThe paper is the first to explore comprehensively the relationship between uncertainty and banks' balance sheet aspects as simultaneously estimated by bank loans, bank liquidity and bank leverage. While many other uncertainty measures display aggregate uncertainty sources, an important contribution in this study is to anatomize uncertainty originating exclusively from banking at a disaggregate level. Besides, shedding light on how uncertainty drives bank funding liquidity as captured by the NSFR under Basel III is entirely novel in the literature.

Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances

This paper reviews the state of the art in applying uncertainty quantification (UQ) methods to additive manufacturing (AM). Physics-based as well as data-driven models are increasingly being developed and refined in order to support process optimization and control objectives in AM, in particular to maximize the quality and minimize the variability of the AM product. However, before using these models for decision-making, a fundamental question that needs to be answered is to what degree the models can be trusted, and consider the various uncertainty sources that affect their prediction. Uncertainty quantification (UQ) in AM is not trivial because of the complex multi-physics, multi-scale phenomena in the AM process. This article reviews the literature on UQ methodologies focusing on model uncertainty, discusses the corresponding activities of calibration, verification and validation, and examines their applications reported in the AM literature. The extension of current UQ methodologies to additive manufacturing needs to address multi-physics, multi-scale interactions, increasing presence of data-driven models, high cost of manufacturing, and complexity of measurements. The activities that need to be undertaken in order to implement verification, calibration, and validation for AM are discussed. Literature on using the results of UQ activities towards AM process optimization and control (thus supporting maximization of quality and minimization of variability) is also reviewed. Future research needs both in terms of UQ and decision-making in AM are outlined.

Intrinsic and environmental factors modulating autonomous robotic search under high uncertainty

Autonomous robotic search problems deal with different levels of uncertainty. When uncertainty is low, deterministic strategies employing available knowledge result in most effective searches. However, there are domains where uncertainty is always high since information about robot location, environment boundaries or precise reference points is unattainable, e.g., in cave, deep ocean, planetary exploration, or upon sensor or communications impairment. Furthermore, latency regarding when search targets move, appear or disappear add to uncertainty sources. Here we study intrinsic and environmental factors that affect low-informed robotic search based on diffusive Brownian, naive ballistic, and superdiffusive strategies (Lévy walks), and in particular, the effectiveness of their random exploration. Representative strategies were evaluated considering both intrinsic (motion drift, energy or memory limitations) and extrinsic factors (obstacles and search boundaries). Our results point towards minimum-knowledge based modulation approaches that can adjust distinct spatial and temporal aspects of random exploration to lead to effective autonomous search under uncertainty.

Considering uncertainties expands the lower tail of maize yield projections

Crop yields are sensitive to extreme weather events. Improving the understanding of the mechanisms and the drivers of the projection uncertainties can help to improve decisions. Previous studies have provided important insights, but often sample only a small subset of potentially important uncertainties. Here we expand on a previous statistical modeling approach by refining the analyses of two uncertainty sources. Specifically, we assess the effects of uncertainties surrounding crop-yield model parameters and climate forcings on projected crop yield. We focus on maize yield projections in the eastern U.S.in this century. We quantify how considering more uncertainties expands the lower tail of yield projections. We characterized the relative importance of each uncertainty source and show that the uncertainty surrounding yield model parameters is the main driver of yield projection uncertainty.

Analysis of measurement uncertainty sources in the casting process

The concepts used in metrology are increasingly part of the productive process of metallurgical companies, considering the globalization of the world market. The interpretation of metrological concepts and their application in decision-making is still a challenge for the industry, especially for the measurement uncertainty calculation. Industries in the casting area are in great need of skilled labor both in relation to the productive process and engineering, as well as in metrology, highlighting the interdisciplinary of the concepts studied in the technical or baccalaureate courses. Therefore, the main objective of this work is to perform a brief literature review of publications made using the value of uncertainty of measurement within the production process of foundries. The results show that there is a potential research in the field, to which it relates the values of measurement uncertainty and fused components.

Estimating the Effect of Climate Internal Variability and Source of Uncertainty in Climate-Hydrological Projections in a Representative Watershed of Northeastern China

The decomposition and quantification of uncertainty sources in ensembles of climate-hydrological simulation chains is a key issue in climate impact researches. The mainly objectives of this study partitioning climate internal variability (CIV) and uncertainty sources in the climate-hydrological projections simulation process, the climate simulation process formed by six downscaled GCMs under two emission scenarios called GCMs-ES simulation chain, the hydrological simulation process add one calibrate Soil and Water Assessment Tool (SWAT) model called GCMs-ES-HM simulation chain. The CIV and external forcing of climate projections are investigated in each GCMs-ES simulation chain. The CIV of precipitation and ET are large in rainy season, and the single-to-noise ratio (SNR) are also relatively high in rainy season. Furthermore, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The CIV and GCMs are the dominate contributors of runoff in rainy season. It worth noting the CIV can propagate from precipitation and ET to runoff projections. In additional, the hydrological model parameters are the third uncertainty contributor of runoff, which embody the hydrological model simulate process play important role in hydrological projections in future. The findings of this study advised that the uncertainty is complex in hydrological, hence, it is meaning and necessary to estimate the uncertainty in climate simulation process, the uncertainty analysis results can provide effectively efforts to reduce uncertainty and then give some positive suggestions to stakeholders for adaption countermeasure under climate change.

Accuracy in starphotometry

Starphotometry, the night-time counterpart of sunphotometry, has not yet achieved the commonly sought observational error level of 1 %: a spectral optical depth (OD) error level of 0.01. In order to address this issue, we investigate a large variety of systematic (absolute) uncertainty sources. The bright-star catalogue of extraterrestrial references is noted as a major source of errors with an attendant recommendation that its accuracy, particularly its spectral photometric variability, be significantly improved. The small field of view (FOV) employed in starphotometry ensures that it, unlike sun- or moonphotometry, is only weakly dependent on the intrinsic and artificial OD reduction induced by scattering into the FOV by optically thin clouds. A FOV of 45 arcsec (arcseconds) was found to be the best trade-off for minimizing such forward-scattering errors concurrently with flux loss through vignetting. The importance of monitoring the sky background and using interpolation techniques to avoid spikes and to compensate for measurement delay was underscored. A set of 20 channels was identified to mitigate contamination errors associated with stellar and terrestrial atmospheric gas absorptions, as well as aurora and airglow emissions. We also note that observations made with starphotometers similar to our High Arctic instrument should be made at high angular elevations (i.e. at air masses less than 5). We noted the significant effects of snow crystal deposition on the starphotometer optics, how pseudo OD increases associated with this type of contamination could be detected, and how proactive techniques could be employed to avoid their occurrence in the first place. If all of these recommendations are followed, one may aspire to achieve component errors that are well below 0.01: in the process, one may attain a total 0.01 OD target error.

Uncertainty Evaluation of the Electrical Transient Rise Time

This paper deals with the uncertainty evaluation of the rise time of one pulse of an electrical fast transient burst (EFT/B), as carried out by the National Institute of Metrology, Quality and Technology (Inmetro) of Brazil. The main goal is to analyse the impact of two uncertainty sources not included in the uncertainty budget example in IEC 61000-4-4 standard, and not very well explored in the literature: Voltage Measurement and Horizontal Accuracy. The uncertainty sources considered in this evaluation were: the oscilloscope’s resolution and calibration of the voltage scale (Voltage Measurement) and the time scale (Horizontal Accuracy); the bandwidth of the measurement system; the oscilloscope sampling rate; and the repeatability of ten different measurements. Two sets of measurements were taken from two different oscilloscope settings: in the first one, the interpolation function was enabled, and in the other one was disabled. In both cases two components stood out for their huge relative contribution: time reading and repeatability. Considered together, these two components added up approximately 87% of the expanded uncertainty for interpolated samples, and 95% for non-interpolated samples. Also, the results indicate that the oscilloscope interpolation function (OIF), if available, should be used, as the expanded uncertainty decreases by 27% due to a better oscilloscope resolution. A discussion of the uncertainty budget example in Annex C of IEC 61000-4-4 is presented. In conclusion, once the OIF is enabled, the two additional uncertainty components discussed in this paper should be considered in the uncertainty budget. They should not be neglected since the combined relative contribution of these components is larger than the relative contribution of repeatability.