Error Contributions Research Articles

Simulation of analysis error contributions arising from uncertainties in the parametrisation of deep convection

AbstractIn order to simulate analysis error contributions resulting from observation errors and model errors, a possible methodology is to run a data assimilation ensemble that includes observation perturbations and a multiphysics approach. This method is examined here formally and experimentally, in order to investigate the effects of deep convection parametrisation uncertainties on analysis errors. A linearised expansion of initial condition errors is first employed to decompose them into contributions arising from observation errors and model errors. This is compared formally with a similar expansion for analysis differences resulting from the use of two different model versions, as in a multiphysics approach. Experimental results with the Action de Recherche Petite Echelle Grande Echelle global model are then examined. Observation error contributions are simulated by analysis differences between an unperturbed experiment and another experiment that includes observation perturbations that are drawn from specified observation error covariances. This is compared with analysis differences resulting from the use of two different parametrisation schemes of deep convection. The results indicate that the contribution of convection uncertainties is relatively large in the Tropics, with similar amplitudes to the contribution of observation errors. Simulated model errors are also found to be pronounced in the boundary layer and in convective areas where the analysis constraint by observations is relatively small, due to lower observation density.

Temporal spectrum of multiconjugate adaptive optics residuals and impact of tip-tilt anisoplanatism on astrometric observations

ABSTRACT Multiconjugate adaptive optics (MCAO) will assist a new era of ground-based astronomical observations with the extremely large telescopes and the Very Large Telescope. High-precision relative astrometry is among the main science drivers of these systems and challenging requirements have been set for the astrometric measurements. A clear understanding of the astrometric error budget is needed and the impact of the MCAO correction has to be taken into account. In this context, we propose an analytical formulation to estimate the residual phase produced by an MCAO correction in any direction of the scientific field of view. The residual phase, computed in the temporal frequency domain, allows to consider the temporal filtering of the turbulent phase from the MCAO loop and to extract the temporal spectrum of the residuals, as well as to include other temporal effects such as the scientific integration time. The formulation is kept general and allows to consider specific frameworks by setting the telescope diameter, the turbulence profile, the guide stars constellation, the deformable mirrors configuration, the modes sensed and corrected, and the tomographic reconstruction algorithm. The formalism is presented for both a closed loop and a pseudo-open loop control. We use our results to investigate the effect of tip-tilt residuals on MCAO-assisted astrometric observations. We derive an expression for the differential tilt jitter power spectrum that also includes the dependence on the scientific exposure time. Finally, we investigate the contribution of the differential tilt jitter error on the future astrometric observations with MAVIS and MAORY.

Effects of meteorological and land surface modeling uncertainty on errors in winegrape ET calculated with SIMS

Characterization of model errors is important when applying satellite-driven evapotranspiration (ET) models to water resource management problems. This study examines how uncertainty in meteorological forcing data and land surface modeling propagate through to errors in final ET data calculated using the Satellite Irrigation Management Support (SIMS) model, a computationally efficient ET model driven with satellite surface reflectance values. The model is applied to three instrumented winegrape vineyards over the 2017–2020 time period and the spatial and temporal variation in errors are analyzed. We illustrate how meteorological data inputs can introduce biases that vary in space and at seasonal timescales, but that can persist from year to year. We also observe that errors in SIMS estimates of land surface conductance can have a particularly strong dependence on time of year. Overall, meteorological inputs introduced RMSE of 0.33–0.65 mm/day (7–27%) across sites, while SIMS introduced RMSE of 0.55–0.83 mm/day (19–24%). The relative error contribution from meteorological inputs versus SIMS varied across sites; errors from SIMS were larger at one site, errors from meteorological inputs were larger at a second site, and the error contributions were of equal magnitude at the third site. The similar magnitude of error contributions is significant given that many satellite-driven ET models differ in their approaches to estimating land surface conductance, but often rely on similar or identical meteorological forcing data. The finding is particularly notable given that SIMS makes assumptions about the land surface (no soil evaporation or plant water stress) that do not always hold in practice. The results of this study show that improving SIMS by eliminating these assumptions would result in meteorological inputs dominating the error budget of the model on the whole. This finding underscores the need for further work on characterizing spatial uncertainty in the meteorological forcing of ET.

The Most Probable Curve method - A robust approach to estimate kinetic models from low plate count data resulting in reduced uncertainty

A novel method is proposed for fitting microbial inactivation models to data on liquid media: the Most Probable Curve (MPC) method. It is a multilevel model that makes a separation between the “true” microbial concentration according to the model, the “actual” concentration in the media considering chance, and the actual counts on the plate. It is based on the assumptions that stress resistance is homogeneous within a microbial population, and that there is no aggregation of microbial cells. Under these assumptions, the number of colonies in/on a plate follows a Poisson distribution with expected value depending on the proposed kinetic model, the number of dilutions and the plated volume.The novel method is compared against (non)linear regression based on a normal likelihood distribution (traditional method), Poisson regression and gamma-Poisson regression using data on the inactivation of Listeria monocytogenes. The conclusion is that the traditional method has limitations when the data includes plates with low (or zero) cell counts, which can be mitigated using more complex (discrete) likelihoods. However, Poisson regression uses an unrealistic likelihood function, making it unsuitable for survivor curves with several log-reductions. Gamma-Poisson regression uses a more realistic likelihood function, even though it is based mostly on empirical hypotheses. We conclude that the MPC method can be used reliably, especially when the data includes plates with low or zero counts. Furthermore, it generates a more realistic description of uncertainty, integrating the contribution of the plating error and reducing the uncertainty of the primary model parameters. Consequently, although it increases modelling complexity, the MPC method can be of great interest in predictive microbiology, especially in studies focused on variability analysis.

On the use of ensemble averaging techniques to accelerate the Uncertainty Quantification of CFD predictions in wind engineering

In this work we focus on reducing the wall clock time required to compute statistical estimators of highly chaotic incompressible flows on high performance computing systems. Our approach consists of replacing a single long-term simulation by an ensemble of multiple independent realizations, which are run in parallel. A failure probability convergence criteria must be satisfied by the statistical estimator of interest to assess convergence. The error analysis leads to the identification of two error contributions: the initialization bias and the statistical error. We propose an approach to systematically detect the burn-in time needed in order to minimize the initialization bias, as well as techniques to choose the effective time needed to keep the statistical error under control. This is accompanied by strategies to reduce simulation cost. The proposed method is assessed in application to the prediction of the drag force over high rise buildings and specifically in application to the CAARC building, a relevant benchmark for the wind engineering community.

Sampling, sample handling and sample preparation of raw sugar, white sugar, speciality sugars and plantation white sugar

Good sampling methods can reduce the sampling error, bad increase it; the contribution of sampling error to what is perceived to be analytical error is as important as any error in the analyses themselves. The ICUMSA® Referee for Sampling, sample Handling and sample Preparation has carried out a three stage study to establish new sampling guidelines for crystalline and liquid sugars. The paper is a shortened version of his report for the ICUMSA® Proceedings 2021 detailing the experimental layout aiming to obtain an optimized way of preparing test samples from laboratory samples. The analytical results of the conductometric ash determination with their statistical analysis are given and compared with previous studies. Based on the results the 2022 ICUMSA® Methods book includes new standards on the collection of laboratory samples, highlighting known pitfalls and suggesting ways that they may be avoided.

Similarity quantification for linear stochastic systems: A coupling compensator approach

For the formal verification and design of control systems, abstractions with quantified accuracy are crucial. This is especially the case when considering accurate deviation bounds between a stochastic continuous-state model and its finite (reduced-order) abstraction. In this work, we introduce a coupling compensator to parameterize the set of relevant couplings and we give a comprehensive computational approach and analysis for linear stochastic systems. More precisely, we develop a computational method that characterizes the set of possible simulation relations and gives a trade-off between the error contributions on the systems output and deviations in the transition probability. We show the effect of this error trade-off on the guaranteed satisfaction probability for case studies where a formal specification is given as a temporal logic formula.

Managing the mechanical integrity of pressure equipment using a layers of protection framework and incorporating integrity operating windows

A requirement of performance based regulatory regimes for managing process safety is to demonstrate the adequacy of control measures for reducing the risk of a major incident so far as reasonably practical (SFARP). Loss of mechanical integrity allowing loss of primary containment (LOPC) is recognized as a possible initiating step for a major incident. Often LOPC results from the action of damage mechanisms such as corrosion that are able to degrade or destroy the mechanical integrity of the containment envelope of pressure equipment. LOPC, allowing a major incident, generally arises from human error in the form of a lack of recognition of a damage mechanism that should have been identified in a HAZOP or risk assessment and captured in a standard operating practice supported by relevant training. In this paper, a layers of protection framework is proposed for assessing the performance standards and performance indicators required of control measures to allow demonstration that likelihood of LOPC has been reduced SFARP, where LOPC has resulted from a damage mechanism such as corrosion. The context is the requirement to demonstrate adequacy of the practices used to assure the integrity of the containment envelope of pressure equipment required to prevent a major incident. The assessment includes examining the adequacy of inspection as a control measure including the RBI practice of using industry failure frequency databases to underpin the justification of an extension of inspection intervals. An improvement is proposed that recognizes the role of integrity operating windows (following API RP 584) for managing the process environment including providing evidence of control of the service conditions and recording excursions from nominal service conditions. Underpinning the proposed improvement is consideration of the contribution of human error to LOPC events and the need to recognize performance shaping factors that minimize error.

Differential effects of prediction error and adaptation along the auditory cortical hierarchy during deviance processing

Neural mismatch responses have been proposed to rely on different mechanisms, including prediction error-related activity and adaptation to frequent stimuli. However, the hierarchical cortical structure of these mechanisms is unknown. To investigate this question, we recorded hemodynamic responses while participants (N = 54) listened to an auditory oddball sequence as well as a suited control condition. In addition to effects in sensory processing areas (Heschl's gyrus, superior temporal gyrus (STG)), we found several distinct clusters that indexed deviance processing in frontal and parietal regions (anterior cingulate cortex/supplementary motor area (ACC/SMA), inferior parietal lobule (IPL), anterior insula (AI), inferior frontal junction (IFJ)). Comparing responses to the control stimulus with the deviant and standard enabled us to delineate the contributions of prediction error- or adaptation-related brain activation, respectively. We observed significant effects of adaptation in Heschl's gyrus, STG and ACC/SMA, while prediction error-related activity was observed in STG, IPL, AI and IFJ. Additional dynamic causal modeling confirmed the superiority of a hierarchical processing structure compared to a flat structure. Thus, we found that while prediction-error related processes increased with the hierarchical level of the brain area, adaptation declined. This suggests that the relative contribution of different mechanisms in deviance processing varies across the cortical hierarchy.

Assessment of Radiation Effects on Attitude Estimation Processing for Autonomous Things

This article investigates and assesses neutron radiation effects on attitude estimation (AE) processing, typically embedded in inertial navigation systems (INSs) and upcoming autonomous things. Findings highlight the importance of radiation-induced critical failures that can upset the on-board AE processing and consequently the inertial navigation. Radiation tests and analyses were conducted by considering as on-board AE processing the execution of classical AE algorithm on multi-core computer hardware exposed to 14-MeV neutron and thermal neutron radiation. Three computing strategies and different case-study scenarios were tested and compared. Results suggest that the contribution of radiation-induced soft errors to be mitigated on the embedded AE processing is essentially related to single-event functional interrupts (SEFIs) that can lead the inertial navigation to critical failures.

Design of a Highly Sensitive Self-Reference Tamm-Plasmon-Polariton Sensor Employing Ti3C2Tx MXene

This paper presents design and analysis of a self-reference refractive index sensor for precise detection of different concentrations of H5N1 virus in poultry. The sensor is based on the study of Tamm plasmon polariton (TPP) modes excited between Ti <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Tx MXene and cavity layer integrated 1D photonic crystal (PhC). We demonstrate the self-reference characteristics of the sensor, which significantly decreases the error contributions by the environmental factors like the light intensity fluctuations and local temperature variations. The transfer matrix method (TMM) is employed to investigate the reflectance and absorbance of the sensor. The cornerstone of this work lies on the assay of the shift in the wavelength and intensity of the TPP mode vis-à-vis different concentrations of the H5N1 virus. Numerous structural parameters like selection of materials, thickness of the MXene layer, thickness of the cavity layer, and period of the PhC are judiciously optimized to envisage maximum sensing performance. The colormap plot of field distribution infers a strong electric field localization in the cavity layer, which indicates high absorption of TPP modes. Compared to the traditional TPP sensor designed with Ag thin film, the electric field intensity (EFI) and sensitivity of the proposed MXene-based TPP sensor have been boosted by 40% and 15.93% respectively. It is appraised that the proposed sensor delivers a maximum sensitivity of 60.9375 nm/HAU, quality factor of 5485.71, and detection limit of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${2.6}\times {{10}}^{-{6}}$ </tex-math></inline-formula> RIU. The proposed sensor can find suitable applications in the field of biomedical diagnostics, healthcare, food safety, and environmental monitoring.

Lexical competition influences correct and incorrect visual word recognition.

A growing body of research suggests that visual word recognition is error-prone, and that errors may contribute to inhibitory neighbour frequency effects in word identification and reading. The present study used the neighbourhood frequency effect to examine the relationship between lexical competition and error making during visual word recognition. A novel adaptation of the visual world paradigm (VWP) was used, in which participants selected a briefly presented printed target word from an array containing the target, its higher- or lower-frequency neighbour, an orthographic onset competitor, and an orthographically unrelated distractor word. Analyses of the visual inspection of the arrays suggested that lexical competition occurred when words were correctly identified, as competitors were preferentially viewed as a function of their orthographic similarity with the target, and higher-frequency neighbours were preferentially viewed over lower-frequency neighbours. Orthographic similarity and neighbour frequency also influenced error making. Targets were often mistaken for their neighbours, and these errors were more common for targets with higher-frequency neighbours. The time course of target and neighbour viewing for error trials also provided preliminary evidence for two kinds of errors: early-occurring, perceptual errors and later-occurring selection errors that resulted from unsuccessfully resolved lexical competition. Together, these findings suggest that neighbour frequency effects reflect the contribution of both general lexical competition and occasional errors.

Arbitrary-order pressure-robust DDR and VEM methods for the Stokes problem on polyhedral meshes

This paper contains two major contributions. First we derive, following the discrete de Rham (DDR) and Virtual Element (VEM) paradigms, pressure-robust methods for the Stokes equations that support arbitrary orders and polyhedral meshes. Unlike other methods presented in the literature, pressure-robustness is achieved here without resorting to an H(div)-conforming construction on a submesh, but rather projecting the volumetric force onto the discrete H(curl) space. The cancellation of the pressure error contribution stems from key commutation properties of the underlying DDR and VEM complexes. The pressure-robust error estimates in hk+1 (with h denoting the meshsize and k≥0 the polynomial degree of the DDR or VEM complex) are proven theoretically and supported by a panel of three-dimensional numerical tests. The second major contribution of the paper is an in-depth study of the relations between the DDR and VEM approaches. We show, in particular, that a complex developed following one paradigm admits a reformulation in the other, and that couples of related DDR and VEM complexes satisfy commuting diagram properties with the degrees of freedom maps.

Visual and instrumental coverage error of two dental shade guides: an in vivo study

ObjectivesThis study aims to evaluate in vivo the color agreement between natural teeth and dental shade guides by means of visual and instrumental coverage error (mathrm{CE}) index.Materials and methodsThe color of the middle third of 735 incisors was visually determined by two evaluators using the Vita Classical (VC) and Vita 3D Master (V3DM) shade guides. The color match between the natural tooth and the shade tab was rated as poor (P), good (G), or optimum (O) by each observer. CIE color coordinates of the target teeth and shade tabs of VC and V3DM were instrumentally measured using a clinical spectrophotometer. Visual ({mathrm{CE}}_{mathrm{V}}) and instrumental ({mathrm{CE}}_{mathrm{I}}) coverage error indexes were computed using CIELAB and CIEDE2000 metrics for both shade guides. For {mathrm{CE}}_{mathrm{V}} calculation, only the concordant inter-observer determination on tooth shade rated as O–O or O–G was used. The results were evaluated using perceptibility (PT, {mathrm{Delta E}}_{mathrm{ab}}^{*}= 1.2, {mathrm{Delta E}}_{00}= 0.8) and acceptability (AT, {mathrm{Delta E}}_{mathrm{ab}}^{*}= 2.7, {mathrm{Delta E}}_{00}= 1.8) color thresholds for dentistry.ResultsVC and V3DM exhibited {mathrm{CE}}_{mathrm{I}} (2.5, 3.2, and 3.2, 2.7 CIELAB units; 1.9, 2.3, and 2.8, 2.4 CIEDE2000 units, respectively, for O–O and O–G match) and {mathrm{CE}}_{mathrm{V}} (4.7, 4.8, and 4.1, 4.6 CIELAB units; 3.3, 3.4, and 3.4, 3.6 CIEDE2000 units, respectively, for O–O and O–G match) values greater than 50:50% AT for both color difference formulas. {mathrm{CE}}_{mathrm{I}} contributes more than 50% (53.2–82.4% range) to the {mathrm{CE}}_{mathrm{V}} value. This contribution depends on the shade guide used and the quality of the visual rating.ConclusionsThe evaluated shade guides exhibited visual coverage errors above acceptability thresholds, largely due to the contribution of the instrumental coverage error to the visual coverage error.Clinical relevanceIt necessary to further improve commercially available dental shade guides to facilitate achievement of satisfactory esthetics results in clinical practice.

Statistical Quantification of the Effects of Marker Misplacement and Soft-Tissue Artifact on Shoulder Kinematics and Kinetics.

The assessment of shoulder kinematics and kinetics are commonly undertaken biomechanically and clinically by using rigid-body models and experimental skin-marker trajectories. However, the accuracy of these trajectories is plagued by inherent skin-based marker errors due to marker misplacements (offset) and soft-tissue artifacts (STA). This paper aimed to assess the individual contribution of each of these errors to kinematic and kinetic shoulder outcomes computed using a shoulder rigid-body model. Baseline experimental data of three shoulder planar motions in a young healthy adult were collected. The baseline marker trajectories were then perturbed by simulating typically observed population-based offset and/or STA using a probabilistic Monte-Carlo approach. The perturbed trajectories were then used together with a shoulder rigid-body model to compute shoulder angles and moments and study their accuracy and variability against baseline. Each type of error was studied individually, as well as in combination. On average, shoulder kinematics varied by 3%, 6% and 7% due to offset, STA or combined errors, respectively. Shoulder kinetics varied by 11%, 27% and 28% due to offset, STA or combined errors, respectively. In conclusion, to reduce shoulder kinematic and kinetic errors, one should prioritise reducing STA as they have the largest error contribution compared to marker misplacements.

Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network

Abstract. Given its abundant coal mining activities, the Upper Silesian Coal Basin (USCB) in southern Poland is one of the largest sources of anthropogenic methane (CH4) emissions in Europe. Here, we report on CH4 emission estimates for coal mine ventilation facilities in the USCB. Our estimates are driven by pairwise upwind–downwind observations of the column-average dry-air mole fractions of CH4 (XCH4) by a network of four portable, ground-based, sun-viewing Fourier transform spectrometers of the type EM27/SUN operated during the CoMet campaign in May–June 2018. The EM27/SUN instruments were deployed in the four cardinal directions around the USCB approximately 50 km from the center of the basin. We report on six case studies for which we inferred emissions by evaluating the mismatch between the observed downwind enhancements and simulations based on trajectory calculations releasing particles out of the ventilation shafts using the Lagrangian particle dispersion model FLEXPART. The latter was driven by wind fields calculated by WRF (Weather Research and Forecasting model) under assimilation of vertical wind profile measurements of three co-deployed wind lidars. For emission estimation, we use a Phillips–Tikhonov regularization scheme with the L-curve criterion. Diagnosed by the emissions averaging kernels, we find that, depending on the catchment area of the downwind measurements, our ad hoc network can resolve individual facilities or groups of ventilation facilities but that inspecting the emissions averaging kernels is essential to detect correlated estimates. Generally, our instantaneous emission estimates range between 80 and 133 kt CH4 a−1 for the southeastern part of the USCB and between 414 and 790 kt CH4 a−1 for various larger parts of the basin, suggesting higher emissions than expected from the annual emissions reported by the E-PRTR (European Pollutant Release and Transfer Register). Uncertainties range between 23 % and 36 %, dominated by the error contribution from uncertain wind fields.

Analysis of Integrated Vapor Transport Biases

Abstract High-fidelity analyses and forecasts of integrated vapor transport (VT) are central to the study of Earth’s hydrological cycle as well as high-impact phenomena such as monsoons and atmospheric rivers. The impact of the in-line analysis correction-based additive inflation (ACAI) on IVT biases and forecast errors is examined within the Navy Earth System Prediction Capability (Navy ESPC) global coupled system. The ACAI technique uses atmospheric analysis corrections from the data assimilation system to approximate model bias and as a representation of stochastic model error to simultaneously reduce systematic and random errors and improve ensemble performance. ACAI reduces the global average magnitude of the 7- and 14-day IVT bias by 16%–17% during Northern Hemisphere summer, reaching 70% reductions in some tropical regions. The global average IVT bias reduction is similar to the bias reduction for low-level wind speed bias and considerably smaller than the bias reduction in total precipitable water. The localized regions where ACAI increases IVT bias occur where the control IVT biases change sign and structure with increasing forecast lead time, such as the South Asian monsoon region. Substituting analyzed wind or moisture fields for the forecast fields when calculating the forecast IVT confirms that, on average, wind errors dominate the IVT error calculation in the tropics, although wind and moisture error contributions are comparable in the extratropics. The existence of regions where using either analyzed winds or analyzed moisture increases IVT bias or mean absolute error reveals areas with compensating errors.

High temporal resolution estimates of Arctic snowfall rates emphasizing gauge and radar-based retrievals from the MOSAiC expedition

This article presents the results of snowfall rate and accumulation estimates from a vertically pointing 35-GHz radar and other sensors deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The radar-based retrievals are the most consistent in terms of data availability and are largely immune to blowing snow. The total liquid-equivalent accumulation during the snow accumulation season is around 110 mm, with more abundant precipitation during spring months. About half of the total accumulation came from weak snowfall with rates less than approximately 0.2 mmh–1. The total snowfall estimates from a Vaisala optical sensor aboard the icebreaker are similar to those from radar retrievals, though their daily and monthly accumulations and instantaneous rates varied significantly. Compared to radar retrievals and the icebreaker optical sensor data, measurements from an identical optical sensor at an ice camp are biased high. Blowing snow effects, in part, explain differences. Weighing gauge measurements significantly overestimate snowfall during February–April 2020 as compared to other sensors and are not well suited for estimating instantaneous snowfall rates. The icebreaker optical disdrometer estimates of snowfall rates are, on average, relatively little biased compared to radar retrievals when raw particle counts are available and appropriate snowflake mass-size relations are used. These counts, however, are not available during periods that produced more than a third of the total snowfall. While there are uncertainties in the radar-based retrievals due to the choice of reflectivity-snowfall rate relations, the major error contributor is the uncertainty in the radar absolute calibration. The MOSAiC radar calibration is evaluated using comparisons with other radars and liquid water cloud–drizzle processes observed during summer. Overall, this study describes a consistent, radar-based snowfall rate product for MOSAiC that provides significant insight into Central Arctic snowfall and can be used for many other purposes.

Multi‐Task Deep Learning of Daily Streamflow and Water Temperature

AbstractDeep learning (DL) models can accurately predict many hydrologic variables including streamflow and water temperature; however, these models have typically predicted hydrologic variables independently. This study explored the benefits of modeling two interdependent variables, daily average streamflow and daily average stream water temperature, together using multi‐task DL. A multi‐task scaling factor controlled the relative contribution of the auxiliary variable's error to the overall loss during training. Our experiments examined the improvement in prediction accuracy of the multi‐task approach using paired streamflow and water temperature data from sites across the conterminous United States. Our results showed that for 56 out of 101 sites, the best performing multi‐task models performed better overall than the single‐task models in terms of Nash‐Sutcliffe efficiency for predicting streamflow with single‐site models. For 43 sites, the best multi‐task, single‐site models made no significant difference in predicting streamflow. The multi‐task approach had a smaller effect when applied to a model trained with data from 101 sites together, significantly improving performance for only 17 sites. The multi‐task scaling factor was consequential in determining to what extent the multi‐task approach was beneficial. A naïve selection of this factor led to significantly worse‐performing models for 3 of 101 sites when predicting streamflow as the primary variable, and 47 of 53 sites when predicting stream temperature as the primary variable. We conclude that a multi‐task approach can make more accurate predictions by leveraging information from interdependent hydrologic variables, but only for some sites, variables, and model configurations.

Medication Errors and Type 2 Diabetes Management: A Qualitative Exploration of Physicians' Perceptions, Experiences and Expectations From Quetta City, Pakistan.

BackgroundType 2 Diabetes-related medication errors are frequently reported from the hospitals and consequently are of major concern. However, such reports are insufficient when developing healthcare settings are pursued in literature. Keeping this inadequacy in mind, we therefore aimed to explore physicians' perceptions, experiences and expectations of medication errors when managing patients with Type 2 Diabetes Mellitus.MethodsA qualitative design was adopted. By using a semi-structured interview guide through the phenomenology-based approach, in-depth, face-to-face interviews were conducted. Physicians practicing at the medicine ward of Sandeman Provincial Hospital, Quetta, were purposively approached for the study. All interviews were audio-taped, transcribed verbatim, and were then analyzed for thematic contents by the standard content analysis framework.ResultsAlthough the saturation was reached at the 13th interview, we conducted additional two interviews to ensure the saturation. Fifteen physicians were interviewed, and thematic content analysis revealed six themes and nine subthemes. Mixed conceptualization and characterization of medication errors were identified. Medication errors were encountered by all physicians however poor understanding of the system, deficiency of logistics and materials were rated as barriers in reporting medication errors. Among contributors of medication errors, physicians themselves as well as dispensing and patient-related factors were identified. Physicians suggested targeted training sessions on medication error-related guidelines and reporting system. Parallel, establishment of an independent unit, involving the pharmacists, and strict supervision of paramedics to minimize medication errors was also acknowledged during data analysis.ConclusionWith a longer life expectancy and a trend of growing population, the incidences of medication errors are also expected to increase. Our study highlighted prescribing, dispensing and administration phases as contributing factors of medication errors. Although, physicians had poor understanding of medication errors and reporting system, they believed getting insights on guidelines and reporting system is essential. A review of admission and discharge reconciliation must be prioritized and a culture of teamwork, communication and learning from mistakes is needed.