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Background The reporting of surgical instrument errors historically relies on cumbersome, non-automated, human-dependent, data entry into a computer database that is not integrated into the electronic medical record. The limitations of these reporting systems make it difficult to accurately estimate the negative impact of surgical instrument errors on operating room efficiencies. We set out to determine the impact of surgical instrument errors on a two-hospital healthcare campus using independent observers trained in the identification of Surgical Instrument Errors. Methods This study was conducted in the 7 pediatric ORs at an academic healthcare campus. Direct observations were conducted over the summer of 2021 in the 7 pediatric ORs by 24 trained student observers during elective OR days. Surgical service line, error type, case type (inpatient or outpatient), and associated length of delay were recorded. Results There were 236 observed errors affecting 147 individual surgical cases. The three most common errors were Missing+ ( n  = 160), Broken/poorly functioning instruments ( n  = 44), and Tray+ ( n  = 13). Errors arising from failures in visualization (i.e. inspection, identification, function) accounted for 88.6% of all errors (Missing+/Broken/Bioburden). Significantly more inpatient cases (42.73%) had errors than outpatient cases (22.32%) ( p  = 0.0129). For cases in which data was collected on whether an error caused a delay (103), over 50% of both IP and OP cases experienced a delay. The average length of delays per case was 10.16 min. The annual lost charges in dollars for surgical instrument associated delays in chargeable minutes was estimated to be between $6,751,058.06 and $9,421,590.11. Conclusions These data indicate that elimination of surgical instrument errors should be a major target of waste reduction. Most observed errors (88.6%) have to do with failures in the visualization required to identify, determine functionality, detect the presence of bioburden, and assemble instruments into the correct trays. To reduce these errors and associated waste, technological advances in instrument identification, inspection, and assembly will need to be made and applied to the process of sterile processing.

The article discusses the determination of the required number of experiments when determining the compressive strength of concrete using the elastic rebound method. It is shown that it is not correct to accept a deterministic value for the minimum number of experiments for all grades (classes of concrete). This value depends on the average strength of concrete and the instrument error of the device used. For a given coefficient of variation, with increasing concrete strength, the required number of experiments increases. For the maximum permissible coefficient of variation of concrete strength of 13.5%, calculated dependences of the minimum number of experiments on the average strength of concrete were obtained for the minimum and maximum instrumental errors of the sclerometers used, determined by analyzing the passport data used in Russian organizations involved in the inspection of buildings and structures. The proposed method, which relates the strength of the material of the structure under study and the instrumental error of the device used, can be extended to other methods of determining strength by non-destructive methods and can be the basis of the technical specifications for the creation of devices for non-destructive testing of concrete.

For decades, stable boundary layer (SBL) turbulence has proven challenging to measure, parameterize, simulate, and interpret. Uncrewed aircraft systems (UAS) are becoming a reliable method to sample the atmospheric boundary layer, offering new perspectives for understanding the SBL. Moreover, continual computational advances have enabled the use of large-eddy simulations (LES) to simulate the atmosphere at ever-smaller scales. LES is therefore a powerful tool in establishing a baseline framework to understand the extent to which vertical profiles from UAS can represent larger-scale SBL flows. To quantify the representativeness of observations from UAS profiles and eddy-covariance observations within the SBL, we performed a random error analysis using a suite of six large-eddy simulations for a wide range of stabilities. We combine these random error estimates with emulated observations of a UAS and eddy-covariance systems to better inform future observational studies. For each experiment, we estimate relative random errors using the so-called relaxed filtering method for first- and second-order moments as functions of height and averaging time. We show that the random errors can be on the same order of magnitude as other instrument-based errors due to bias or dynamic response. Unlike instrument errors, however, random errors decrease with averaging time. For these reasons, we recommend coupling UAS observations with other ground-based instruments as well as dynamically adjusting the UAS vertical ascent rate to account for how errors change with height and stability.

Conventional radar altimeter makes measurement of sea surface height (SSH) in one-dimensional profiles along the ground tracks of a satellite. Such profiles are combined via various mapping techniques to construct two-dimensional SSH maps, providing a valuable data record over the past two decades for studying the global ocean circulation and sea level change. However, the spatial resolution of the SSH is limited by both coarse sampling across the satellite tracks and the instrument error in the profile measurements. A new satellite mission based on radar interferometry offers the capability of making high-resolution wide-swath measurement of SSH. This mission is called Surface Water and Ocean Topography (SWOT), which will demonstrate the application of swath altimeter to both oceanography and land hydrology. This paper presents a brief introduction to the design of SWOT, its performance specification for SSH, and the anticipated spatial resolution and coverage, demonstrating the promise of SWOT for fundamental advancement in observing SSH. A main objective of the paper is to address issues in the anticipated transition of conventional profile altimetry to swath altimetry in the future—in particular, the need for consistency of the new observing system with the old for extending the existing data record into the future. A viable approach is to carry a profile altimeter in the SWOT payload to provide calibration and validation of the new measurement against the old at large scales. This is the baseline design of SWOT. The unique advantages of the approach are discussed in the context of a new standard for observing the global SSH in the future.

All eddy-covariance flux measurements are associated with random uncertainties which are a combination of sampling error due to natural variability in turbulence and sensor noise. The former is the principal error for systems where the signal-to-noise ratio of the analyser is high, as is usually the case when measuring fluxes of heat, CO2 or H2O. Where signal is limited, which is often the case for measurements of other trace gases and aerosols, instrument uncertainties dominate. Here, we are applying a consistent approach based on auto- and cross-covariance functions to quantify the total random flux error and the random error due to instrument noise separately. As with previous approaches, the random error quantification assumes that the time lag between wind and concentration measurement is known. However, if combined with commonly used automated methods that identify the individual time lag by looking for the maximum in the cross-covariance function of the two entities, analyser noise additionally leads to a systematic bias in the fluxes. Combining data sets from several analysers and using simulations, we show that the method of time-lag determination becomes increasingly important as the magnitude of the instrument error approaches that of the sampling error. The flux bias can be particularly significant for disjunct data, whereas using a prescribed time lag eliminates these effects (provided the time lag does not fluctuate unduly over time). We also demonstrate that when sampling at higher elevations, where low frequency turbulence dominates and covariance peaks are broader, both the probability and magnitude of bias are magnified. We show that the statistical significance of noisy flux data can be increased (limit of detection can be decreased) by appropriate averaging of individual fluxes, but only if systematic biases are avoided by using a prescribed time lag. Finally, we make recommendations for the analysis and reporting of data with low signal-to-noise and their associated errors.

The paper considers metrological instrumentation for studying the rheological properties of magnetic fluids. It also presents the design of a magnetic coaxial viscometer. There is a metrological analysis of the factors that affect the measurement accuracy. Errors are systematized into 2 main groups: method and instrumental. The authors analyse the contribution of each group to the total metrological error. It is established that the temperature instability of the liquid under study and the error in determining the level of the liquid in contact with the measuring cylinder have the greatest effect on the measurement results. There is a theoretical assessment of the instrument error and its minimum value. The authors propose the main directions for improving the viscometer design. They also show the possibility of a significant reduce in the total measurement error by calibrating the device. There are comparative studies of experimental and reference liquids indicating good accuracy and reliability of experimental data.

Background There is currently no method to precisely measure the errors that occur in the sequencing instrument/sequencer, which is critical for next-generation sequencing applications aimed at discovering the genetic makeup of heterogeneous cellular populations. Results We propose a novel computational method, SequencErr, to address this challenge by measuring the base correspondence between overlapping regions in forward and reverse reads. An analysis of 3777 public datasets from 75 research institutions in 18 countries revealed the sequencer error rate to be ~ 10 per million (pm) and 1.4% of sequencers and 2.7% of flow cells have error rates > 100 pm. At the flow cell level, error rates are elevated in the bottom surfaces and > 90% of HiSeq and NovaSeq flow cells have at least one outlier error-prone tile. By sequencing a common DNA library on different sequencers, we demonstrate that sequencers with high error rates have reduced overall sequencing accuracy, and removal of outlier error-prone tiles improves sequencing accuracy. We demonstrate that SequencErr can reveal novel insights relative to the popular quality control method FastQC and achieve a 10-fold lower error rate than popular error correction methods including Lighter and Musket. Conclusions Our study reveals novel insights into the nature of DNA sequencing errors incurred on DNA sequencers. Our method can be used to assess, calibrate, and monitor sequencer accuracy, and to computationally suppress sequencer errors in existing datasets.

We investigated the effect of flow dependency in the assimilation of high-density, high-frequency observations. Radial winds from a Doppler radar are assimilated using a regional hybrid four-dimensional variational data assimilation (4D-Var) scheme with a flow-dependent background error covariance. To consistently assimilate 5 km × 5.625° cell-averaged radial winds at an interval of 10 min, the spatial and temporal correlations of the observation error are statistically diagnosed to be incorporated into the hybrid 4D-Var. The spatial correlation width is larger than that expected from instrument error, suggesting a contribution from representation error whose propagation is also considered to lead to temporal correlation, the width of which is diagnosed to increase with forecast time. The background error covariance also has an important role in incorporating observational information into the analysis. Single observation experiments show that the hybrid 4D-Var has more small-scale structure in its flow-dependent background error correlation than the 4D-Var limited from the climatological background error covariance mainly in the former part of the assimilation window. This suggests the higher potential of the hybrid 4D-Var to allow more higher-wavenumber components in the increment. A case study shows that the hybrid 4D-Var makes better use of the dense and frequent observations, reflecting more detailed representation of flow throughout the assimilation window, leading to promising results in the forecast. Sensitivity experiments also show that it is important to use the optimal observation error correlation. It is suggested that the flow-dependent background error becomes necessary to effectively use high-resolution, high-frequency observations.

All sensing systems have some inherent error. Often, these errors are systematic, and observations taken within a similar region of space and time can have correlated error structure. However, the data from these systems are frequently assumed to have completely independent and uncorrelated error. This work introduces a correlated error model for GNSS reflectometry (GNSS-R) using observations from NASA’s Cyclone Global Navigation Satellite System (CYGNSS). We validate our model against near-simultaneous observations between two CYGNSS satellites and double-difference our results with modeled observables to extract the correlated error structure due to the observing system itself. Our results are useful to catalog for future GNSS-R missions and can be applied to construct an error covariance matrix for weather data assimilation.

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review.