Measurement Accuracy Research Articles

FinTech forecasting using an evolving connectionist system for lenders and borrowers: ecosystem behavior

<span lang="EN-US">Financial Technology (FinTech) which is included in the development of digitalization in the financial sector in the industrial era 4.0. Fintech can make any transactions anywhere with the pillars of Peet-to-Peer (P2P) lending, merchants and crowdfunding. In the P2P Lending pillar, there are borrowers and lenders who are digitized in Fintech devices. Fintech in Indonesia is controlled by a state agency called the Otoritas Jasa Keuangan or Financial Services Authority (OJK). In the movement of P2P Lending, there are borrowers and lenders who can be said to be investors where these activities are reported to the OJK. This data can be forecasted using a neural network approach such as ECoS, which is a method capable of forecasting with learning that develops in the hidden layer. In this research article, we present results on forecasting borrowers with a Mean Absolute Percentage Error (MAPE) of 0.148% and forecasting lenders with an accuracy measurement with MAPE of 0.209% with a learning rate 1 = 0.6 and a learning rate 2 = 0.3. So, this forecasting model can be said as an optimization in FinTech activities on the behavior of borrowers and lenders.</span>

Assessing the suitability of fused deposition modeling to produce acrylic removable denture bases.

To study the feasibility of using poly methyl methacrylate(PMMA) filament and fused deposition modeling (FDM) to manufacture denture bases via the development of a study that considers both conventional and additive-based manufacturing techniques. Five sample groups were compared: heat and cold cured acrylic resins, CAD/CAM milled PMMA, 3D-printed PMMA (via FDM), and 3D-printed methacrylate resin (via stereolithography, SLA). All groups were subjected to mechanical testing (flexural strength, impact strength, and hardness), water sorption and solubility tests, a tooth bonding test, microbiological assessment, and accuracy of fit measurements. The performance of sample groups wasreferred to ISO 20795-1 and ISO/TS 19736. The data was analyzed using one-way ANOVA. Samples manufactured using FDM performed within ISO specifications for mechanical testing, water sorption, and solubility tests. However, the FDM group failed to achieve the ISO requirements for the tooth bonding test. FDM samples presented a rough surface finish which could ultimately encourage an undesirable high level of microbial adhesion. For accuracy of fit, FDM samples showed a lower degree of accuracy than existing materials. Although FDM samples were a cost-effective option and were able to be quickly manufactured in a reproducible manner, the results demonstrated that current recommended testing regimes for conventionally manufactured denture-based polymers are not directly applicable to additive-manufactured denture base polymers. Therefore, new standards should be developed to ensure the correct implementation of additive manufacturing techniques within denture-based fabrication workflow.

Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration

Data collection using thermocouple sensors in low-cost data acquisition is prone to noise interference, which could reduce the data quality. Noise sources such as cold junction compensators, electromagnetic interference, and Johnson noise can significantly affect the reliability and accuracy of conventional measurements. This study aims to improve the quality of thermocouple sensor readings on low-cost data acquisition using calibration method based on deep learning and the denoising process using a wavelet transform. This taken approach successfully increase the accuracy value of 97.67% with a mean absolute error (MAE) of 0.2. The precision also increases of 262.7% as indicated by the result of signal-to-noise ratio (SNR) with a value of 105.29 dB. Comparative analysis was carried out against National Instruments® device and it was found that deep-wavelet method had a lower and higher of MAE and SNRdB values of 16.67% and 0.8% respectively. This study shows that the denoising-calibration method with deep-wavelet can improve the accuracy and reliability of data from low-cost thermocouple devices.

HPETC: History Priority Enhanced Tensor Completion for Network Distance Measurement

In network distance measurement, how to estimate the whole network distance data from partially observed samples has attracted lots of attention because of its significance for network performance evaluation. Matrix completion becomes the most effective approach. However, the two-dimension matrix can only capture the spatial features in the network distance data while ignoring the temporal features. To conquer the problem, few recent studies begin to model the network distance data as a three-dimension tensor and propose tensor completion approaches for distance estimation. Although promising, existing tensor completion approaches still suffer the problem of low recovery accuracy and high measurement cost because they ignore the history priority information. To fully utilize both spatial and temporal features hidden in the distance data, this paper formulates a novel History Priority Enhanced Tensor Completion (HPETC) for distance estimation as a weighted tensor nuclear norm minimization problem where the weight is defined based on the history subspaces information. To solve the weighted tensor nuclear norm minimization problem, we firstly transform it into a factorization-based Frobenius norm minimization problem to avoid costly T-SVD computations, and then propose an iterative algorithm to solve the transformed problem. We further derive a theoretical sampling bound that is lower than the existing sampling bound, thus leads a lower measurement cost. We demonstrate the effectiveness of the proposed algorithm by conducting extensive experiments using two real network distance datasets. The result shows that the proposed algorithm can not only improve the estimation accuracy but also reduce the sampling complexity compared to the state-of-the-art approaches.

Enhancing vehicular platoon stability in the presence of communication Cyberattacks: A reliable longitudinal cooperative control strategy

This study proposes a reliable longitudinal distributed control strategy for connected and automated vehicles (CAVs) in the presence of communication cyberattacks. The proposed strategy is designed to utilize dependable measurements from onboard sensors to evaluate the reliability of vehicle-to-vehicle (V2V) information and enhance the platoon string stability in an integrated manner. Specifically, this paper proposes a real-time reliability evaluation mechanism for the ego vehicle based on sensing and communication information from multiple predecessors, employing an upper-tailed chi-squared test. The mechanism is then incorporated into a distributed multi-predecessor linear feedback controller to adaptively weigh the preceding vehicles’ information, ensuring robustness against cyberattacks and maintaining the string stability of the vehicular platoon. To better understand the whole framework, sufficient conditions of true string stability and pseudo string stability are theoretically derived, unveiling the disturbance amplification jointly impacted by measurement accuracy of onboard sensors, cyberattack severity in V2V communication, and control parameters. Numerical experiments are conducted to validate the controller across various types of communication cyberattacks. The results suggest that the proposed control strategy can significantly alleviate the impact of cyberattacks and simultaneously dampen traffic oscillations effectively.

Fatigue crack detection in orthotropic steel deck bridges applied by FMC/TFM using reflected waves of multiple paths (1)

We introduce the latest developed FMC/TFM (Full Matrix Capture and Total Focusing Method) and its application for fatigue crack detection in orthotropic steel deck bridge. This developed inspection system consists of the advanced high-speed ultrasonic inspection equipment and the remotely operated four-wheel scanner with a 32- channel ultrasonic array probe for angle beam inspection. We developed MP-FMC/TFM (Multiple Paths Phased- array FMC/TFM) in this project. MP-FMC/TFM enables to visualize outline image of cracks clearly by image synthesis based on the propagation route information of direct path and the other paths reflected on the bottom of an inspection object against clacks. This developed inspection system enables to generate clear B-scope images of deck plate crack and bead (in deck-to-rib weld) crack with high-resolution. Furthermore, it enables to synthesizes 3D images of crack propagation with high-resolution along the weld line in real time by integrating the B-scope images every time the scanner moves 2mm. As a result of verification test of fatigue crack propagation test specimens, it was confirmed that this inspection system enables to visualize crack shape clearly and to achieves measurement accuracy of ±0.8mm for depth of deck plate crack, and ±1.0mm for depth of bead crack. Currently, verification tests are being conducted on an actual bridge, and practical application is planned in near future.

Surface accuracy prediction method for mesh reflector antenna considering uncertainty factors in manufacturing process

The cable-net structure of mesh reflector antenna meets the requirement of surface accuracy by optimizing the shape and tension. However, in the process of manufacturing and assembly, there are inevitable random errors and structure gravity, which lead to the displacement of the nodes in surface, the surface accuracy of the antenna decreases after fabrication eventually. It is challenging to analyze the uncertainty factors and the effects on the surface accuracy. Therefore, this paper analyzes the uncertainty factors of the antenna during fabrication and proposes a method to predict the surface accuracy of the antenna after manufacturing and assembly. Firstly, based on the equilibrium equation of the cable-net structure, the mechanism of node displacement induced by uncertainty factors leading to the reduction of surface accuracy is analyzed. Secondly, based on the structure characteristics and manufacturing conditions of the antenna, the uncertainty factors including cable-net manufacturing and assembly errors, cable-truss assembly errors and gravity are analyzed qualitatively and quantitatively, the distributions of the uncertainty factors are given. Thirdly, based on Monte Carlo method, the mean and interval of the surface accuracy of different dimension parameters are predicted, the empirical prediction formulas of the surface accuracy mean and interval are fitted. Finally, the accuracy and effectiveness of the proposed formulas are verified by several numerical simulations and surface accuracy measurement experiments. The prediction method can effectively guide the design and craft of large-aperture mesh reflector antenna.

High-accuracy optical vector network analyzer for optical notch and bandpass responses

A high-accuracy optical vector network analyzer (OVNA) based on optical carrier-suppressed double sideband (CS-DSB) modulation is proposed and experimentally demonstrated. The ±1st-order sideband signals are generated by CS-DSB modulation and then pass through the symmetric optical device under test (DUT). The band-stop or band-pass responses can be realized by detecting and processing the double frequency of the driven RF signal. Compared with the conventional symmetrical DSB-based OVNA, the measurement accuracy is improved by eliminating the errors caused by the even-order sidebands, and the complexity is reduced as the proposed method with only one step measurement can avoid the complex postprocessing. In addition, the optical carrier is aligned to the center frequency of the DUT by employing the Pound–Drever–Hall (PDH) technique, which provides stable measurement. At the same time, the limitation that the band-pass responses cannot be measured by the traditional single-sideband (SSB)-based OVNA is overcome. Additionally, accurate magnitude and phase responses of the DUT near the optical carrier can be also achieved since the proposed OVNA is optical filter-immune. The proposed method is theoretically analyzed and verified by experiment. A Fabry–Perot (FP) interferometer serves as the symmetric DUT; the band-stop responses in a frequency range of 6 GHz are obtained with a resolution of 1.2 MHz; and the band-pass responses with the range from 0 to 13 GHz offsetting the optical carrier are also obtained. The measurement time can reach up to 30 min with high stability. The proposed OVNA offers enhanced accuracy and a stable approach for applications in photonic systems and other innovations.

Space-time image velocimetry in blurred scenes based on BSTI-DCGAN data augmentation

Due to the limited sample quantity and the complex data collection process of the blurred space-time image (BSTI) dataset, the deep learning-based space-time image velocimetry (STIV) results in larger errors when applied to blurry videos. To enhance the measurement accuracy, we propose the use of STIV in blurred scenes based on BSTI-deep convolutional generative adversarial network (DCGAN) data augmentation. Firstly, BSTI-DCGAN is developed based on the DCGAN. This network utilizes a bilinear interpolation-convolution module for upsampling and integrates coordinated attention and multi-concatenation attention to enhance the resemblance between generated and real images. Next, further expanding the dataset by using artificially synthesized space-time images subsequently, all space-time images are transformed into spectrograms to create a training dataset for the classification network. Finally, the primary spectral direction is detected using the classification network. The experimental results indicate that our approach effectively augments the dataset and improves the accuracy of practical measurements. Under the condition of video blur, the relative errors of the average flow velocity and discharge are 3.92% and 2.72%, respectively.

A Compensation Method for the Geomagnetic Measurement Error of an Underwater Ship-Borne Magnetometer Based on Constrained Total Least Squares

When magnetic matching aided navigation is applied to an underwater vehicle, the magnetometer must be installed inside the vehicle, considering the navigation safety and concealment of the underwater vehicle. Then, the interference magnetic field will seriously affect the accuracy of geomagnetic field measurement, which directly affects the accuracy of geomagnetic matching aided navigation. Therefore, improving the accuracy of geomagnetic measurements inside the vehicle through error compensation has become one of the most difficult problems that requires an urgent solution in geomagnetic matching aided navigation. In order to solve this problem, this paper establishes the calculation model of the internal magnetic field of the underwater vehicle and the geomagnetic measurement error model of the ship-borne magnetometer. Then, a compensation method for the geomagnetic measurement error of the ship-borne magnetometer, based on the constrained total least square method, is proposed. To verify the effectiveness of the method proposed in this paper, a simulation experiment of geomagnetic measurement and compensation of a ship-borne three-axis magnetometer was constructed. Among them, to be closer to the real situation, a combination of the geomagnetism model, the elliptic shell model and the magnetic dipole model was used to simulate the internal magnetic field of the underwater vehicle. The experimental results indicated that the root mean square error of geomagnetic measurement in an underwater vehicle was less than 5 nT after compensation, and the accuracy of geomagnetic measurement met the requirements of geomagnetic matching aided navigation.

Exact surface measurement based on phase error insensitive method for white-light scanning interferometer

Abstract Phase error induced by the scanning error of piezoelectric transducer and vibration in white-light interference signal (WLIS) impose limitations on measurement accuracy. In this paper, two techniques are investigated to eliminate phase error cooperatively. A monochromatic interferometer owning the same optical path as the white-light scanning interferometer (WLSI) is employed to detect and compensate the phase error. Due to the incomplete consistency between the detected phase error and its actual distribution, an algorithm is introduced to further enhance the measurement accuracy. The simulation is given to prove the proposed method is available in different magnitude of noise. In the experiment, the front surface of wedged window is measured, whose maximum fluctuations are reduced to 28.8 nm and 3.0 nm from 62.7 nm with the gradual application of two technologies, and the repeatability reach 5.0 nm and 0.4 nm from 16.8 nm, respectively. The root mean square height Sq and maximum height Sz ultimately decrease to 1.3 nm and 1.6 nm. The measurement of step with height of about 3.0 m also has the good accuracy and repeatability after phase error elimination. Since the different reflectivity of measured surfaces in the wavelength range of light source will also bring the measurement error, its influences are analyzed by simulation. The proposed techniques provide a phase error insensitive method for WLSI that has potential on measurement in the complex environment.

Investigation of triaxial cables and microdetectors in small field dosimetry

Background. Small field dosimetry presents unique challenges with source occlusion, lateral charged particle equilibrium and detector size. As detector volume decreases, signal strength declines while noise increases, deteriorating the signal-to-noise ratio (SNR). This issue may be compounded by triaxial cables connecting detectors to electrometers. However, effects of cables, critical for precision dosimetry, are often overlooked. There is a need to evaluate triaxial cable and detector impacts on SNR in small fields. The purpose of this study is to evaluate the influence of triaxial cables and microdetectors on signal-to-noise ratios in small-field dosimetry. This study also aims to establish the importance of cable quality assurance for measurement accuracy. Methods. Six 9.1 m length triaxial cables from different manufacturers were tested with six microdetectors (microDiamond, PinPoint, EDGE, Plastic scintillator, microSilicon, SRS-Diode). A 6 MV photon beam (TrueBeam) was used, with a water phantom at 5 cm depth with 0.5 × 0.5 cm2 to 10 × 10 cm2 fields at 600 MU min−1. Readings were acquired using cable-detector permutations with a dedicated electrometer (except the scintillator which has its own). Cables had differing connector types, conductor materials, insulation, and diameters. Detectors had various sensitive volumes, materials, typical signals, and bias voltages. Results. Normalized field output correction factors (FOFs) relative differences of 13.4% and 4.6% between the highest and lowest values across triaxial cables for 0.5 × 0.5 cm2 and 1 × 1 cm2 fields, respectively. The maximum difference in FOF between any cable-detector combinations was 0.2% for the smallest field size. No consistent FOF trend was observed across all detectors when increasing cable diameter. Additionally, the non-normalized FOF differences of 0.9% and 0.3% were observed between cables for 0.5 × 0.5 cm2 and 1 × 1 cm2 fields, respectively. Conclusions. Regular triaxial cable quality assurance is critical for precision small field dosimetry. A national protocol is needed to standardize cable evaluations/calibrations, particularly for small signals (<pC) from modern detectors. This could enhance measurement accuracy and treatment delivery with advanced small-field radiotherapy techniques that promise improved patient outcomes. Further studies should expand detector and cable models tested across institutions to establish robust quality control guidelines.

Weak Force Sensing Based on Optical Parametric Amplification in a Cavity Optomechanical System Coupled in Series with Two Oscillators

AbstractIn the realm weak force sensing, an important issue is to suppress fundamental noise (quantum noise and thermal noise), as they limit the accuracy of force measurement. In this study, a weak force sensing scheme that combines a degenerate optical parametric amplifier (OPA) and an auxiliary mechanical oscillator into a cavity optomechanical system to reduce quantum noise is investigated. It is demonstrated that the noise reduction of two coupled oscillators depends on their norm mode splitting. and provide a classic analogy and quantum perspective for further clarification. Besides, the noise reduction mechanism of OPA is to reduce the fluctuation of photon number and enhance the squeezing of the cavity field. A specific design is proposed that aimed at enhancing the joint effect of both, beyond what can be achieved using OPA alone or two series coupled oscillators. This scheme provides a new perspective for deeper understanding of cavity field squeezing and auxiliary oscillator in force sensing.

Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT

Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remained unexplored. The accuracy of previous methodologies has been limited by the inclusion of non-parenchymal liver regions. To overcome this limitation, we present a novel deep-learning (DL) based method tailored for the automatic selection of parenchymal portions in CT images. This innovative method automatically delineates circular regions for effectively detecting hepatic steatosis. We use 1,014 multinational CT images to develop a DL model for segmenting liver and selecting the parenchymal regions. The results demonstrate outstanding performance in both tasks. By excluding non-parenchymal portions, our DL-based method surpasses previous limitations, achieving radiologist-level accuracy in liver attenuation measurements and hepatic steatosis detection. To ensure the reproducibility, we have openly shared 1014 annotated CT images and the DL system codes. Our novel research contributes to the refinement the automated detection methodologies of hepatic steatosis on CT images, enhancing the accuracy and efficiency of healthcare screening processes.

An inversion method for calculating formation and borehole parameters in X-ray lithology density logging

The use of X-ray sources in place of the 137Cs sources used in traditional lithology density logging methods has become a new trend in the development of nuclear logging techniques. How to eliminate the effects of drilling fluids or mudcake in the measurement process is a key question that determines the accuracy of measurement. In order to reduce the effects of mudcake and improve the accuracy of measurement of formation parameters, this paper presents an inversion method that can accurately calculate formation and borehole parameters and is suitable for X-ray lithology density logging. The general process of this inversion method is described below. First, a response model for broad-beam attenuation during X-ray lithology density logging is derived. Subsequently, the responses of four detectors under various formation and borehole conditions are studied by means of Monte Carlo simulation, and the energy spectra measured by each detector are divided into four energy windows (ranges) depending on the correlation with formation parameters. Finally, accurate values of formation and borehole parameters are obtained through iterative inversion using the Levenberg-Marquardt (LM) algorithm. The results of this study show that compared with previously established analysis methods, the inversion method based on forward modeling can effectively improve the accuracy of measurement of formation density and lithology index during X-ray lithology density logging, reduce the influence of the borehole environment, and overcome the deficiencies of data processing techniques based on the spine and ribs plot.

Automated multivolume placental reconstruction using three-dimensional power Doppler ultrasound and infrared camera tracking.

Placental insufficiency contributes to many obstetric pathologies however there is no bedside clinical tool to evaluate placental perfusion. We have developed a method to acquire multiple three-dimensional power Doppler ultrasound (3D PD-US) volumes of placental vasculature with infrared camera tracking providing global coordinates. These are automatically reconstructed ('stitched') into a model of the entire placenta. The purpose of the study was to evaluate the accuracy of automated reconstruction in an US phantom and apply this technique to human placentas. A custom-designed acrylic phantom was constructed with dimensions mimicking a third-trimester placenta, containing 12 quadrilateral towers of varying heights submersed in tissue-mimicking solution. Triplicated three-dimensional ultrasound volumes of this phantom were acquired at three different acquisition angles using infrared camera tracking. Data was transformed into a three-dimensional cartesian volume and automatically stitched. A single-centre, cross-sectional feasibility study was conducted on uncomplicated second-to-third trimester singleton pregnancies using standardised obstetric settings. Multiple 3D PD-US and grayscale volumes of the placenta were acquired with infrared camera tracked coordinates. Volumes were stitched to create a model of placental vasculature. 6 phantom datasets were reconstructed at each of 3 volume angles with a median of 9 volumes required. Perfect volume alignment occurred in 66.7% of 648 datapoints. Mean distance error for volume misalignment was 2.92mm. Measurements of 210 distances in each stitched volume (2160 total distances) differed an average of 1.51mm from true measurements. These compare favourably with recent literature, though for a substantially larger phantom. 17 participants were scanned with 92% reconstruction success per placental volume set and 100% participant achievability. Median reconstruction time was 10 minutes. Placental vasculature was qualitatively assessed to be present, continuous, and detailed throughout. Volume measurement of entire segmented placentas was highly repeatable (ICC 0.96). We present an automated method to model the entire structure and vasculature of second-to-third trimester placentas, with verified accuracy and clinical feasibility for grayscale and power Doppler. This study builds the foundation to develop a practical screening tool for detecting placental insufficiency, and expansion to adult organ perfusion evaluation. This article is protected by copyright. All rights reserved.

Choosing a camera and optimizing system parameters for speckle contrast optical spectroscopy

Speckle contrast optical spectroscopy (SCOS) is an emerging camera-based technique that can measure human cerebral blood flow (CBF) with high signal-to-noise ratio (SNR). At low photon flux levels typically encountered in human CBF measurements, camera noise and nonidealities could significantly impact SCOS measurement SNR and accuracy. Thus, a guide for characterizing, selecting, and optimizing a camera for SCOS measurements is crucial for the development of next-generation optical devices for monitoring human CBF and brain function. Here, we provide such a guide and illustrate it by evaluating three commercially available complementary metal–oxide–semiconductor cameras, considering a variety of factors including linearity, read noise, and quantization distortion. We show that some cameras that are well-suited for general intensity imaging could be challenged in accurately quantifying spatial contrast for SCOS. We then determine the optimal operating parameters for the preferred camera among the three and demonstrate measurement of human CBF with this selected low-cost camera. This work establishes a guideline for characterizing and selecting cameras as well as for determining optimal parameters for SCOS systems.

High-Entropy CeNbO4+δ-Based Ceramics with Ultrahigh Comprehensive Thermosensitive Performances.

Next-generation advanced high-temperature sensors rely heavily on negative temperature coefficient thermosensitive ceramics with low cost, small volume, high sensitivity, and fast response. However, thus far, the enormous challenge of achieving ultrahigh stability and accuracy has become a critical bottleneck restricting the development of thermosensitive ceramics in high-temperature sensor applications. Here, we propose a high-entropy strategy to design a "cation valence self-equilibrium" system in CeNbO4+δ-based ceramics introducing redox couple compensation and ultrahigh density dislocations to solve the problem of temperature-dependent oxygen nonstoichiometry that restricts the performances of high-temperature thermosensitive ceramics. Ferroelastic domains are generated by enhancing the configurational entropy at both A and B sites, resulting in a dramatic increase of dislocation density to >1010 mm-2, which ultimately optimizes the thermosensitive performances. Extreme temperature measurement accuracy with R2 as high as 999.98‰ and RSS as low as 0.011 and high-temperature stability with ΔR/R0 as low as 0.23% after aging at 873 K for 1000 h are realized in high-entropy CeNbO4+δ-based ceramics, indicating a breakthrough in the comprehensive performances of thermosensitive ceramics. This work opens up an effective way to design thermosensitive materials with ultrahigh comprehensive performance to meet the requirements of advanced high-temperature sensors.

Kalman filter for radio source power and direction of arrival estimation

Images are an important source of information for spacecraft navigation. Based on an image and a known attitude, triangulation techniques (intersection or resection) are often used for positioning and navigation. In the resection problem, an observer estimate its unknown location by using angle measurements to points at known locations (i.e., landmarks), the localization performance depending on the accuracy of the angle measurements. As a contribution to resection for spacecraft navigation, we considers the dynamic image estimation problem based on radio interferometry, i.e., image of radio source power, where the measurements are sample covariance matrices (SCMs). Considering the case where several measurements are available as well as a known dynamic linear model of image evolution, a.k.a a linear state model, the minimum mean-squared error image estimator (MMSE) is given by the Kalman filter (KF) or one of its variants. However standard Kalman-like filters are not a priori suitable for the problem at hand since the measurements (i.e., SCMs) cannot be formulated analytically as a function of state parameters to be estimated (i.e., radio source power). In fact, this lack of analytical formulation can be circumvented by a statistical linear fitting allowing the SCMs to be expressed in terms of the state. This linear fitting introduces an additive residual noise, equivalent to a measurement noise, whose covariance matrix depends on the current state, a non-standard case for a measurement model. The covariance matrix of the residual noise is derived whatever the distributions of the radio sources and of the additive noise at the samples level, unveiling the contribution of their multivariate kurtosis. The proposed method is evaluated on simulated data representative of a dynamic radio interferometry framework. The results show that the proposed method is capable of effectively tracking moving radio sources in complex scenes with theoretical guaranties when the signal multivariate kurtosis is known.

#1128 Accuracy of shorter iohexol glomerular filtration rate measurement protocols in individuals with preserved kidney function

Abstract Background and Aims Measuring glomerular filtration rate (mGFR) using exogenous tracers is recommended in a number of settings with plasma one-compartment multi-sample protocols (MSP) being the most commonly used and iohexol being the dominant tracer. The accuracy of MSP protocols has mostly been evaluated in the setting of reduced GFR where delayed initial and final samples are recommended. Much less is known about MSPs when GFR is not decreased and the default protocol tends to include initial sampling at 120 minutes and final sampling at 240 minutes post iohexol injection. The objective of this study was to assess the performance of shorter MSPs with earlier initial (60 and 90 minutes) and final (150, 180, and 210 minutes) sampling times in individuals with preserved GFR. Method Participants were recruited from a 4-year prospective, observational study evaluating the cardiovascular, renal, and bone health profile of young adults with Type 1 Diabetes. The reference mGFR was calculated using a plasma iohexol clearance one compartment slope intercept method with 5 samples collected between 120-240 minutes post iohexol injection. Four different combinations of shorter sampling strategies were investigated: 60-150 min, 60-180 min, 90-180 min and 90-210 min. Performance was evaluated using measurements of bias, precision, and accuracy (P2, P5, and mean absolute error). Results Mean age of the 43 participants was 23.9 ± 1.9 yrs with a mean eGFRCKiD-1 of 95.5 ± 15.9 ml/min/1.73 m2 and mean reference mGFR (120-240 min) of 102.3 ± 13.7 ml/min/1.73 m2. All exploratory shorter mGFRs performed well, with median biases less than 1 ml/min/1.73 m2 and mean absolute error less than 1.6 ml/min/1.73 m2. All shorter mGFRs were within 5% of the reference mGFR and the majority were within 2%. Conclusion These results demonstrate that shortening the mGFR procedure in individuals with preserved GFR provided very similar results to the current standard while significantly decreasing procedure time. The cost savings and improved feasibility of these shortened protocols should reduce some of the barriers to more widespread adoption of GFR measurement.