Significant Figures Research Articles

Parametric Continuation Analysis of heat and mass transfer of Ethylene glycol nanofluid flow between two vertical plates

Abstract This study examines the behavior of nanofluid with three nano particles namely ZnO2, T iO2 and Graphene with base fluid as Ethylene glycol trapped between two vertical plates, with particular attention to the temperature-dependent electrical and magnetic conductivity, affected by the Soret and Dufour effects. The governing equations for fluid flow are transformed into dimensionless ordinary differential equations using the Adeosun and Ukaegbu method [1]. The Parametric Continuation Method (PCM) is then employed to analyze the numerical results. The numerical results obtained using the (PCM) method are graphically validated against the solutions provided by the bvp4c package, showing excellent agreement between them. Additionally, numerical results of the (PCM) method are also compared with previously published studies and about two decimal places accuracy is reported. It is found that ZnO2is more effective in reducing the nanofluids temperature because of its high thermal conductivity than the other two. So ZnO2

A Decade of Prostate Intervention Robots: Systematic Review

Abstract: The image-guided prostate intervention robot can assist surgeons in performing minimally invasive intervention procedures. It has the advantage of overcoming the disadvantages of a traditional surgeon's hand tremor and dependence on the need for an experienced surgeon, improving the intervention precision, and reducing the hazards caused by normal soft tissues due to surgical precision errors compared to traditional intervention procedures. In the design of prostate intervention robots, structural design, materials, actuators, and compatibility issues should be considered. Based on the above considerations, a representative literature of the last decade was selected for review. Patents and articles on image-guided prostate intervention robots published in the last 10 years (2013-2023)were searched in several electronic databases, focusing on keywords (structural design, puncture accuracy, image compatibility, actuator) for screening. We retrieved a total of 26 prostate intervention robots guided by different images, classified them by US, MRI, MRI-US and CT, selected representative robots guided by different image modalities for review and tabulated them for comparison, and finally boldly predicted that the future direction will be prostate robots guided by MRI-US images. Image-guided prostate intervention robots can improve the deficiencies of conventional intervention procedures. In the future how to improve the intervention accuracy will be the primary problem of all intervention robots, through structural design, materials, actuators, and image compatibility issues have been improved.

Oscillatory traveling waves provide evidence for predictive coding abnormalities in schizophrenia

BackgroundThe computational mechanisms underlying psychiatric disorders are hotly debated. One hypothesis, grounded in the Bayesian predictive coding framework, proposes that schizophrenia patients have abnormalities in encoding prior beliefs about the environment, resulting in abnormal sensory inference, which can explain core aspects of the psychopathology, such as some of its symptoms. MethodsHere, we tested this hypothesis by identifying oscillatory traveling waves as neural signatures of predictive coding. By analyzing an EEG dataset comprising 146 schizophrenia patients and 96 age-matched healthy controls, during resting states and a visual backward masking task. ResultsWe found that schizophrenia patients have stronger top-down alpha-band traveling waves compared to healthy controls during resting state, supposedly reflecting overly precise priors at higher levels of the predictive processing hierarchy. We also found stronger bottom-up alpha-band waves in schizophrenia patients during a visual task, in line with the notion of enhanced signaling of sensory precision errors. ConclusionsOur results yield a novel spatial-based characterization of oscillatory dynamics in schizophrenia, considering brain rhythms as traveling waves and providing a unique framework to study the different components involved in a predictive coding scheme. Altogether, our findings significantly advance our understanding of the mechanisms involved in fundamental pathophysiological aspects of schizophrenia, promoting a more comprehensive and hypothesis-driven approach to psychiatric disorders.

New Insight of Nanosheet Enhanced Oil Recovery Modeling: Structural Disjoining Pressure and Profile Control Technique Simulation

This study presents a novel Enhanced Oil Recovery (EOR) method using Smart Black Nanocards (SLNs) to mitigate the environmental impact of conventional thermal recovery, especially under global warming. Unlike prior studies focusing on wettability alteration via adsorption, this research innovatively models ‘oil film detachment’ in a reservoir simulator to achieve wettability alteration. Using the CMG-STARS (2020) simulator, this study highlights SLNs’ superior performance over traditional chemical EOR and spherical nanoparticles by reducing residual oil saturation and shifting wettability toward water-wet conditions. The structural disjoining pressure (SDP) of SLNs reaches 20.99 × 103 Pa, 16.5 times higher than spherical particles with an 18.5 nm diameter. Supported by the Percus–Yevick (PY) theory, the numerical model achieves high accuracy in production history matching, with oil recovery and water cut fitting within precision error ranges of 0.02 and 0.05, respectively. This research advances chemical EOR technologies and offers an environmentally sustainable, efficient recovery strategy for low-permeability and heavy oil reservoirs, serving as a promising alternative to thermal methods.

The primes perform a Benford dance

This paper develops a new description of the asymptotics for the empirical distributions of significands and significant digits associated with [Formula: see text], where [Formula: see text] denotes the [Formula: see text]th prime number. The work utilizes the space of probability measures on the significand, endowed with a suitable Kantorovich metric, as well as finite-dimensional projections thereof. For sequences sufficiently close to [Formula: see text], it is shown that the limit points of the associated empirical distributions form a circle that is made up of all rescalings of a single absolutely continuous distribution, and is centered at a distribution known as Benford’s law (BL). The precise rate of convergence to that circle is determined. Moreover, even in the infinite-dimensional setting of significands the convergence is seen to occur along a distinguished low-dimensional object, in fact, along a smooth curve intimately related to BL. By connecting [Formula: see text] and BL in a new way, the results rigorously confirm well-documented experimental observations and complement known facts in the literature.

Prediction of Lung Cancer Disease Using Machine Learning Techniques

The pursuit of algorithms utilizing external examples to formulate extensive hypotheses predicting the occurrence of novel instances is recognized, as supervised machine learning (SML). One of the jobs that intelligent systems perform the most frequently is supervised classification. The goal of this work is to evaluate supervised learning algorithms, explain SML classification methodologies, and identify the most effective classification algorithm given the available data. Two distinct machine learning (ML) techniques were examined: Random Forest (RF) and Neural Networks (NN). The algorithms were implemented using Python for knowledge analysis. For the categorization, 310 cases from a lung cancer data set were employed, with 15 features serving as independent variables and one serving as the dependent variable. In comparison to NN classification methods, RF was found to be the algorithm with the highest precision and accuracy, according to the results. The study reveals that while the kappa statistic and mean square error (MSE) are factors on the one hand, the time required to create a model and precision (accuracy) are factors on the other. Consequently, to have supervised predictive ML algorithms need to be precise, accurate, and minimum error. Thus, as a consequence of the research, we are currently at this analysis. The categorizing of NNs accuracy is 0.75 the MSE is 0.25, The RF classification accuracy is 0.89 and the MSE is 0.21.

Ionization limits and upper levels in P-XIV and in Cl-XVI

Abstract In this study, the ground state and excited state ionization limits with and without consideration of relativistic effects are computed for a helium-like iso-spectrum level sequence for the following configurations: 1s 2 S 0 1 , 1s3s S 0 1 , 1s3p P 0 o 3 , 1s2p P 1 o 1 , 1s3p P 1 o 1 , 1s4p P 1 o 1 , 1s5p P 1 o 1 , 1s6p P 1 o 1 , 1s3p P 1 o 3 , 1s3s S 1 3 , 1s4s S 1 3 , 1s3d D 2 1 , 1s3d D 2 1 , 1s5d D 2 3 , 1s2d P 2 o 3 , 1s3d D 3 3 , 1s4d D 3 3 , 1s5d D 3 3 for nuclear charge Z . The non-relativistic ionization limits are derived from the weakest bound electron potential model, and relativistic corrections are included by using the Breit-Pauli approximation, which has also been applied to the computed relativistic ionization limits at high-order polynomials in the effective nuclear charge Z ′ . This study suggests two hundred and seventy-two excited states ionization potentials with and without a relativistic effect. Furthermore, it fills the gap of thirty-four excited state ionization potentials and their energies for the following configurations: 1s3s S 0 1 , 1s3p P 0 o 3 , 1s2p P 1 o 1 , 1s3p P 1 o 1 , 1s4p P 1 o 1 , 1s5p P 1 o 1 , 1s6p P 1 o 1 , 1s3p P 1 o 3 , 1s3s S 1 3 , 1s4s S 1 3 , 1s3d D 2 1 , 1s3d D 2 1 , 1s5d D 2 3 , 1s2d P 2 o 3 , 1s3d D 3 3 , 1s4d D 3 3 , 1s5d D 3 3 in P − X I V and, C l − X V I which is very onerous to compute owing to foreign-level configuration mixing causing high perturbation, are currently not listed at NIST. This work shows excellent agreement between the computed values and NIST values. The yield ionization limits I n r and I N , Z ′ departing from NIST ones by not more than 1.0 eV and 0.00001 eV, respectively. Both sets of data reported are precise up to 4 decimal place.

SYNTHESIS AND RADIOGRAPHY OF NEW ZIRCON-MANGANITES OF LANTHANUM AND ALKALINE EARTH METALS AND CALCULATION OF THEIR THERMODYNAMIC PROPERTIES

Annotation. The synthesis of LaMeIIZrMnO6 compounds (MeII – Mg, Ca, Sr, Ba) was carried out using ceramic technology in the range of 800-1200оС by the interaction of lanthanum (III) oxides of the " extra clean." qualification, zirconium (IV), manganese (III) and magnesium, calcium, strontium and barium carbonates of the " clean for analysis " brand.". The weights of the starting materials were weighed with an accuracy of up to the fourth decimal place. Stoichiometric amounts of the starting materials were carefully ground in an agate mortar, then poured into alund crucibles and subjected to heat treatment for solid-phase interaction in air in the "SNOL" furnace. X-ray methods have established that all synthesized zircon-manganites crystallize in cubic symmetry with the following lattice parameters: LaMgZrMnO6 – а = 13,46 ±0,02 Å; V0 = 2437,86 ±0,05 Å3; Z = 4; V° elem cell = 609,47 ± 0,02 Å3; r x-ray = 4,42 g/сm3; r picn = 4,35 ± 0,07 g/сm3; LaCaZrMnO6 – а = 14,50 ± 0,02Å; V0 = 3048,63 ± 0,06 Å3; Z = 4; V° elem cell = 762,16 ± 0,02 Å3; r x-ray = 3,67 g/сm3; r picn = 3,62 ± 0,05 g/сm3; LaSrZrMnO6 – а = 14,56 ± 0,02 Å; V0 = 3087,52 ±0,06 Å3; Z = 4; V° elem cell = 771,88 ± 0,02 Å3; r x-ray. = 4,03 g/сm3; r picn.= 3,99 ± 0,04 g/сm3, LaBaZrMnO6 – а = 14,79 ± 0,02Å; V0 = 3233,45 ± 0,06 Å3; Z = 4; V° elem cell = 808,36 ± 0,02 Å3; r x-ray = 4,25 g/сm3; r picn= 4,19 ± 0,05 g/сm3. It was found that with an increase in the ionic radii in the Mg® Ca®Sr®Ba series, the values of the parameter "a", the volumes of lattices and elementary cells of zircono-manganites increase. The calculation of the temperature dependence of the heat capacity and the standard heat capacity by indirect methods is carried out. The equations of temperature dependences of the investigated zircon-manganites of lanthanum and alkaline earth metals are derived.

Impact of temperature-reliant thermal conductivity and viscosity variations on the convection of Jeffrey fluid in a rotating cellular porous layer

In this analysis, the collective impact of temperature-dependent thermal conductivity and viscosity variations on the convective instability of a Jeffrey fluid in a rotating layer of cellular porous material is examined using an improved Jeffrey–Darcy model. This study has significant implications for cellular foams made from plastics, ceramics and metals, in which radiative heat transmission can be taken as a diffusion practice. Utilizing the linear stability concept and Galerkin method, approximate analytical and numerical solutions accurate to one decimal place are offered. The analysis reveals that the effect of the thermal conductivity variation factor and the rotation factor is to postpone the convective wave, whereas the viscosity variation factor and the Jeffrey factor have a dual effect in the form of rotation. The range of the convective cell is reduced with cumulating thermal conductivity variation factor, viscosity variation factor, Jeffrey factor and rotation factor. In the absence of rotation, the range of the convective cell is not dependent on the Jeffrey factor or the viscosity variation factor. Furthermore, the outcomes are matched with the existing literature for the specific case of this investigation.

An Enhanced Multiscale Retinex, Oriented FAST and Rotated BRIEF (ORB), and Scale-Invariant Feature Transform (SIFT) Pipeline for Robust Key Point Matching in 3D Monitoring of Power Transmission Line Icing with Binocular Vision

Power transmission line icing (PTLI) poses significant threats to the reliability and safety of electrical power systems, particularly in cold regions. Accumulation of ice on power lines can lead to severe consequences, such as line breaks, tower collapses, and widespread power outages, resulting in economic losses and infrastructure damage. This study proposes an enhanced image processing pipeline to accurately detect and match key points in PTLI images for 3D monitoring of ice thickness using binocular vision. The pipeline integrates established techniques such as multiscale retinex (MSR), oriented FAST and rotated BRIEF (ORB) and scale-invariant feature transform (SIFT) algorithms, further refined with m-estimator sample consensus (MAGSAC)-based random sampling consensus (RANSAC) optimization. The image processing steps include automatic cropping, image enhancement, feature detection, and robust key point matching, all designed to operate in challenging environments with poor lighting and noise. Experiments demonstrate that the proposed method significantly improves key point matching accuracy and computational efficiency, reducing processing time to make it suitable for real-time applications. The effectiveness of the pipeline is validated through 3D ice thickness measurements, with results showing high precision and low error rates, making it a valuable tool for monitoring power transmission lines in harsh conditions.

Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials with binary and count outcomes

BackgroundThe fixed effects model is a useful alternative to the mixed effects model for analyzing stepped-wedge cluster randomized trials (SW-CRTs). It controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. While all clusters in a SW-CRT are typically designed to crossover from the control to receive the intervention, some trials can end with unexposed clusters (clusters that never receive the intervention), such as when a trial is terminated early due to safety concerns. It was previously unclear whether unexposed clusters would contribute to the estimation of the intervention effect in a fixed effects analysis. However, recent work has demonstrated that including an unexposed cluster can improve the precision of the intervention effect estimator in a fixed effects analysis of SW-CRTs with continuous outcomes. Still, SW-CRTs are commonly designed with binary outcomes and it is unknown if those previous results extend to SW-CRTs with non-continuous outcomes.MethodsIn this article, we mathematically prove that the inclusion of unexposed clusters improves the precision of the fixed effects intervention effect estimator for SW-CRTs with binary and count outcomes. We then explore the benefits of including an unexposed cluster in simulated datasets with binary or count outcomes and a real palliative care data example with binary outcomes.ResultsThe simulations show that including unexposed clusters leads to tangible improvements in the precision, power, and root mean square error of the intervention effect estimator. The inclusion of the unexposed cluster in the SW-CRT of a novel palliative care intervention with binary outcomes yielded smaller standard errors and narrower 95% Wald Confidence Intervals.ConclusionsIn this article, we demonstrate that the inclusion of unexposed clusters in the fixed effects analysis can lead to the improvements in precision, power, and RMSE of the fixed effects intervention effect estimator for SW-CRTs with binary or count outcomes.

Determining state points through the radial distribution function of Yukawa fluids at equilibrium

Based on our previous work [Fluid Phase Equilibria, 2023, 567, 113709], we here use the radial distribution function (RDF) to determine the state points (density and temperature) of a fluid under the Yukawa potential at equilibrium. The reduced density and reduced temperature are defined as ρ*=ρσ3 and β*=1/T*=ϵ/kBT, respectively. Through the Molecular Dynamics (MD) simulations, we obtain equilibrium configurations and use these data for building models via two methods. The first method establishes two empirical correlations for each potential considered, one between the heights of the first peaks of the RDFs and state points, as well as the other between the displacements of the first peaks of the RDFs and state points. Through these empirical correlations, we can determine the state points of new Yukawa fluid systems with 100% accuracy. The second method utilizes artificial neural network models to predict state points from the heights and displacements of the first peaks of the RDFs, achieving 100% accuracy when the predicted results are rounded to one decimal place. The success of these methods again demonstrates the feasibility of determining state points solely based on equilibrium configurations, is an extension from the Lennard-Jones fluids to the Yukawa potential related fluids.

Integration of Metrology in Grinding and Polishing Processes for Rotationally Symmetrical Aspherical Surfaces with Optimized Material Removal Functions

Aspherical surfaces, with their varying curvature, minimize aberrations and enhance clarity, making them essential in optics, aerospace, medical devices, and telecommunications. However, manufacturing these surfaces is challenging because of systematic errors in CNC equipment, tool wear, measurement inaccuracies, and environmental disturbances. These issues necessitate precise error compensation to achieve the desired surface shape. Traditional methods for spherical optics are inadequate for aspherical components, making accurate surface shape error detection and compensation crucial. This study integrates advanced metrology with optimized material removal functions in the grinding and polishing processes. By combining numerical control technology, computer technology, and data analysis, we developed CAM software (version 1) tailored for aspherical surfaces. This software uses a compensation correction algorithm to process error data and generate NC programs for machining. Our approach automates and digitizes the grinding and polishing process, improving efficiency and surface accuracy. This advancement enables high-precision mass production of rotationally symmetrical aspherical optical components, addressing existing manufacturing challenges and enhancing optical system performance.

Efficient hardware accelerators for k-nearest neighbors classification using most significant digit first arithmetic

Efficient hardware accelerators for k-nearest neighbors classification using most significant digit first arithmetic

Characterization of a Large Family of Convergent Series That Leads to a Rapid Acceleration of Slowly Convergent Logarithmic Series

Logarithmic series are known to have a very slow rate of convergence. For example, it takes more than the first 20,000 terms of the sum of the reciprocals of squares of the natural numbers to attain 5 decimal places of accuracy. In this paper, I will devise an acceleration scheme that will yield the same level of accuracy with just the first 400 terms of that power series. To accomplish this, I establish a relationship between all monotonically decreasing sequence of positive terms whose sum converges, a positive number <i>ρ </i>and a differentiable function <i>φ</i>. Then, I use <i>ρ </i>and <i>φ </i>to define the <i>T<sub>φ, ρ </sub></i>transformations on the partial sums of any convergent series. Furthermore, I prove that these <i>T<sub>φ, ρ </sub></i>transformations yield a rapid rate of convergence for many slowly convergent logarithmic series. Finaly, I provide several examples on how to compute <i>φ </i>if one is given the convergent series of decreasing, positive terms.

The Precision Improvement of Robot Integrated Joint Module Based on a New ADRC Algorithm

This article analyzes the factors causing the precision error of the robot joint module, such as gear meshing disturbance, output elastic deformation, and load mutation. An improved active disturbance rejection control (ADRC) algorithm is proposed to overcome the uncertainty of nonlinear factors and variables of this research topic. The output precision loss of the joint module is introduced as a new input variable of ADRC, combined with the input variable of torque current of the joint module. The extended state observer is redesigned, and the online estimation of disturbance is realized. According to the disturbance estimation results, the current loop algorithm of permanent magnet synchronous motors is improved to compensate the torque disturbance of the robot joint module. The experimental results show that the improved ADRC algorithm can obviously suppress the disturbance of the joint module, weaken the meshing error and torque output deformation of the harmonic reducer gear, and improve the control accuracy of the joint module.

B-143 Using Big Data Analytics to Measure the Multi-year Consistency of Sigma Metrics for Vitros Oncology Assays

Abstract Background Sigma metrics assess lab quality by incorporating precision, accuracy and total error allowed (TEa). Sigma metrics report quality on a 0-6 scale, with 6 representing World-Class quality. Due to logistical constraints, Sigma metrics are often calculated with limitations such as a small number of analyzers, single reagent lot or short duration. QuidelOrtho developed a novel approach using big data analytics for the objective, automated calculation of Sigma metrics across thousands of Vitros analyzers running in real lab conditions. This approach was previously used to report the Sigma metric performance for 115 assays using data from 2022. Of particular interest is the consistency of quality levels for Vitros oncology assays assessed in the study. The aim of this analysis was to examine Sigma metrics for Vitros oncology assays and compare Sigma metrics to data from 2022. Methods The methods follow the previous approach using the same Python algorithm to extract worldwide QC data from Jan. to Nov. 2023 (inclusive). A QC fluid lot from the study period was selected for each analyte to align with the fluid level reported in the 2022 study, which was based on medical relevance. Metrics were calculated for each set of QC data that originated from a single analyzer, reagent lot and calibration curve; the median Sigma metric is reported in the Results. Proficiency testing limits were utilized as TEa limits where available, with preference to CLIA 2024 limits. Results The following assays reported Sigma metrics as >6 using both 2022 and 2023 data: alpha-fetoprotein, cancer antigen 125, carcinoembryonic antigen, prostate-specific antigen (PSA), free PSA, IgA, and IgG. Assays with higher metrics using 2023 data: cancer antigen 19-9 (5.0 vs >6) and IgM (5.3 vs >6). Assays with lower metrics using 2023 data: cancer antigen 15-3 (4.8 vs 4.4). Conclusions Vitros oncology assays report similar performance for 2022 and 2023. All Vitros oncology assays assessed report Sigma metrics of 4 or greater, indicating they achieve results within the corresponding allowable error.

Galileo-an Artificial Intelligence tool for evaluating pre-implantation kidney biopsies.

Pre-transplant procurement biopsy interpretation is challenging, also because of the low number of renal pathology experts. Artificial intelligence (AI) can assist by aiding pathologists with kidney donor biopsyassessment. Herein we present the "Galileo" AI tool, designed specifically to assist the on-call pathologist with interpreting pre-implantation kidney biopsies. A multicenter cohort of whole slide images acquired from core-needle and wedge biopsies of the kidney was collected. A deep learning algorithm was trained to detect the main findings evaluated in the pre-implantation setting (normal glomeruli, globally sclerosed glomeruli, ischemic glomeruli, arterioles and arteries). The model obtained on the Aiforia Create platform was validated on an external dataset by three independent pathologists to evaluate the performance of the algorithm. Galileo demonstrated a precision, sensitivity, F1 score and total area error of 81.96%, 94.39%, 87.74%, 2.81% and 74.05%, 71.03%, 72.5%, 2% in the training and validation sets, respectively. Galileo was significantly faster than pathologists, requiring 2min overall in the validation phase (vs 25, 22 and 31min by 3 separate human readers, p < 0.001). Galileo-assisted detection of renal structures and quantitative information was directly integrated in the final report. The Galileo AI-assisted tool shows promise inspeeding up pre-implantation kidney biopsy interpretation, as well as inreducing inter-observer variability. This toolmay represent a starting point for further improvements based on hardendpoints such as graft survival.

B-144 Sigma Metrics for Vitros Reproductive Endocrinology Assays Across Multiple Fluid Levels

Abstract Background Sigma metrics assess laboratory quality by incorporating precision, accuracy and total error allowed (TEa). Sigma metrics report quality on a scale of 0 to 6, with 6 representing the World-Class quality. QuidelOrtho developed a novel approach using big data analytics for the objective and automated calculation of Sigma metrics across thousands of Vitros® Analyzers running in real laboratory conditions. This approach was previously used to report the Sigma metric performance for 115 assays using data from 2022. A limitation of that study is the metrics were reported for a single fluid that was selected for medical relevance. However, reproductive endocrinology assays have complex medical utilization and there is a benefit to understanding Sigma metrics at multiple levels. The aim of this poster is to provide additional data on the Sigma metrics for reproductive endocrinology assays. Methods The methods follow the previous approach using the same Python algorithm to extract QC data from Jan. to Aug. 2022 (inclusive). Up to seven QC fluid lots were selected for each analyte. Metrics were calculated for each set of QC data that originated from a single analyzer, reagent lot and calibration curve; the median Sigma metric is reported in the Results. Proficiency testing limits were utilized as TEa limits where available, with preference to CLIA 2024 limits. Results Sigma metrics are reported for multiple fluids for the following assays: estradiol, follicular stimulating hormone, luteinizing hormone, progesterone, prolactin, testosterone, human chorionic gonadotropin, and thyroid stimulating hormone. All fluids examined report Sigma metrics greater than 4, which is considered “Good” performance. An exception is luteinizing hormone which has Sigma metrics = 1.9 at the lowest reported concentration. Conclusions Vitros reproductive endocrinology assays report strong Sigma metrics across multiple concentrations, indicating they are highly capable of achieving results within the allowable error.

B-142 Using Big Data Analytics to Measure the Multi-year Consistency of Sigma metrics for Vitros Cardiac Assays

Abstract Background Sigma metrics assess lab quality by incorporating precision, accuracy and total error allowed (TEa). Sigma metrics report quality on a 0-6 scale, with 6 representing World-Class quality. Due to logistical constraints, Sigma metrics are often calculated with limitations such as a small number of analyzers, single reagent lot or short duration. QuidelOrtho developed a novel approach using big data analytics for the objective, automated calculation of Sigma metrics across thousands of Vitros(R) analyzers running in real lab conditions. This approach was previously used to report the Sigma metric performance for 115 assays using data from 2022. Of particular interest is the consistency of quality levels for Vitros cardiac assays assessed in the study. Methods The methods follow the previous approach using the same Python algorithm to extract QC data from Jan. to Nov. 2023 (inclusive). A QC fluid lot from the study period was selected for each analyte to align with the fluid level reported in the 2022 study, which was based on medical relevance. Metrics were calculated for each set of QC data that originated from a single analyzer, reagent lot and calibration curve; the median Sigma metric is reported in the Results. Proficiency testing limits were utilized as TEa limits where available, with preference to CLIA 2024 limits. Results The following assays reported Sigma metrics as &amp;gt;6 using both 2022 and 2023 data: apolipoprotein A1, apolipoprotein B, CK-MB, dLDL, LDH, myoglobin, NT-proBNP, and triglycerides. Assays with higher metrics using 2023 data: CK (5.2 vs 5.5), dHDL (5.8 vs &amp;gt;6), hsTnI (4.1 vs 4.3), and NT-proBNP2 (4.2 vs &amp;gt;6). Assays with lower metrics using 2023 data: cholesterol (5.8 vs. 5.3) and homocysteine (&amp;gt;6 vs 5.9). CKMB2 (&amp;gt;6) is a newer assay that did not have metrics reported in 2022. Vitros cardiac assays report metrics within 0.5 Sigma of the 2022 metrics. An exception is NT-proBNP2, which changed from 4.2 to &amp;gt;6. However, the 2023 analysis has almost double the number of Vitros analyzers (51 vs 90), representing new use of NT-proBNP2. Conclusions Vitros cardiac assays report consistent metrics when using 2023 data compared to 2022. All Vitros cardiac assays assessed report Sigma metrics of 4 or greater, indicating they achieve results within the corresponding allowable error.