Mean Absolute Deviation Research Articles

A novel method for multiple targets localization based on normalized cross-correlation adaptive variable step-size dynamic template matching

The template matching method has been widely utilized in the defect detection of wafer surfaces. However, the traditional matching approaches are limited by illumination, noise, and deformation, which cannot meet the requirements of accuracy and robustness. In this paper, a novel multiple targets localization method, named Normalized Cross-correlation Adaptive Variable Step-Size Dynamic Template (NCC-AVSSDT) matching, is proposed to improve the accuracy and efficiency of image localization, which combines the advantages of NCC and AVSSDT. The AVSSDT method is utilized to dynamically adjust the scanning step size based on the NCC matching coefficients. This approach optimizes the scanning process, accelerating convergence toward the optimal matching position. Experimental results verify the accuracy and robustness of the proposed method under different conditions, especially when dealing with rotational variations and variations in noise textures. Therefore, NCC-AVSSDT can be used to perform multiple targets localization of chip image in nearly real-time. Three experiment types were used for comprehensive evaluations, including multiple targets, noise, and rotation angles. Experimental results show that NCC-AVSSDT is much better than the sequential similarity detection algorithm and mean absolute deviation methods in terms of multiple targets (0.667 vs 0.811 s, 0.832 s) and success rate (100% vs 35%, 20%).

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

Influence of Tacrolimus Intrapatient Variability on Allograft Rejection Frequency and Survival Following Liver Transplantation.

Tacrolimus is the primary calcineurin inhibitor used in immunosuppressive regimens to prevent allograft rejection (AR) after organ transplantation. Recent studies have linked intrapatient variability (IPV) of tacrolimus with AR occurrence and reduced survival, especially in kidney transplant recipients. However, limited data are available on the impact of tacrolimus IPV on adverse outcomes after liver transplantation (LT). The aim of this study was to assess the association between tacrolimus IPV using various methodologies with acute AR and long-term patient survival after LT. All patients who underwent LT from January 2010 to July 2021 were retrospectively evaluated. Tacrolimus IPV was calculated for each patient using the mean and SD, mean absolute deviation (MAD), coefficient of variation (CV), and time in therapeutic range (TTR). These measures were then compared with AR within the first 24 months after LT and to long-term survival. Out of 234 patients, 32 (13.7%) developed AR and 183 (78.2%) survived, with a mean follow-up of 101 ± 43 months. Tacrolimus IPV, assessed by mean, SD, MAD, and CV, was 8.3 ± 2.1, 2.7 ± 1.3, 32.0% ± 11.7%, and 39.4% ± 15.4%, respectively. There was no statistically significant correlation between Tacrolimus IPV and AR or survival post-LT. In a large cohort of patients from diverse racial backgrounds, tacrolimus IPV was not associated with clinically relevant outcomes such as AR and survival after LT.

Assessment of liquid-liquid partition for the assignment of descriptors for the solvation parameter model

The solvation parameter model uses five system independent descriptors to characterize compound properties defined as excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity, A, hydrogen-bond basicity, B, and McGowan's characteristic volume, V, to model transfer properties between condensed phases. The V descriptor is assigned from structure. For compounds liquid at 20 °C the E descriptor can be assigned from the characteristic volume and its refractive index. The E descriptor for compounds solid at 20 °C and the S, A, and B descriptors are experimental properties traditionally assigned from chromatographic, liquid-liquid partition, and solubility measurements. In this report liquid-liquid partition constants in totally organic and aqueous biphasic systems are evaluated as a standalone technique for descriptor assignments. Using six totally organic biphasic systems the S, A, and B descriptors were assigned with an average absolute deviation (AAD) of about 0.04, 0.03, and 0.04, respectively, compared with the best estimate of the true descriptor values for 65 compounds. The E descriptor for compounds solid at 20 °C can only be estimated with an AAD of approximately 0.1. For six aqueous biphasic systems the B descriptor is assigned with a lower AAD of 0.028 and higher AAD of 0.08 and 0.05 for the S and A descriptors, respectively, than for the totally organic biphasic systems for compounds with a reliable value for the E descriptor. The preferred system for descriptor assignments utilizes both totally organic biphasic systems (heptane-1,1,1-trifluoroethanol, isopentyl ether-propylene carbonate, isopentyl ether-ethanolamine, heptane-ethylene glycol, heptane-formamide, and 1,2-dichloroethane-ethylene glycol) and aqueous biphasic systems (octanol-water, cyclohexane-water) with the possible substitution of some systems with alternative systems of similar selectivity. For 55 varied compounds this combination of eight organic and aqueous biphasic systems resulted in an AAD of approximately 0.03, 0.02, and 0.02 for the S, A, and B descriptors compared to the best estimate of the true descriptor value. For 30 compounds solid at 20 °C the AAD for the E descriptor of 0.11 is poorly assigned. The relative average absolute deviation in percent (RAAD) corresponds to 9.7 %, 3.1 %. 4.0 % and 8.3 % for E, S, A, and B, respectively, for the eight biphasic systems. Liquid-liquid partition is compared to reversed-phase liquid and gas chromatography as a standalone technique for descriptor assignments.

Autobench V1.0: Benchmarking Automation for Electronic Structure Calculations.

This work reports on new software for automatic conformer energy benchmarking calculations for flexible molecules. The software workflow consists of four parts: conformational search, preoptimization, optimization, and frequency calculations at a higher level and last calculations using several theoretical levels. The software was written to be user-friendly and versatile to be used by nonexperts in computational chemistry. Any theoretical levels available in either Gaussian 16 or ORCA 5 may be applied in the benchmarking study. The workflow will automatically run conformational search calculations and deal with conformers that converge to the same minimum and those that show a negative frequency. At the end of the workflow, the user will have the mean absolute deviations and the most accurate method/DFT functional and basis set in comparison to the benchmark to be applied for the molecular system of interest. Case examples are given at the end of the paper that may help users to get insight into the software's main features.

Damage localization on reinforced concrete slab structure using electromechanical impedance technique and probability-weighted imaging algorithm

Damage localization plays a key role in structural damage identification and health monitoring. This paper proposed a new damage localization approach using electromechanical impedance/admittance (EMI/EMA) technique integrated with probability-weighted imaging algorithm based on Gaussian distribution. The proposed approach comprises five steps namely measurement of the EMA signature from an array of piezoelectric ceramic lead zirconate titanate (PZT) transducers, computation of statistic damage indicator, establishment of regression model for the indicator, damage visualization via Gaussian distribution and final damage localization. Experimental validation of the approach was conducted on a reinforced concrete (RC) slab with artificial crack damages detected by using an array of surface-bonded PZT patches. Comparative studies on localization accuracy were performed by utilizing root mean square deviation (RMSD), baseline-changeable RMSD namely RMSDk, mean absolute percentage deviation (MAPD) and correlation coefficient (CC). Experimental results demonstrated that the proposed approach could achieve precise localization of concrete crack with error as small as 0 mm, and that using RMSDk index exhibited the highest localization accuracy in average. The proposed approach promoted the capability of the EMI technique in characterization of concrete structural damages.

A Fuzzy Portfolio Model With Cardinality Constraints Based on Differential Evolution Algorithms

Uncertain information in the securities market exhibits fuzziness. In this article, expected returns and liquidity are considered as trapezoidal fuzzy numbers. The possibility mean and mean absolute deviation of expected returns represent the returns and risks of securities assets, while the possibility mean of expected turnover represents the liquidity of securities assets. Taking into account practical constraints such as cardinality and transaction costs, this article establishes a fuzzy portfolio model with cardinality constraints and solves it using the differential evolution algorithm. Finally, using fuzzy c-means clustering algorithm, 12 stocks are selected as empirical samples to provide numerical calculation examples. At the same time, fuzzy c-means clustering algorithm is used to cluster the stock yield data and analyse the stock data comprehensively and accurately, which provides a reference for establishing an effective portfolio.

Experimental study on single- to two-phase flow and heat transfer of water in a small tube

Experimental investigation on flow boiling heat transfer in a small tube under high system pressure has been conducted in this study. The test-section is constructed with a 316L stainless steel tube. The inner diameter and heated length of the test-section are 5 mm and 930 mm, respectively. The working fluid is deionized water. The operating conditions of the experiments are as follows, the mass flux from 548 to 978 kg/(m2·s), the wall heat flux from 136 to 238 kW/m2, and the system pressure from 1.0 to 2.9 MPa. The experimental results showed that the inlet subcooling mainly affects the onset of nucleate boiling, but its impact on the heat transfer coefficient at the same vapor quality is almost negligible. The heat transfer coefficient increases with the increase of mass flux and wall heat flux, especially in the region where xe < 0.02. The average heat transfer coefficient is higher under high system pressure owing to the variation of the thermophysical properties. Additionally, the existing flow and heat transfer correlations cannot predict the flow boiling heat transfer coefficient and frictional pressure drop in the experiments accurately enough. Therefore, the new flow and heat transfer correlations are developed based on the experimental data in this paper, and the mean absolute deviation of the flow boiling heat transfer correlation and frictional pressure drop correlation are 13.3% and 12.1%, respectively.

A robust non-contact heart rate estimation from facial video based on a non-parametric signal extraction model

Remote photoplethysmography (rPPG) is a non-contact method to extract heart rate (HR) pulse signals from facial videos. However, challenges persist due to head motion disturbances and ambient illumination variations. To address these issues, a novel non-parametric signal extraction model is proposed for HR estimation using the webcam camera. We first select the better-quality RGB signal from the divided face regions through the ranking of signal-to-noise ratio (SNR). Then, we use blind source separation and skin reflection modeling to eliminate the effect of ambient illumination. Finally, we employ a non-parametric signal extraction model named circulant singular spectrum analysis (CiSSA), with frequency-based decomposition and energy-driven reconstruction, to extract the HR-related pulse signal effectively. To evaluate the performance of our method, we conducted the validation experiments on three datasets, including two public datasets (UBFC and PURE dataset) and one self-collected dataset (ILVA dataset). Our method outperformed significantly on all three datasets, achieving Mean Absolute Deviation (MAD) of 2.42, 1.36, and 2.84 bpm, Root Mean Square Error (RMSE) of 1.89, 2.24, and 3.58 bpm, and Pearson Correlation Coefficients (PCC) of 0.98, 0.98, and 0.93, respectively. The experimental results demonstrate that our method achieves comparable performance, particularly in scenarios involving severe head motion disturbances and variations in ambient illumination.

Numerical investigation of carbon dioxide capture using nanofluids via machine learning

Using nanofluids to capture CO2 is a promising method of reducing emissions. The goal of this study was to develop models that could forecast how well water-based nanofluids would absorb CO2. Several machine learning models, such as Decision Tree, Random Forest, eXtreme Gradient Boosting, and K-Nearest Neighbors, underwent training using a total of 1306 experimental datasets. These datasets contained information on the CO2 solubility in aqueous solutions for different types of nanoparticles. In terms of Average Absolute Relative Deviation (AARD%), Mean Absolute Error (MAE), Relative Absolute Error, Mean Squared Error, and Correlation Coefficient, the predictive performance on separate test data was assessed. The XGBoost model demonstrates a higher degree of accuracy in simulating the absorption capacity of aqueous nanofluid compared to other models. This is evident through the AARD value of 2.8%, MSE of 0.00084, MAE of 0.012 and an R value of 0.992. The research provides insight into the utilization of different machine learning algorithms for simulating the absorption of CO2 by nanofluids. By employing accurate data-driven models, the efficiency of nanofluid-based CO2 capture processes can be enhanced through improvements in their design and operating conditions.

Generalized Analytical Well-Test Solutions for Vertically Fractured Wells in Commingled Reservoirs

Abstract Layered zones with vertical fracture are often found in analyzing well-test data of deep/ultra-deep reservoirs and tight reservoirs. Analyzing and modeling the well-test data in a computation-accurate and easy-program manner have been a challenge for these problems due to the lack of suitable solutions. This work thus presents the generalized analytical well-test solutions for vertically fractured wells in infinite and bounded commingled reservoirs with computation accuracy and functional simplicity. These solutions are derived based on the early-time approximate solution of the infinite/finite-conductivity fracture model, Laplace and Fourier cosine transformation, pressure superposition principle, and Duhamel principle. Subsequently, model validation is carried out by comparing the pressure and derivative results with those of commercial saphir software. The results show that the average absolute percent deviation between the presented analytical solutions and saphir for three kinds of outer boundaries is ∼2% for pressure results and ∼4% for pressure derivative results. Finally, a field case in Xinjiang oilfield is interpreted, indicating that the proposed analytical well-test solutions are feasible to interpret the parameters of commingled reservoirs.

Viscosity of imidazolium ionic liquids and mixtures of ILs from entropy scaling using the PC-SAFT and ePC-SAFT equations of state

Entropy scaling is a promising method for the development of engineering models for transport properties of ionic liquids (ILs). Here we present two entropy scaling models for the viscosity of ionic liquids and their mixtures at various temperatures and pressures: 1) a molecular-based approach treating each IL as one pure species and using the PC-SAFT equation of state (EoS); and 2) treating each ion as a separate species and using the ePC-SAFT EoS. Both approaches were correlated to viscosity data of 12 ionic liquids from the imidazolium cation class and three different anions comprising 4233 data points with overall average absolute deviations (%AARDs) of less than 8 % over a wide range of temperature and pressures. The ion-based approach was better able to correlate IL viscosity at low temperatures and/or high pressure (to 4011 bar) than the molecular approach. The functional entropy-scaling coefficients for both models were regressed as linear functions of molecular weight, which allowed good prediction of another nine ionic liquids not in the training set. Interestingly, both approaches well predict the viscosity of binary mixtures of imidazolium ionic liquids. In addition, the effect of various reference viscosities and comparison with the Peng-Robinson EoS was performed.

Numerical investigation and data-driven prediction of flow boiling heat transfer in manifold microchannels with slopes

The manifold microchannels (MMC) with high heat transfer coefficient and low pressure drop has received much attention. Flow boiling heat transfer inside MMC is promising for dissipating heat of high-power devices. In this study, flow boiling heat transfer inside MMC is further numerically studied with slopes added in the upstream and downstream corners of MMC. The results show that downstream slope leads to lower local temperature and thermal resistance by removing the upstream vapor more easily, and upstream slope leads to higher heat flux corresponding to the turning point in the flowing boiling curve by restraining the local high temperature at upstream corner. The combination of the upstream and downstream slopes can reduce the thermal resistance by 5.45% and increase the heat flux related to the turning point by 14.28% compared to the MMC without slopes. A new flow boiling heat transfer correlation considering the slopes is developed with prediction accuracy of mean absolute deviation (MAD) of about 0.05%. An artificial neural network (ANN) is further built to predict the vapor saturation inside the MMC based on the outside temperature at the MMC bottom surface, which presents high accuracy of 97.03%.

MR compatible detectors assessment for a 0.35 T MR-linac commissioning

PurposeTo assess a large panel of MR compatible detectors on the full range of measurements required for a 0.35 T MR-linac commissioning by using a specific statistical method represented as a continuum of comparison with the Monte Carlo (MC) TPS calculations. This study also describes the commissioning tests and the secondary MC dose calculation validation.Material and methodsPlans were created on the Viewray TPS to generate MC reference data. Absolute dose points, PDD, profiles and output factors were extracted and compared to measurements performed with ten different detectors: PTW 31010, 31021, 31022, Markus 34045 and Exradin A28 MR ionization chambers, SN Edge shielded diode, PTW 60019 microdiamond, PTW 60023 unshielded diode, EBT3 radiochromic films and LiF µcubes. Three commissioning steps consisted in comparison between calculated and measured dose: the beam model validation, the output calibration verification in four different phantoms and the commissioning tests recommended by the IAEA-TECDOC-1583.Main resultsThe symmetry for the high resolution detectors was higher than the TPS data of about 1%. The angular responses of the PTW 60023 and the SN Edge were − 6.6 and − 11.9% compared to the PTW 31010 at 60°. The X/Y-left and the Y-right penumbras measured by the high resolution detectors were in good agreement with the TPS values except for the PTW 60023 for large field sizes. For the 0.84 × 0.83 cm2 field size, the mean deviation to the TPS of the uncorrected OF was − 1.7 ± 1.6% against − 4.0 ± 0.6% for the corrected OF whereas we found − 4.8 ± 0.8% for passive dosimeters. The mean absolute dose deviations to the TPS in different phantoms were 0 ± 0.4%, − 1.2 ± 0.6% and 0.5 ± 1.1% for the PTW 31010, PTW 31021 and Exradin A28 MR respectively.ConclusionsThe magnetic field effects on the measurements are considerably reduced at low magnetic field. The PTW 31010 ionization chamber can be used with confidence in different phantoms for commissioning and QA tests requiring absolute dose verifications. For relative measurements, the PTW 60019 presented the best agreement for the full range of field size. For the profile assessment, shielded diodes had a behaviour similar to the PTW 60019 and 60023 while the ionization chambers were the most suitable detectors for the symmetry. The output correction factors published by the IAEA TRS 483 seem to be applicable at low magnetic field pending the publication of new MR specific values.

Thermal−hydraulic characteristics of R32 and R410A condensation in plate heat exchangers with 1 mm chevron depth

Compact brazed plate heat exchangers (BPHEs) with low chevron depths are widely used in new heat pumps and air-conditioners for reducing inventory and improving efficiency. This study analyses the complete condensation of R410A and R32 inside BPHEs with a chevron depth of 1 mm. The effects of the mass flux and chevron patterns (V-shaped and W-shaped) on the heat transfer coefficient (HTC) and frictional pressure drop are investigated. The HTC of R32 is 30–50 % higher than that of R410A, which has a 10–20 % higher friction factor at high mass fluxes and vice versa at low mass fluxes. The HTC of R410A monotonically increases with the mass flux, whereas that of R32 does not. The W-shaped pattern results in 30 % and 15 % higher pressure drops compared with the V-shaped pattern for R410A and R32, respectively. The friction factor of R32 decreases more sharply than that of R410A as the mass flux increases, particularly at low mass fluxes. The correlations developed by Longo et al. and Zhang et al. show the best agreement with the experimental data for the HTCs of R32 and R410A, respectively, with mean absolute percentage deviations (MAPDs) of 15 %. The friction factor correlation of Zhang et al. accurately predicts the pressure drop of R32 for the W-shaped and V-shaped patterns. In addition, it accurately predicts the pressure drop of R410A for the W-shaped pattern, with an MAPD of less than 10 %, but over-estimates it by 37 % for the V-shaped pattern.

Measurements of density and viscosity of carbon dioxide-loaded and -unloaded nano-fluids: Experimental, genetic programming and physical interpretation approaches

In the present study, the density and viscosity of the CO2-loaded and –unloaded base solution and nano-fluid were experimentally measured and investigated from an intermolecular point of view. Nano-fluids are composed of nano-particles such as Al2O3 (0.1 wt.%), Silica-2-methylimidazole, zinc salt (Si-ZIF-8) (0.02 wt.%), and super activated carbon (SAC) (0.1 wt.%) dispersed in aqueous and hybrid Methyl diethanolamine context (MDEA, 40 wt.%) +Sulfolane (SFL), 30 wt.%) +H2O) Experimental measurements were carried out at the low-temperature ranges 303.15–315.15 K, atmospheric pressure, and three different CO2 loadings. The results show that nanomaterials do not have a significant effect on the density and viscosity of the unloaded suspension; however, the density and viscosity of loaded suspensions and base solvent become more by increasing CO2 concentration. In the case of CO2-loaded fluids, the comparison of the results in the presence and absence of nanoparticles shows that the density of the solution is not much different in the two cases, but the viscosity of CO2-loaded in Si-ZIF-8, SAC, and γ-Al2O3 base nano-fluids in comparison with base solvent shows an increase of 35 % in high CO2 loading, ∼0.3 mol CO2 per mol MDEA. Density and viscosity experimental data were modeled using the Genetic Programming approach. The highest values of absolute average relative deviation (AARD) and root mean square error (RMSE) parameters obtained for modeling data are 3.04 and 0.317, respectively, and the lowest value of regression coefficient (R2) is 0.995, which indicates the appropriate fitting of the results.

Modeling H2S solubility in aqueous MDEA, MEA and DEA solutions by the electrolyte SRK-CPA EOS

In this study, an electrolyte cubic plus association equation of state (e-CPA EOS) has been proposed to predict the vapor–liquid equilibrium of (H2S) in aqueous solutions of monoethanolamine (MEA), diethanolamine (DEA), and methyldiethanolamine (MDEA). This EOS is composed of repulsive forces, short-range interactions, short-range ionic interactions, association term, long-range ionic interactions, and Born term. The Soave– Redlich– Kwong EOS is used as a non-electrolyte part. A new iterative procedure based on the Jacobi method is introduced to obtain the compositions of all species in the liquid phase. The genetic algorithm (GA) is used to find the optimal values of binary interaction coefficients between molecules and ions. Compared with experimental data, the e-CPA EOS model successfully describes the vapor–liquid equilibrium of H2S in the aqueous solutions of MEA, DEA, and MDEA. The average absolute deviations for 313 data of MEA-H2S-H2O, 156 data of DEA-H2S-H2O, and 145 data of MDEA-H2S-H2O systems were obtained as 8.20 %, 8.04 %, and 9.41 %, respectively. In addition, for some similar cases, the capability of the e-CPA EOS was compared with the Clegg–Pitzer, N-Wilson-NRF, e-NRTL, and e-CTS models. The results indicated that the e-CPA EOS has less error than the four other models.

Fundamental research on the condensation heat transfer of the hydrocarbon-mixture energy in a spiral tube described by a universal model using flow pattern based and general modes

The transportation of liquefied natural gas (LNG) is a crucial aspect of global energy application and security. However, there are still some challenges in liquefaction. This study establishes systematic experiments to investigate the condensation two-phase flow and heat transfer process of hydrocarbon-mixture energy (methane/ethane/propane/isobutane) in a spiral tube. The main influencing parameters, including mass flux (200–560 kg/(m2·s)), operating pressure (2–4 MPa), and vapor quality (0–1) on the heat transfer intensity and frictional pressure drop were analyzed. Different two-phase flow patterns were observed and categorized them as bubble flow, intermittent flow, stratified-wavy flow, and annular flow. For bubble flow and intermittent flow, vapor quality is found to remarkably affect the heat transfer; regarding stratified-wavy flow and annular flow, gravitational force and inertia force are found to prominently influence the heat transfer, respectively. Additionally, a newly universal model includes split flow patterns mode (SFPM) and the general mode (GM) has been proposed, which is with high-precision for describing the condensation heat transfer coefficients of different flow patterns. Furthermore, the analytical results show the SFPM's root mean square error and mean absolute relative deviation are 97.35 W/(m2·K) and 0.036.

Multiple conformity tests to assess deviations from the Newcomb-Benford Law (NBL): A replication of Koch and Okamura (2020)

In this paper, we critically reevaluate Koch and Okamura’s (2020) conclusions on the conformity of Chinese COVID-19 data with Benford’s Law. Building on Figueiredo et al. (2022), we adopt a framework that combines multiple tests, including Chi-square, Kolmogorov-Smirnov, Euclidean Distance, Mean Absolute Deviation, Distortion Factor, and Mantissa Distribution. The primary rationale behind employing multiple tests is to enhance the robustness of our inference. The main finding of the study indicates that COVID-19 infections in China do not adhere to the distribution expected under Benford’s Law, nor does it align with the figures observed in the U.S. and Italy. The usefulness of deviations from Benford’s Law in detecting misreported or fraudulent data remains controversial. However, addressing this question requires a more careful statistical analysis than what is presented in the Koch and Okamura (2020) paper. By employing a combination of several tests using fully transparent procedures, we establish a more reliable approach to evaluating conformity to the Newcomb-Benford Law in applied research.

Accuracy Analysis of 3D Bone Fracture Models: Effects of Computed Tomography (CT) Imaging and Image Segmentation

The introduction of three-dimensional (3D) printed anatomical models has garnered interest in pre-operative planning, especially in orthopedic and trauma surgery. Identifying potential error sources and quantifying their effect on the model dimensional accuracy are crucial for the applicability and reliability of such models. In this study, twenty radii were extracted from anatomic forearm specimens and subjected to osteotomy to simulate a defined fracture of the distal radius (Colles’ fracture). Various factors, including two different computed tomography (CT) technologies (energy-integrating detector (EID) and photon-counting detector (PCD)), four different CT scanners, two scan protocols (i.e., routine and high dosage), two different scan orientations, as well as two segmentation algorithms were considered to determine their effect on 3D model accuracy. Ground truth was established using 3D reconstructions of surface scans of the physical specimens. Results indicated that all investigated variables significantly impacted the 3D model accuracy (p < 0.001). However, the mean absolute deviation fell within the range of 0.03 ± 0.20 to 0.32 ± 0.23 mm, well below the 0.5 mm threshold necessary for pre-operative planning. Intra- and inter-operator variability demonstrated fair to excellent agreement for 3D model accuracy, with an intra-class correlation (ICC) of 0.43 to 0.92. This systematic investigation displayed dimensional deviations in the magnitude of sub-voxel imaging resolution for all variables. Major pitfalls included missed or overestimated bone regions during the segmentation process, necessitating additional manual editing of 3D models. In conclusion, this study demonstrates that 3D bone fracture models can be obtained with clinical routine scanners and scan protocols, utilizing a simple global segmentation threshold, thereby providing an accurate and reliable tool for pre-operative planning.