Accuracy Of Calculations Research Articles

Design optimization of vibration amplitude reduction based on virtual prototype and machine learning

The traditional design optimization of vibration amplitude reduction mainly has the disadvantages of low modeling and prediction accuracy as well as low optimization efficiency. Therefore, this paper presents a design optimization method for vibration amplitude reduction based on virtual prototyping and machine learning, which combines the high accuracy of numerical calculations with the efficiency of machine learning, overcoming the shortcomings of traditional methods. Firstly, sample points are collected through the design of experiments and virtual prototype simulation. Then, based on the sampled data, a prediction model for the relationship between the design parameters and the amplitude of the product is established using Genetic Algorithm-Support Vector Regression (GA-SVR). On the basis of the GA-SVR prediction model, a multi-objective optimization model of product is established, and Multiple Objectives Particle Swarm Optimization -entropy weight- Technique for Order Preference by Similarity to Ideal Solution (MOPSO-entropy weight-TOPSIS) is used to solve for the optimal design parameters. Finally, the washing machine suspension system is used as an example to verify the effectiveness of the model. The results show that, compared with the original design scheme, the design scheme obtained by the model can reduce the amplitude of the washing machine suspension system by 12.68%, and reduce the total weight of the counterweight by 7.35%. This method is conducive to the intelligent and efficient design optimization of vibration amplitude reduction, and is of great significance to product life cycle design.

Computational Search for Compounds with the Highest Melting Temperature in the Ternary HfC‐Based Ceramics

The melting temperature (Tm) determines the service temperature of ultrahigh‐temperature ceramics (UHTCs), so it is of great significance to search for compound with the highest Tm in UHTCs. In present work, the formula to calculate the Tm of UHTCs with the rock salt structure is modified first: Tm = f(c)EVm/(100kB), f(c) = 0.6412*c + 0.9947, which considers the influence of vacancy concentration (c) on the Tm. The calculated Tms are in good agreement with the experimental values of UHTCs, and the calculation accuracy exceeds 91%. The Tm could be obtained by only calculating the elastic modulus and volume per atom. Using this formula, the Tms of ternary HfC‐based UHTCs with the rock salt structure are predicted, and a new compound with the highest melting temperature, HfTa2C3, is found. Finally, the empirical relationship between the Tm and shear modulus or Vickers hardness of UHTCs with the rock salt structure is also investigated.

INFLUENCE OF ANOMALOUS VALUES ON THE ACCURACY OF FORECASTING LOSSES IN DISTRIBUTION NETWORKS

The relevance of the study is determined by modern trends in managing the operation modes of distribution electrical networks using Smart Grid technologies, as well as the need to reduce the costs incurred by distribution system operators for purchasing electricity. Accurate load forecasting results at network nodes for different forecasting horizons are crucial for this purpose. Sudden changes in network topology can increase the errors in loss forecasts as a single time series, negatively impacting network management efficiency and increasing the costs of electricity procurement to cover losses. The study proposes using forecasting methods based on artificial neural networks for the calculation and prediction of electricity losses, along with a comparison of these methods. The calculations were performed using data from one of Ukraine's distribution system operators, and the test electrical network was adapted based on the CIGRE scheme for modeling electricity losses. Since the nodal load data contained gaps and anomalies, a two-step data analysis algorithm was employed using the DBSCAN clustering method for detection and correction. As a result of loss calculations based on cleaned data, the error was reduced threefold compared to calculations based on load factors. Applying data analysis methods and forecasting methods based on artificial neural networks significantly improves the accuracy of loss calculations and minimizes errors.

Using the Rstudio Environment in Researching Problems in Mathematics, Physics and Economics

The purpose of the article is to study the possibilities of using the RStudio environment to solve problems in mathematics, physics and economics. In particular, the emphasis is on the integration of statistical analysis tools, the use of a set of mathematical functions and operations that ensure efficiency and accuracy in calculations. Research methods. An analysis of scientific papers, methodological recommendations and examples of RStudio application in various fields of knowledge related to mathematics, physics and economics was conducted, the functionality of the RStudio environment was tested on practical tasks, and the results obtained using RStudio were compared with the results obtained using other methods to assess the effectiveness of the environment. The scientific novelty lies in the use of R functionality, which allows developing and implementing new methods and approaches to data analysis and solving scientific problems. Conclusions. The RStudio environment demonstrates its effectiveness for solving problems in mathematics, physics and economics due to a wide range of functions for numerical modeling, statistical analysis and data visualization. Using RStudio allows you to significantly simplify the process of solving complex mathematical problems, in particular, it contributes to the integration of mathematical methods in the study of physical phenomena and economic processes, which allows you to form new approaches to data analysis and modeling. RStudio can be used as an effective tool in education, contributing to the formation of analytical thinking and programming skills in students engaged in mathematical, physical and economic problems. In particular, the use of the Tukey criterion and the functions of the RStudio environment for comparing data in large arrays, such as the results of experiments or measurements. The results of the study confirm the feasibility of implementing RStudio in the practice of teaching and scientific research, and also indicate the need for further research into the capabilities of the environment in solving problems in scientific fields.

Optimal Dielectric Boundary for Binding Free Energy Estimates in the Implicit Solvent.

Accuracy of binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a multidimensional optimization pipeline is used to find optimal atomic radii, specifically for binding calculations in the implicit solvent. To reduce overfitting, the optimization target includes separate, weighted contributions from both binding and hydration free energies. The resulting five-parameter radii set, OPT_BIND5D, is evaluated against experiment for binding free energies of 20 host-guest (H-G) systems, unrelated to the types of structures used in the training. The resulting accuracy for this H-G test set (root mean square error of 2.03 kcal/mol, mean signed error of -0.13 kcal/mol, mean absolute error of 1.68 kcal/mol, and Pearson's correlation of r = 0.79 with the experimental values) is on par with what can be expected from the fixed charge explicit solvent models. Best agreement with the experiment is achieved when the implicit salt concentration is set equal or close to the experimental conditions.

Dual virtual non-contrast imaging: a Bayesian quantitative approach to determine radiotherapy quantities from contrast-enhanced DECT images

Objective.Contrast agents in computed tomography (CT) scans can compromise the accuracy of dose calculations in radiation therapy planning, especially for particle therapy. This often requires an additional non-contrast CT scan, increasing radiation exposure and introducing potential registration errors. Our goal is to resolve these issues by accurately estimating radiotherapy parameters from dual virtual non-contrast (dual-VNC) images generated by contrast-enhanced dual-energy CT (DECT) scans, while accounting for noise and variability in tissue composition.Approach.A new Bayesian model is introduced to estimate dual-VNC Hounsfield units from contrast-enhanced DECT data. The model defines a prior distribution that describes tissue variations in terms of elemental compositions and mass densities. Multiple reference tissues are used to estimate variations across human tissues. A likelihood distribution is also defined to model the noise contained in CT data. The model is thoroughly validated in a simulated environment including 12 virtual patients under low and high iodine uptake scenarios, while incorporating noise and beam hardening effects. The eigentissue decomposition technique is used to derive elemental compositions and parameters critical for radiotherapy from the dual-VNC images, such as electron density (ρe), particle stopping power (SPR), and photon energy absorption coefficient (EAC).Main results.The proposed method yields accurate voxelwise estimations forρe, SPR, and EAC, with root mean square errors of 3.09%, 3.14%, and 1.34% for highly-enhanced tissues, compared to 5.93%, 6.39%, and 17.11% when the presence of contrast agent is ignored. It also demonstrates robustness to systematic shifts in tissue composition and bandwidth variations in the prior distribution, resulting in overall uncertainties down to 1.13%, 1.33%, and 0.86% forρe, SPR, and EAC in soft tissues; 1.17%, 1.32%, and 1.34% in enhanced soft tissues; and 4.34%, 4.00%, and 2.50% in bones.Significance.The proposed method accurately derives radiotherapy parameters from contrast-enhanced DECT data and demonstrates robustness against systematic errors in reference data, highlighting its potential for clinical use.

Balance Strategy Prediction Model Based on Power Load Data Mining

To meet the purpose of a multi-objective solution, a scheme is introduced into the optimal strategy-intensive training process. It supports a multi-task-driven computing model and has good reasoning accuracy, which makes the hydropower management system have efficient collaborative strategy prediction ability. Aiming at the trade-off problem between the cost-benefit and energy load of the hydropower control system, based on the neural network model to improve the stacked sparse denoising auto encoder second-order cone programming (SSDAE-SOCP) algorithm, a low-level backtracking depth uncertainty optimization scheduling model is proposed to obtain the optimal scheduling strategy. It intends to improve the poor accuracy of the global optimal results caused by the lack of priority sampling in the traditional strategy. The feature space without boundary conditions is set to solve the optimal solution for global/local organizations. Further, the flexibility is transferred according to the single-core function of task decomposition for improving the calculation accuracy of the whole solution data and the dynamic double-mutation balance. Simulation results have shown that the proposed algorithm enhances the training quality and performance by 9.5% and 15.2% compared with the traditional MOPSO and MOIMPA algorithms. Gradient descent and convergence speed, as well as the stability and security of intensive training, are better than the comparison methods. The training features verify the stability of the balanced prediction model, showing its importance in enhancing the anti-noise ability of the matrix. The research results can further enhance the multiple dispatching and command capabilities of the hydropower projects, providing technical support for the sustainable development of maximizing the benefits for the systems.

Facilitating comprehensive child health monitoring within REDCap - an open-source code for real-time Z-score assessments

BackgroundMonitoring of somatic development through the assessment of anthropometric variables such as weight, height, and BMI is vital for evaluating the physical development and nutritional status of children. This approach aids in the early identification of somatic developmental disorders, enabling timely medical interventions. It traditionally relies on Z-scores, which compare anthropometric variables with reference standards. In addition to somatic development monitoring, the early detection and management of pediatric and adolescent hypertension are crucial due to potential long-term health risks. However, manual calculations of Z-scores are time-consuming and error-prone, impeding timely interventions for at-risk children. This article introduces an innovative open-code solution for real-time Z-score assessments directly within the electronic data capture platform, Research Electronic Data Capture, (REDCap™), aiming to streamline the monitoring of somatic development in children.MethodsLeveraging the World Health Organization (WHO) growth standards and National Health and Nutrition Examination Survey (NHANES) references, our approach integrates Z-score computations directly into REDCap, providing a secure and user-friendly environment for healthcare professionals and researchers. We employed Bland-Altman analyses to compare our method with established calculators (Knirps and Growth XP™) using synthetic data values for all variables.ResultsBland-Altman plots demonstrated strong agreement between our REDCap calculations and the Knirps and Growth XP systems. Z-scores for height, BMI, and blood pressure consistently aligned, affirming the accuracy of our approach across the measurement range.ConclusionThe integration with REDCap streamlines data collection and analysis, eliminating the need for separate software and data exports. Moreover, our solution uses the World Health Organization (WHO) growth standards and National Health and Nutrition Examination Survey (NHANES) references. This not only ensures calculation accuracy but also enhances its suitability for diverse research contexts. The Bland-Altman analyses establish the reliability of our method, contributing to a more effective approach to child growth and blood pressure monitoring.

Biomimetic Active Stereo Camera System with Variable FOV

Inspired by the biological eye movements of fish such as pipefish and sandlances, this paper presents a novel dynamic calibration method specifically for active stereo vision systems to address the challenges of active cameras with varying fields of view (FOVs). By integrating static calibration based on camera rotation angles with dynamic updates of extrinsic parameters, the method leverages relative pose adjustments between the rotation axis and cameras to update extrinsic parameters continuously in real-time. It facilitates epipolar rectification as the FOV changes, and enables precise disparity computation and accurate depth information acquisition. Based on the dynamic calibration method, we develop a two-DOF bionic active camera system including two cameras driven by motors to mimic the movement of biological eyes; this compact system has a large range of visual data. Experimental results show that the calibration method is effective, and achieves high accuracy in extrinsic parameter calculations during FOV adjustments.

Few-time-points time-integrated activity coefficients calculation using non-linear mixed-effects modeling: Proof of concept for [111In]In-DOTA-TATE in kidneys

PurposeThe purpose of this study is to investigate the accuracy of few-time-points (FTP) time-integrated activity coefficients (TIACs) in peptide-receptor radionuclide therapy (PRRT) using non-linear mixed-effects (NLME) modeling. MethodsBiokinetic data of [111In]In-DOTA-TATE in kidneys at T-1 = (2.9 ± 0.6) h, T-2 = (4.6 ± 0.4) h, T-3 = (22.8 ± 1.6) h, T-4 = (46.7 ± 1.7) h, and T-5 = (70.9 ± 1.0) h after injection were obtained from eight patients using planar imaging. The Sum-Of-Exponentials (SOE) function with four parameters was used, which was selected as the best model for the renal biokinetic data of [111In]In-DOTA-TATE. The parameters of the SOE function were fitted to the all-time-point data in the NLME framework to derive reference (rTIACs). FTP fits, which consist of all combinations of time points, are done to calculate the estimated TIACs (eTIACs). The accuracy of the FTP-NLME TIACs calculations was assessed by calculating the relative deviations (RDs) and relative root-mean-square errors (RMSEs) between the eTIACs and rTIACs. ResultsThe lowest (mean ± SD) of RDs for the single-, two-, three-, four-time point FTPs were (0 ± 8) % (T-4), (1 ± 6) % (T-3 and T-4), (3 ± 5) % (T-2, T-3 and T-4), and (0 ± 2) % (T-2, T-3, T-4, and T-5), respectively. The lowest RMSEs for the one-, two-, three-, and four-time point FTPs were 8 % (T-4), 6 % (T-3 and T-4), 5 % (T-2, T-3 and T-4), and 2 % (T-2, T-3, T-4, and T-5), respectively. ConclusionOur results showed that FTP-NLME in an example of [111In]In-DOTA-TATE could lead to a high accuracy of eTIAC across various time points, when incorporating time point T-4 = (46.7 ± 1.7) h.

Interface Equilibrator: Numerical solutions to capillarity and wetting equilibrium and quasi-equilibrium problems

This paper introduces Interface Equilibrator (IE), a new graphical-user-interface software for simulating the equilibrium shape of fluid–fluid interfaces in a wide range of wetting and capillarity problems. IE provides an easy-to-use three-dimensional computer-aided-design environment to define the problem's geometry (i.e., the solid surfaces and the fluids' volumes), by simply loading opportune triangular meshes, and chemistry, by selecting the value of the relevant experimental parameters (e.g., Young's contact angle). No other input is required. Then, IE calculates the fluid–fluid interface's equilibrium shape using a novel numerical methodology, presented in this paper, that consists in an energy-minimization Monte Carlo simulation alongside other built-in automated methods to, e.g., refine the fluid–fluid interface mesh according to its local curvature and polish it. The energy-minimization algorithm is based on a numerical approach introduced a few years ago [Soligno et al., “The equilibrium shape of fluid-fluid interfaces: Derivation and a new numerical method for Young's and Young–Laplace equations,” J. Chem. Phys. 141, 244702 (2014)] that is generalized here to handle unconstructed meshes with any topology and to include also new types of forces (e.g., due to a rotating system or to a line tension). In addition, several illustrative and scientifically interesting novel results are presented in this paper to demonstrate IE's versatility and capability of addressing a broad spectrum of research problems, relevant for many technological applications, such as microfluidics, fluid management at various length scales, printing, colloids, soldering for chip manufacture, etc. Finally, the paper reports numerous validation tests, where known analytic or numerical solutions are compared with IE's results to verify the correctness and accuracy of IE's calculations.

Analysis of Volume Calculations for Clay Mining Excavation Plans Using The Surface to Surface Method

Abstract Calculating the volume of excavation is generally a large and complex problem for a project in an industry. This is because the accuracy of the volume calculation affects the project budget for the project provider. Calculating the excavation volume is important aspect of mine planning since it serves as a guide for hitting production goals in terms of both profit and technical success. The method commonly used in calculating excavation volume is surface to surface. To overcome this problem, a method or technique is needed to calculate the excavation volume that can provide accurate calculation results. the surface-to-surface approach will be used to optimize volume calculations. Additionally, the accuracy of excavation volume calculations based on ASTM (American Society for Testing and Material) tolerances. Analysis is carried out by calculating the smallest percentage value. In this way, excavation volume results will be obtained that are close to the reference value based on ASTM tolerances. The aim of this research is to determine the optimal results of surface-to-surface method volume calculations from the most accurate contour intervals. The volume calculation results that are closest to the reference of 999,922.91 tonnes have a percentage value of 0.01% with a volume difference to the forecast study of 77.09 tonnes. To obtain accurate volume calculations, a contour interval of 1 meter is better than several other contour intervals. This research can become new alternative in volume calculations to support SDGs activities, especially in the resource use efficiency to achieve a sustainable industrialisation.

Discussion on the Strength Calculation Method for Connecting Bolts between Head Cover and Stay Ring of Pumped Storage Power Station in Unit Runaway

Abstract The units of pumped storage power plants are frequently started and operate under complex modes. There have been many fracture accidents of connecting bolts between head cover and stay ring in China and abroad, resulting in flooding of the plants, affecting the safety of personnel and property. The unit runaway mode is one of the controlling modes in the calculation of head cover bolts. Due to the unstable flow in this mode, it is difficult to accurately obtain the head cover load, which affects the accuracy of bolt strength calculation. This article explores the relevant calculation methods through an example of the strength review calculation of the head cover and stay ring connecting bolts of a pumped storage power station. A method was proposed to perform CFD steady state calculations on several characteristic points of the runaway mode using one-dimensional transient calculation dates as boundary conditions, and then correct the result of head cover load calculation. The method in this article takes into account both the calculation time and accuracy, and has certain engineering practicality, which can be used as a reference for the industry.

An analytical model for estimating aerodynamic damping of wind turbines in the shutdown state

As wind farms are constantly being constructed, the risk of tower failure for wind turbines increases significantly under strong winds. Compared with the extensively concerned wind-induced behaviors during the operating state, those ones during the shutdown state attract little attention but may lead to serious problems of damages or even collapse. To clearly grasp the aero-structure interaction in the shutdown state, this paper develops an analytical model for estimating aerodynamic damping of wind turbines. In this method, an analytical expression of aerodynamic damping coupling matrix is derived via the combination of multibody dynamics and first-order Taylor expansion. This matrix is further quantified as the ratio of modal aerodynamic damping with the aid of state-space equation and complex eigenvalue analysis. This treatment can facilitate the straightforward application of efficient calculation methods, such as frequency domain analysis and uncoupled analysis. More importantly, the developed model is able to simultaneously consider multiple realistic factors, such as blade flexibility, tower top rotation, yaw error, wind shear, and pitch angle. This model may have the high calculation efficiency and accuracy, as well as strong applicability for estimating the aerodynamic damping. Numerical examples based on a typical 5 MW wind turbine are employed to validate the effectiveness of the developed model. Experimental analyses demonstrate that this model outperforms the existing formula and presents a high consistency with OpenFAST in the estimation of aerodynamic damping. Meanwhile, the influence of multiple realistic factors is quantitatively analyzed, which even makes the estimation error exceed 70%.

Optimization of the blow-off efficiency of kerosene adhering to the inner wall of chambers with complex structures

The research objective of this paper is to obtain an efficient space engine chamber wall adherent kerosene blow-off solution, the research methodology uses computational fluid dynamics method and experiments are conducted to verify the numerical calculation accuracy. The effect of gas pressure, gas temperature, ambient temperature, and gas type on the blow-off efficiency was obtained, in which increasing the pressure and temperature of the blow-off gas increased the treatment efficiency by 14.16% and 9.85%, respectively, and the blow-off efficiency was significantly improved. Increasing the operating ambient temperature and changing the type of purge gas increased the treatment efficiency by 4.76% and 5.88%, respectively. Finally, the removal scheme was optimized by increasing the blow-off gas pressure and temperature, and the treatment efficiency of the optimized scheme was improved by 37.5% compared to the original scheme. The findings of this paper provide important guidance for the efficient blow-off off of kerosene adhering to the inner wall of complex structural cavities.

The effect of stress history on the critical shear stress of bedload transport in gravel-bed streams

Critical shear stress is a pivotal parameter that describes the bedload transport and riverbed stability. Recent studies indicate that different interevent periods in gravel-bed streams can lead to a realignment of riverbed structures, thereby disrupting the bedload transport. In this research, a series of flume experiments were conducted to study the effect of stress history on the critical shear stress of bedload transport in a gravel-bed channel. The results reveal the temporal-spatial variation patterns of critical shear stress. The critical shear stress has an increased trend after interevent with low flow and long duration. Conversely, a decreasing trend in critical shear stress observed after low flow and short duration conditions. After interevent durations with medium to high flow condition, no matter the duration, critical shear stress shows a decreasing trend. A significant positive correlation between the flow magnitude of the interevent period and the critical shear stress of the subsequent flood period is shown. Besides, critical shear stress has a significant spatial variation pattern on the gravel bed due to the presence of microtopography as revealed by the correlation analysis results among the spatial density of microtopography with different protrusion heights, riverbed statistical parameters, and critical shear stress. Moreover, the stress history, by adjusting the position of fine particles on the riverbed surface through different magnitudes and durations of the interevent period, affects the spatial density of the microtopography, thereby influencing the critical shear stress. The conventional bedload transport equations are deficit by only considering the riverbed slope or standard deviation of bed surface elevation. An optimization of the critical shear stress calculation method, based on parameter correlation analysis, is proposed, potentially enhancing the calculation accuracy of bedload transport in mountainous rivers.

Thermal performance analysis of a deep coaxial borehole heat exchanger with a horizontal well based on a novel semi-analytical model

This study introduces a novel semi-analytical model for deep coaxial borehole heat exchanger with a horizontal well(H-DBHE), which incorporates the effects of formation stratification and transient heat conduction within the pipe walls. Initially, the model's validity was checked through a comparison with the results derived from the published numerical models. This semi-analytical approach significantly reduces computational time while ensuring calculation accuracy. Subsequently, the study quantitatively examined the influence of the operation parameters, structural characteristics and the thermal capacity within the borehole on the dynamic heat extraction of H-DBHE. The numerical results demonstrate that the thermal capacity within the borehole significantly influences the heat extraction performance during intermittent operations of H-DBHE. Based on this finding, the study further investigated the effects of intermittent operation and heating time on the heat recovery performance of the system. Additionally, the study compared the long-term operational performance and thermal attenuation between H-DBHE and deep coaxial borehole heat exchanger (DBHE) over a ten-year simulation period. This research introduces a novel semi-analytical model for H-DBHE, which is of significant theoretical guidance for accurately predicting the heat extraction performance of H-DBHE and scientifically designing geothermal utilization systems.

Impact of uncertainty quantification through conformal prediction on volume assessment from deep learning-based MRI prostate segmentation

ObjectivesTo estimate the uncertainty of a deep learning (DL)-based prostate segmentation algorithm through conformal prediction (CP) and to assess its effect on the calculation of the prostate volume (PV) in patients at risk of prostate cancer (PC).MethodsThree-hundred seventy-seven multi-center 3-Tesla axial T2-weighted exams from biopsied males (66.64 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 7.47 years) at risk of PC were retrospectively included in the study. Assessment of PV based on PI-RADS 2.1 ellipsoid formula (PVref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{ref}}$$\\end{document}) was available for included patients. Prostate segmentations were obtained from a DL model and used to calculate the PV (PVDL\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{DL}}$$\\end{document}). CP was applied at a confidence level of 85% to flag unreliable pixel segmentations of the DL model. Subsequently, the PV (PVCP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{CP}}$$\\end{document}) was calculated when disregarding uncertain pixel segmentations. Agreement between PVDL\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{DL}}$$\\end{document} and PVCP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{CP}}$$\\end{document} was evaluated against the reference standard PVref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{ref}}$$\\end{document}. Intraclass correlation coefficient (ICC) and Bland–Altman plots were used to assess the agreement. The relative volume difference (RVD) was used to evaluate the PV calculation accuracy, and the Wilcoxon Signed-Rank Test was used to assess statistical differences. A p-value < 0.05 was considered statistically significant.ResultsConformal prediction significantly reduced RVD when compared to the DL algorithm (RVD = − 2.81 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 8.85 and RVD = −8.01 ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 11.50). PVCP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{CP}}$$\\end{document} showed a significantly larger agreement than PVDL\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{DL}}$$\\end{document} when using the reference standard PVref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{ref}}$$\\end{document} (mean difference (95% limits of agreement) PVCP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{CP}}$$\\end{document}: 1.27 mL (− 13.64; 16.17 mL) PVDL\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{DL}}$$\\end{document}: 6.07 mL (− 14.29; 26.42 mL)), with an excellent ICC (PVCP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\rm{PV}}}}_{{CP}}$$\\end{document}: 0.97 (95% CI: 0.97 to 0.98)).ConclusionUncertainty quantification through CP increases the accuracy and reliability of DL-based PV assessment in patients at risk of PC.Critical relevance statementConformal prediction can flag uncertain pixel predictions of DL-based prostate MRI segmentation at a desired confidence level, increasing the reliability and safety of prostate volume assessment in patients at risk of prostate cancer.Key PointsConformal prediction can flag uncertain pixel predictions of prostate segmentations at a user-defined confidence level.Deep learning with conformal prediction shows high accuracy in prostate volumetric assessment.Agreement between automatic and ellipsoid-derived volume was significantly larger with conformal prediction.Graphical

Thermal conductivity predictions in monolayer MoSi2N4: Integrating neural network potentials with phonon scattering analysis

Two-dimensional (2D) materials, known for their exceptional thermal conductivity and mechanical flexibility, have emerged as promising candidates for thermal management applications. Recently, increasing attention has been given to investigating the lattice thermal conductivity of these materials. While traditional methods combining density functional theory (DFT) with the Boltzmann transport equation (BTE) can produce accurate results, these approaches are computationally expensive and demand substantial resources. To address this challenge, we employed machine learning to successfully model the interatomic potential of monolayer MoSi2N4. This neural network potential (NNP), combined with BTE, facilitated the theoretical calculation of MoSi2N4′s thermal conductivity. Using NNP, we efficiently and accurately calculated the lattice thermal conductivity of MoSi2N4, highlighting the importance of selecting an appropriate interaction cutoff distance to ensure calculation accuracy. Furthermore, using this NNP, we investigated how four-phonon scattering influences the heat conduction properties of MoSi2N4, thereby strengthening our comprehension of phonon scattering dynamics. This study not only optimized computational efficiency but also provided fresh perspectives on the heat transfer mechanisms in complex 2D materials.

Hybrid metaheuristic algorithm methods and econometric models in prediction of dogecoin price

Purpose This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models. Design/methodology/approach An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between crypto-price and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision. Findings The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models. Originality/value The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies’ prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.