Algorithm For Accurate Computation Research Articles

P-322 Development of a CD138-based histopathological scoring system to diagnose chronic endometritis

Abstract Study question Does a standardized histopathological classification system lead to a robust interobserver agreement to diagnose chronic endometritis (CE) and can an accurate computation algorithm be developed? Summary answer The proposed scoring system has a particularly good to excellent interobserver agreement among pathologists. A machine-learning algorithm outperformed pathologists in the clinically most relevant 2-tier-system. What is known already Plasma cell (PC) presence in the stromal endometrium has been proposed as a marker for CE, yet no standardized classification system that considers the amount or distribution of PCs exists. Also, interobserver agreement has been insufficiently reported in the previous publications on the topic. Without a robust histopathological score, the potential clinical impact of CE on ART treatment outcomes cannot be investigated. This issue is strongly reflected in the heterogeneity of the current diagnostic and therapeutic approaches, specifically for patients experiencing repeated implantation failure (RIF) or recurrent pregnancy loss (RPL). Study design, size, duration A 5-tier classification system based on CD138-positive PCs (Roche B-A38/automated Ventana system) was established: class0 (no PCs), classIPC (isolated PCs), class1 (³1 cluster of 5-19 PCs/0.25mm2), class2 (³1 cluster of 20-49 PCs/0.25mm2), class3 (³1 cluster of 50 or more PCs/0.25mm2). A test set of 78 endometrial biopsies (collected over two years) was selected by a certified pathologist who sorted >1000 biopsies to ensure the full spectrum of PC counts/distribution was accounted for. Participants/materials, setting, methods The 3DHistech-platform digitized the slides (pixel size 0.2738x0.2738mm) and a scoring session was set up in the Pathomation software suite. Six pathologists scored independently after training. Overall agreement was expressed by Fleiss’ Kappa values. Pairwise interobserver agreement was calculated using Cohen’s Kappa statistics. A supervised machine-learning algorithm was developed in QuPath(V0.4.2) and considered a seventh observer in the interobserver agreement calculations. Main results and the role of chance The CD138-based semiquantitative scoring system showed particularly good to excellent interobserver agreement among all observers. Overall agreement values were 0.722 for the 5-tier system and 0.858 for the 2-tier system (0+IPC versus 1 + 2+3, which was considered the clinically most relevant classification, i.e. CE absent versus diagnosed). Pairwise interobserver agreement was 0.871-0.951. Intra-class correlations varied from 0.727 to 0.839 in the 5-tier system and 0.893 to 0.965 in the 2-tier system. All findings were statistically significant (p < 0.001). The supervised machine-learning algorithm was able to compete with human pathologist observers and outperformed them in the 2-tier system. The proposed classification system provides a framework for assessing the presence and severity of endometrial PC infiltration and can guide further research. Limitations, reasons for caution Inconsistent staining intensity, non-specific epithelial staining and background interference are common problems that might limit interlaboratory use of the algorithm. Furthermore, the algorithm has not yet been associated to patient/clinical parameters. Accurate analysis of an endometrial biopsy might still be insufficient to provide a formal diagnosis of CE. Wider implications of the findings To unravel the association between PCs in endometrial biopsies (as a proxy for the diagnosis of CE) and the reproductive outcomes following ART, a standardized methodology is of crucial importance for future studies. Higher accuracy, efficieny and automation are advantages when using computer-aided diagnostics for CE. Trial registration number BUN 143202042810

Mortar Method for 2D Elastic Bounded Contact Problems

Abstract This paper presents a contribution to the field of numerical solutions for contact problems, which pose significant challenges in engineering and simulations. Specifically, we address the intricate task of connecting bodies that have been discretized using non-conforming and non-overlapping meshes. Our primary focus lies in investigating the efficacy of the mortar method with a segment-to-segment approach. In this context, we provide a concise overview of the underlying theoretical framework and present our implementation in the MATLAB programming environment. To ascertain the reliability and accuracy of our proposed methodology, we conduct a rigorous validation study by comparing the outcomes obtained from our implementation with those derived from the widely adopted commercial software, ANSYS. To enable a comprehensive evaluation, we select specific benchmark problems that involve the interaction of two elastic bodies. Through a meticulous analysis and comparison of results, we demonstrate the effectiveness and robustness of our approach. The findings of this study contribute substantively to the advancement of numerical techniques for solving contact problems. The validated methodology not only establishes a solid foundation for future research endeavors but also offers a reliable framework for conducting simulations in this domain. Furthermore, the insights gained from this study can potentially facilitate the development of more efficient and accurate computational algorithms for addressing contact problems encountered in various engineering applications.

Comparative Performance of High-Throughput Methods for Protein pKa Predictions.

The medically relevant field of protein-based therapeutics has triggered a demand for protein engineering in different pH environments of biological relevance. In silico engineering workflows typically employ high-throughput screening campaigns that require evaluating large sets of protein residues and point mutations by fast yet accurate computational algorithms. While several high-throughput pKa prediction methods exist, their accuracies are unclear due to the lack of a current comprehensive benchmarking. Here, seven fast, efficient, and accessible approaches including PROPKA3, DeepKa, PKAI, PKAI+, DelPhiPKa, MCCE2, and H++ were systematically tested on a nonredundant subset of 408 measured protein residue pKa shifts from the pKa database (PKAD). While no method outperformed the null hypotheses with confidence, as illustrated by statistical bootstrapping, DeepKa, PKAI+, PROPKA3, and H++ had utility. More specifically, DeepKa consistently performed well in tests across multiple and individual amino acid residue types, as reflected by lower errors, higher correlations, and improved classifications. Arithmetic averaging of the best empirical predictors into simple consensuses improved overall transferability and accuracy up to a root-mean-square error of 0.76 pKa units and a correlation coefficient (R2) of 0.45 to experimental pKa shifts. This analysis should provide a basis for further methodological developments and guide future applications, which require embedding of computationally inexpensive pKa prediction methods, such as the optimization of antibodies for pH-dependent antigen binding.

System reliability evaluation and dynamic optimization based on an improved reliability block diagram

Reliability block diagram (RBD) is an effective tool for modeling and evaluating system reliability. During operation, a system’s reliability may decrease significantly due to the failure of certain critical nodes and thus should be reconfigured. This paper presents a framework for system reliability evaluation and dynamic optimization based on RBD, designed from the perspective of system users. First, we improve the classic RBD model with a new encoding scheme and develop an accurate RBD computation algorithm that is easily recognized by computers and highly efficient. Second, we create an optimization algorithm based on Tabu Search to reconfigure the system after node failure, striking a balance between system reliability recovery and RBD variation amplitude. Finally, we provide some numerical examples and a computational experiment based on a practical instance from a navy fleet to demonstrate the correctness and effectiveness of our proposed methods.

Numerical Solution of Shrödinger Equations Based on the Meshless Methods

In this work, two-dimensional time-dependent quantum equation problems are studied. We introduce a numerical algorithm for solving the two-dimensional nonlinear complex quantum system with MLS and FDM methods. An efficient and accurate computational algorithm based on both, the moving least squares (MLS) and the finite difference (FDM) methods is proposed for solving it. The results demonstrate that the proposed algorithm is a robust algorithm with good accuracy. This is developed on MLS and FDM methods using numerical simulation for solving these kind of problems.

An Efficient and Privacy-Preserving Route Matching Scheme for Carpooling Services

With the popularity of intelligent terminals and the advances of mobile Internet, carpooling service, which reduces the travel cost of each user by allowing multiple users to share one car, has received considerable attention and makes our life more convenient. However, the vigorous development of carpooling services still faces severe challenges in users’ location or route privacy. In this article, we propose an efficient and privacy-preserving route matching scheme called TAROT for carpooling services. With TAROT, users can enjoy high-quality carpooling services while without revealing sensitive location and route information. Specifically, based on a Goldwasser–Micali-based equality determination algorithm (GMEDA), we design an accurate similarity computation algorithm (ASCA), which allows users to get accurate carpooling results over ciphertexts. Meanwhile, the reverse Minhash (RM) method is also designed to construct a dissimilar route filter algorithm (DRFA), which can filter out dissimilar routes in advance and reduce computational costs and communication overheads. Security analysis shows that TAROT can protect users’ location privacy. In addition, TAROT is also evaluated with many random maps, and the simulation results demonstrate that TAROT is highly efficient.

Petroleum rock mechanics: An area worthy of focus in geo-energy research

Petroleum rock mechanics: An area worthy of focus in geo-energy research

An Accurate and Fast Computational Algorithm Based on Hybrid Methods

The numerical solutions of nonlinear equation for a single diode equivalent circuit of a solar cell are introduced. Four numerically algorithms, which include newton's; Predictor-Corrector Hally and Accelerated Predictor-Corrector Hally methods are described and compared in the present work. These algorithms are applied to calculate the voltage; current; power of a solar cell with the different values of load resistance programmed by Matlab language. The results showed that the proposed algorithm is the most efficient compare with other three algorithms.

Experimental determination for viscosity of fly ash nanofluid and fly ash-Cu hybrid nanofluid:Prediction and optimization using artificial intelligent techniques

ABSTRACT The advanced heat transfer applications of nanofluids make it important to investigate their viscous properties. For various engineering applications, accurate measurement of dynamic viscosity of nanofluid plays a major role. This research aims to provide accurate and reliable soft computational algorithms to predict the viscosity of fly ash nanofluids and fly ash-Cu hybrid nanofluids accurately using measured data. In this work, the dynamic viscosity of water-based stable fly ash nanofluid and fly ash–Cu (80:20% by volume) hybrid nanofluid for the concentration range of 0–4 vol.% determined in the temperature range of 30–60°C experimentally. The nanoparticles are characterized by SEM, TEM, and DLS techniques. The stability of the nanofluids assessed by Zeta potential measurement. Outcomes show that the viscosity of hybrid nanofluid is found to be higher than fly ash nanofluid. Correlations are suggested using multiple linear regression analysis to predict the nanofluid viscosity based on the obtained results. We have adopted a novel soft computing technique, namely ANN and MGGP to optimize the dynamic viscosity data of nanofluids obtained experimentally. The MGGP model predicts the viscosity values for fly ash nanofluid by (R = 0.99988, RMSE = 0.0019, and MAPE = 0.25%). In addition, statical analysis reveals that the MGGP model has excellent performance for fly ash-Cu/Water hybrid nanofluid viscosity (R = 0.9975, RMSE = 0.0063, and MAPE = 0.664%). In contrast to the ANN method and multiple linear regression analysis, the MGGP approach exhibits better results in estimating nanofluid viscosity.

Non-adiabatic Excited-State Molecular Dynamics: Theory and Applications for Modeling Photophysics in Extended Molecular Materials.

Optically active molecular materials, such as organic conjugated polymers and biological systems, are characterized by strong coupling between electronic and vibrational degrees of freedom. Typically, simulations must go beyond the Born-Oppenheimer approximation to account for non-adiabatic coupling between excited states. Indeed, non-adiabatic dynamics is commonly associated with exciton dynamics and photophysics involving charge and energy transfer, as well as exciton dissociation and charge recombination. Understanding the photoinduced dynamics in such materials is vital to providing an accurate description of exciton formation, evolution, and decay. This interdisciplinary field has matured significantly over the past decades. Formulation of new theoretical frameworks, development of more efficient and accurate computational algorithms, and evolution of high-performance computer hardware has extended these simulations to very large molecular systems with hundreds of atoms, including numerous studies of organic semiconductors and biomolecules. In this Review, we will describe recent theoretical advances including treatment of electronic decoherence in surface-hopping methods, the role of solvent effects, trivial unavoided crossings, analysis of data based on transition densities, and efficient computational implementations of these numerical methods. We also emphasize newly developed semiclassical approaches, based on the Gaussian approximation, which retain phase and width information to account for significant decoherence and interference effects while maintaining the high efficiency of surface-hopping approaches. The above developments have been employed to successfully describe photophysics in a variety of molecular materials.

Modulation of reproducing kernel Hilbert space method for numerical solutions of Riccati and Bernoulli equations in the Atangana-Baleanu fractional sense

Abstract This article is concerned with design and comprehensive study of a numerical approach for solving Riccati and Bernoulli equations in the Atangana-Baleanu fractional sense. The proposed technique is using the reproducing kernel Hilbert space to approximate the solution to those class of fractional differential equations in the form of uniformly convergent series with respect to space variables. An accurate computational algorithm is presented to confirm the gained analysis. The relevant theorems and characterizations related to Riccati and Bernoulli equations are included among the reproducing kernel theory. Supplementary problems at the end of the article serve as a complete review of the utilized method and theories. The numerical consequences of the proposed approach are practically effective and impressive, whilst the utilized theories lay the foundation stone for addressing such issues. In light of the summary, conclusions and future recommendations are also provided.

A New Accurate and Fast Homography Computation Algorithm for Sports and Traffic Video Analysis

Homography has wide applications in aerial photographic surveys, camera calibration, traffic scene analysis, and sports science, such as player and team performance evaluation. Unlike the mainstream homography that utilizes points as matching features, homography estimation for sports and traffic video can achieve higher accuracy and speed by utilizing straight lines in the scenes, which convey more information than points. Owing to the more stringent requirement of accuracy and computational speed for advanced video analysis, this paper presents a novel homography computational algorithm. Three major novelties are proposed and validated, which are multiple points Hough transform for straight line extraction, correspondence initialization by angle to estimate a set of quasi-optimal solutions, and the feature correspondences optimization to achieve a minimized error using genetic algorithm. With these contributions, the experiments have shown that the proposed algorithm can improve the homography computational accuracy by up to 130% and reduce the processing time by up to 96% over the state-of-the-art algorithms for the same purposes.

An improved ghost-cell sharp interface immersed boundary method with direct forcing for particle laden flows

In this paper, an accurate and stable sharp interface immersed boundary method(IBM) is presented for the direct numerical simulation of particle laden flows. The current IBM method is based on the direct-forcing method by incorporating the ghost-cell approach implicitly. An important feature of this IBM is the sharp representation of the solid surface, contrary to other variants of IBM for freely moving particles in which the solid surface is diffuse. Moreover, a correction of the diameter is not necessary for obtaining accurate results. The current ghost-cell IBM is stable because spurious oscillations incurred due to discontinuity in the pressure and velocity field in moving particle simulations is avoided. An algorithm for accurate torque computation is developed. The proposed algorithm is verified by comparison to an analytical expression and is shown to give a substantial improvement over the existing method. Finally, the present IBM is validated for various test cases of single and multi-particle systems and is shown to be accurate and robust for a wide range of flow conditions.

Communication: Calculation of interatomic forces and optimization of molecular geometry with auxiliary-field quantum Monte Carlo.

We propose an algorithm for accurate, systematic, and scalable computation of interatomic forces within the auxiliary-field quantum Monte Carlo (AFQMC) method. The algorithm relies on the Hellmann-Feynman theorem and incorporates Pulay corrections in the presence of atomic orbital basis sets. We benchmark the method for small molecules by comparing the computed forces with the derivatives of the AFQMC potential energy surface and by direct comparison with other quantum chemistry methods. We then perform geometry optimizations using the steepest descent algorithm in larger molecules. With realistic basis sets, we obtain equilibrium geometries in agreement, within statistical error bars, with experimental values. The increase in computational cost for computing forces in this approach is only a small prefactor over that of calculating the total energy. This paves the way for a general and efficient approach for geometry optimization and molecular dynamics within AFQMC.

On uniqueness of time-consistent Markov policies for quasi-hyperbolic consumers under uncertainty

We give a set of sufficient conditions for uniqueness of a time-consistent stationary Markov consumption policy for a quasi-hyperbolic household under uncertainty. To the best of our knowledge, this uniqueness result is the first presented in the literature for general settings, i.e. under standard assumptions on preferences, as well as some new condition on a transition probability. This paper advocates a “generalized Bellman equation” method to overcome some predicaments of the known methods and also extends our recent existence result. Our method also works for returns unbounded from above. We provide a few natural extensions of optimal policy uniqueness: convergent and accurate computational algorithm, monotone comparative statics result and generalized Euler equation.

Efficient computational algorithms for modeling guided wave dispersion in arterial walls

Arterial stiffness is a well-known biomarker of early cardiovascular disease. Shear wave dispersion ultrasound vibrometry (SDUV) has emerged as a promising technique to estimate local arterial stiffness from the observed dispersion of guided waves. With the ultimate goal of developing real-time inversion for arterial stiffness from SDUV measurements, we develop and validate highly efficient and accurate computational algorithms that compute the wave dispersion in multi-layered immersed tubes. The proposed approach carefully combines Fourier transformation and one-dimensional finite-element discretization to accurately capture fully three-dimensional wave propagation. The method is several orders of magnitude more efficient than three-dimensional finite element simulation, and eliminates other complexities such as the need for absorbing boundary conditions. The method is validated using SDUV experiments on tissue-mimicking phantoms. The validation exercise uncovered an important detail that is often overlooked—the dispersion curve captured through SDUV experiments does not correspond to a single dispersion curve, but a combination of multiple dispersion curves. This implies that proper identification of the dispersion curves could be critical to estimating the arterial stiffness. In this talk, we present the details of the proposed method including formulation, computational complexity, verification and validation.Arterial stiffness is a well-known biomarker of early cardiovascular disease. Shear wave dispersion ultrasound vibrometry (SDUV) has emerged as a promising technique to estimate local arterial stiffness from the observed dispersion of guided waves. With the ultimate goal of developing real-time inversion for arterial stiffness from SDUV measurements, we develop and validate highly efficient and accurate computational algorithms that compute the wave dispersion in multi-layered immersed tubes. The proposed approach carefully combines Fourier transformation and one-dimensional finite-element discretization to accurately capture fully three-dimensional wave propagation. The method is several orders of magnitude more efficient than three-dimensional finite element simulation, and eliminates other complexities such as the need for absorbing boundary conditions. The method is validated using SDUV experiments on tissue-mimicking phantoms. The validation exercise uncovered an important detail that is often overlo...

A new efficient and accurate spline algorithm for the matrix exponential computation

In this work an accurate and efficient method based on matrix splines for computing matrix exponential is given. An algorithm and a MATLAB implementation have been developed and compared with the state-of-the-art algorithms for computing the matrix exponential. We also developed a parallel implementation for large scale problems. This implementation allowed us to get a much better performance when working with this kind of problems.

An Accurate and Fast Computational Algorithm for the Two-diode Model of PV Module Based on a Hybrid Method

This paper proposes an improved hybrid method to compute the parameters of the two-diode model of photovoltaic (PV) module. Unlike previous methods, it attains the speed of the analytical approach by utilizing only datasheet information. Furthermore, its accuracy is not compromised as it does not require simplifications in its computation. Four parameters are determined analytically, while the remaining three are optimized by using an evolutionary algorithm, i.e., the differential evolution. The speed is improved because the parameters are optimized only once, i.e., at standard test condition, while the values at other conditions are computed analytically. Furthermore, a procedure to guide the initial conditions of the Newton–Raphson iteration is introduced. For validation, the algorithm is compared to other established computational methods for mono-, polycrystalline, and thin film modules. When evaluated against the experimental data, the mean absolute error is improved by one order of magnitude, while the speed is increased by approximately threefold. The standard deviation of the decision parameters over 100 independent runs is less than 0.1—which suggests that the optimization process is very consistent. Due to its speed and accuracy, the method is envisaged to be useful as a computational engine in PV simulator.

Detailed simulation of the pulsating detonation wave in the shock-attached frame

The paper is devoted to the numerical investigation of the stability of propagation of pulsating gas detonation waves. For various values of the mixture activation energy, detailed propagation patterns of the stable, weakly unstable, irregular, and strongly unstable detonation are obtained. The mathematical model is based on the Euler system of equations and the one-stage model of chemical reaction kinetics. The distinctive feature of the paper is the use of a specially developed computational algorithm of the second approximation order for simulating detonation wave in the shock-attached frame. In distinction from shock capturing schemes, the statement used in the paper is free of computational artifacts caused by the numerical smearing of the leading wave front. The key point of the computational algorithm is the solution of the equation for the evolution of the leading wave velocity using the second-order grid-characteristic method. The regimes of the pulsating detonation wave propagation thus obtained qualitatively match the computational data obtained in other studies and their numerical quality is superior when compared with known analytical solutions due to the use of a highly accurate computational algorithm.

An efficient approach for computing scattering by inhomogeneous objects with thin dielectric structures

SummaryThis paper incorporates the compensated Mitzner boundary conditions (CMBC) into the hybrid finite element‐ boundary integral‐multilevel fast multipole algorithm (FE‐BI‐MLFMA) for fast and accurate computation of scattering by three‐dimensional inhomogeneous objects with thin dielectric structures. In this CMBC‐FE‐BI‐MLFMA, each thin dielectric layer is reduced to an interface with zero thickness; then CMBC is applied to establish the relation of electromagnetic fields across the interfaces. This approach can reduce the amount of computation and remain the advantages of versatility, accuracy, and efficiency of the conventional FE‐BI‐MLFMA algorithm. The formulation of this CMBC‐FE‐BI‐MLFMA is presented in this paper. An efficient preconditioner based on the sparse approximate inverse is employed and further parallelized on memory‐distributed platforms. Numerical results are presented to demonstrate the accuracy, efficiency, and capability of the parallel CMBC‐FE‐BI‐MLFMA. Copyright © 2016 John Wiley & Sons, Ltd.