Accuracy Of Method Research Articles

A robust solution for recognizing accurate handwritten text extraction using quantum convolutional neural network and transformer models

Handwritten text extraction from images is challenging due to the variability of styles in handwriting, quality of images, and noise backgrounds. Existing methods often struggle to achieve high accuracy, hindering document analysis, optical character recognition, and data entry applications. We propose a novel approach to improve extraction accuracy, combining Quantum convolutional neural networks (QCNN) and transformer-based neural networks (TextExtractNet) named QTEN. Our method leverages the strengths of both models to recognize and extract handwritten text from images. Experimental results show that our approach achieves a 96 % accuracy rate, outperforming existing methods. This breakthrough has significant implications for automating document processing, data entry, and related applications. Our method's robustness and accuracy make it a valuable tool for industries relying on handwritten document processing, such as healthcare, finance, and government.

Assessing the Effectiveness of MoSCoW Prioritization in Software Development: A Holistic Analysis across Methodologies

Effective software Requirement Prioritization plays a pivotal role in the success of the Software Development process, ultimately contributing to the successful delivery of high-quality products. Among the various methods for Requirement Prioritization, the MoSCoW method has gained widespread adoption due to its ease of use. However, its overall effectiveness remains a subject of inquiry. This paper presents a rigorous assessment of the MoSCoW Requirement Prioritization technique, drawing insights from software developers who engage in the Prioritization process. Our evaluation encompasses a distinct perspective: that of the developers tasked with Prioritization. The feedback solicited from developers encapsulates a diverse set of criteria, shedding light on the method's efficacy. Additionally, we perform sentiment analysis on the user experience of the Prioritization task to corroborate the method's accuracy and efficiency. Our study unfolds through a practical exercise involving the Prioritization of a predefined set of requirements using MoSCoW principles. A mixed method approach is employed for the purpose of assessing the effectiveness of MoSCoW. The findings of our quantitative research underscore the method's limitations, indicating that it may not be as effective and precise as previously believed. Furthermore, through qualitative analysis, we are able to highlight the complexities and challenges associated with MoSCoW-based Prioritization. The insights gained from this analysis prompt contemplation regarding the potential introduction of an evolved Requirement Prioritization method, while leveraging MoSCoW as a foundational framework. This research aims to inform the ongoing evolution of Requirement Prioritization methodologies, ultimately enhancing the efficiency and accuracy of Software Development processes.

Improving Accuracy and Reproducibility of Cartilage T2 Mapping in the OAI Dataset Through Extended Phase Graph Modeling.

The Osteoarthritis Initiative (OAI) collected extensive imaging data, including Multi-Echo Spin-Echo (MESE) sequences for measuring knee cartilage T2 relaxation times. Mono-exponential models are used in the OAI for T2 fitting, which neglects stimulated echoes and B1 inhomogeneities. Extended Phase Graph (EPG) modeling addresses these limitations but has not been applied to the OAI dataset. To assess how different fitting methods, including EPG-based and exponential-based approaches, affect the accuracy and reproducibility of cartilage T2 in the OAI dataset. Retrospective. From OAI dataset, 50 subjects, stratified by osteoarthritis (OA) severity using Kellgren-Lawrence grades (KLG), and 50 subjects without OA diagnosis during OAI duration were selected (each group: 25 females, mean ages ~61 years). 3-T, two-dimensional (2D) MESE sequence. Femoral and tibial cartilages were segmented from DESS images, subdivided into seven sub-regions, and co-registered to MESE. T2 maps were obtained using three EPG-based methods (nonlinear least squares, dictionary matching, and deep learning) and three mono-exponential approaches (linear least squares, nonlinear least squares, and noise-corrected exponential). Average T2 values within sub-regions were obtained. Pair-wise agreement among fitting methods was evaluated using the stratified subjects, while reproducibility using healthy subjects. Each method's T2 accuracy and repeatability varying signal-to-noise ratio (SNR) were assessed with simulations. Bland-Altman analysis, Lin's concordance coefficient, and coefficient of variation assessed agreement, repeatability, and reproducibility. Statistical significance was set at P-value <0.05. EPG-based methods demonstrated superior T2 accuracy (mean absolute error below 0.5 msec at SNR > 100) compared to mono-exponential methods (error > 7 msec). EPG-based approaches had better reproducibility, with limits of agreement 1.5-5 msec narrower than exponential-based methods. T2 values from EPG methods were systematically 10-17 msec lower than those from mono-exponential fitting. EPG modeling improved agreement and reproducibility of cartilage T2 mapping in subjects from the OAI dataset. 3 TECHNICAL EFFICACY: Stage 1.

Revolutionizing endometriosis treatment: automated surgical operation through artificial intelligence and robotic vision.

Clinical limitations due to poverty significantly impact the lives and health of many individuals globally. Nevertheless, this challenge can be addressed with modern technologies, particularly through robotics and artificial intelligence. This study aims to address these challenges using advanced technologies in robotic surgery and artificial intelligence, proposing a method to fully automate endometriosis robotic surgery with a focus on interpretability, accuracy, and reliability. A methodology for fully automatic endometriosis surgery is introduced. Given the complexity of endometriosis lesions detection, they are categorized by their anatomical location to improve system interpretability. Then, three ensemble U-Net frameworks are designed to detect and localize common types of endometriosis lesions intraoperatively. A cross-training approach is employed, exploring U-Net models with diverse neural architectures-such as ResNet50, ResNet101, VGG19, InceptionV3, MobileNet, and EfficientNetB7-to develop U-Net ensemble models for precise endometriosis lesions segmentation. A novel image augmentation technique is also introduced, enhancing the segmentation models' accuracy and reliability. Furthermore, two U-Net models are developed to localize the ovaries and uterus, mitigating unexpected noise and bolstering the method's accuracy and reliability. The image segmentation models, assessed using the Intersection over Union (IoU) metric, achieved outstanding results: 97.57% for ovarian, 96.35% for uterine, and 92.58% for peritoneal endometriosis. This study proposes a fully automatic method for some common types of endometriosis surgery, including ovarian endometriomas and superficial endometriosis. This method is centered around three ensemble U-Net frameworks and a noise reduction technique using two additional U-Nets for localizing the ovaries and uterus. This approach has the potential to significantly improve the accuracy and reliability of robotic surgeries, potentially reducing healthcare costs and improving outcomes for patients worldwide.

A fault location method for DC transmission line based on the distributed parameter model considering line parameter uncertainty

Addressing the impact of whether line parameters can be accurately obtained and the errors due to environmental influences on the accuracy of fault location for DC transmission line, this paper proposes a fault location method based on the distributed parameter model that accounts for uncertainty in line parameter. To address the challenge of accurately obtaining DC transmission line parameters, a high-order derivative-based parameter estimation method is introduced to determine unknown equivalent parameters of the DC transmission line. Additionally, an optimization algorithm is incorporated to dynamically correct parameter errors, thereby enhancing parameter stability and constructing an accurate line model. Based on this, theoretical derivations confirm the applicability of the Tellegen's quasi-power theorem in the normal operation and internal fault equivalent models of DC transmission line. A fault location function is established based on the relationship between the line's Tellegen quasi-power characteristic and fault distance. The proposed method's effectiveness and accuracy are validated using a DC system model built on the PSCAD/EMTDC simulation platform. Simulation results indicate that the proposed method can locate faults under different fault conditions.

A novel optimization‐based modal pushover analysis procedure for estimating the response of structures

AbstractIn recent years, studies have focused on the seismic behavior of various structural systems, including single‐ and double‐layered spatial structures. While nonlinear dynamic analyses are common for these studies, they are time‐consuming and complex. To address this, many pushover procedures have been developed, but their effectiveness in spatial structures remains underexplored. This study introduces an optimized lateral loading profile for evaluating the seismic response of walled bilayer spatial structures. By combining dominant inertial forces and optimizing their corresponding coefficients with the particle swarm optimization (PSO) algorithm, an optimal loading profile is derived. The method's accuracy was tested on models with varying arch height‐to‐span ratios and a constant wall height‐to‐span ratio, as well as a 15‐story frame structure. The results obtained showed that the proposed method closely matches incremental dynamic analyses (IDAs) in predicting base shear, initial stiffness, and displacements, especially for higher arch height‐to‐span ratios. Additionally, the method accurately estimates drift profiles and nodal displacements on roofs and walls, and performs better than similar mode‐based methods for frame structures.

Quantitative Analysis of Protein-Protein Equilibrium Constants in Cellular Environments Using Single-Molecule Localization Microscopy.

Current methods for determining equilibrium constants often operate in three-dimensional environments, which may not accurately reflect interactions with membrane-bound proteins. With our technique, based on single-molecule localization microscopy (SMLM), we directly determine protein-protein association (Ka) and dissociation (Kd) constants in cellular environments by quantifying associated and isolated molecules and their interaction area. We introduce Kernel Surface Density (ks-density,) a novel method for determining the accessible area for interacting molecules, eliminating the need for user-defined parameters. Simulation studies validate our method's accuracy across various density and affinity conditions. Applying this technique to T cell signaling proteins, we determine the 2D association constant of T cell receptors (TCRs) in resting cells and the pseudo-3D dissociation constant of pZAP70 molecules from phosphorylated intracellular tyrosine-based activation motifs on the TCR-CD3 complex. We address challenges of multiple detection and molecular labeling efficiency. This method enhances our understanding of protein interactions in cellular environments, advancing our knowledge of complex biological processes.

A new thermodynamic modeling method for Brayton cycle schemes based on heat flux of the heat source

The supercritical carbon dioxide Brayton cycle is a promising option for power generation. However, the existing models for assessing its performance lack universality for various layouts and rely on challenging-to-acquire thermodynamic parameters at state points (such as the inlet temperature of the turbine). The present study proposes a modular thermodynamic modeling method, making it suitable for various layouts. A parameter-matching method is proposed using the heat flux of the heat source as an input, making it more practical than methods relying on parameters at state points. The method's accuracy is verified with a deviation not exceeding ±1.5%. Subsequently, four typical schemes including simple recuperation, recompression, intercooling, and reheating cycles are analyzed. The effect of heat flux on power generation and thermal efficiency of each scheme is also evaluated. It is found that the simple recuperation cycle is capable of operating effectively within lower thresholds for both maximum pressure and temperature. For maximizing power generation, the intercooling cycle offers an advantage. As for thermal efficiency, the simple recuperation cycle is best suited for heat fluxes under 1.6 MW/m2, while the recompression cycle is recommended for fluxes exceeding this limit. The outcomes can guide the designing and optimization of Brayton cycle schemes.

A TDLAS gas detection method based on digital signal modulation

This research presents a novel TDLAS gas concentration detection method based on digital modulation. The method inherits the advantage of a simple system structure from Direct Absorption Spectroscopy, as well as the excellent sensitivity of Wavelength Modulation Spectroscopy. A low-frequency sawtooth wave is employed to drive the laser, resulting in a digital direct absorption spectrum signal. By constructing a cosine modulation sequence in the time domain and performing linear interpolation on the direct absorption spectrum, it successfully converts to a wavelength-modulated absorption spectrum and simultaneously generates a digital reference signal at double the frequency. Ultimately, phase-locked amplification technology can be used to calculate the amplitude of the second harmonic. Since both the modulation and reference signals are generated digitally, they ensure a strict frequency-doubling relationship and phase correlation, which not only reduces the complexity of the hardware system but also effectively minimizes system errors. Because digital technology generates both the modulation and reference signals, it ensures that they have the same phase and a strict frequency-doubling relationship. Experimental results demonstrate that, when fitted by a cubic polynomial, the second harmonic amplitude and the gas concentration have a correlation coefficient (R2) as high as 0.99999 and a root mean square error as low as 0.0011. This proves the method's great accuracy and reliability in practical applications.

AQbD-guided stability indicating HPLC method for azelnidipine and chlorthalidone fixed-dose combination tablet: a green approach

This study developed a stability-indicating RP-HPLC method for quantifying azelnidipine and chlorthalidone in fixed-dose formulations using Analytical Quality by Design (AQbD) principles. A Plackett-Burman design was used for factor screening, followed by risk assessment and optimization using a central composite design to evaluate the effects of the organic phase percentage, flow rate, and modifier concentration. Acetonitrile percentage was identified as the most critical factor. The optimized method employed a Reliant™ C18 Waters column with a mobile phase of 0.1% formic acid and acetonitrile (62:38% v/v), with chlorthalidone and azelnidipine eluting at 4.1 and 13.7 minutes, respectively. Validation showed the method's robustness, accuracy, and precision. Forced degradation studies confirmed its reliability for tablet formulation analysis, and an Analytical Greenness score of 0.55 highlighted its environmental sustainability. This method is efficient, simple, and well-suited for routine quality control in the pharmaceutical industry.

High‐speed algorithm for fault detection and location in DC microgrids based on a novel time–frequency analysis

AbstractProtecting DC microgrids (DCMGs) from faults is critical due to the rapid current changes that occur in milliseconds. However, ensuring fast and accurate protection in DCMGs is more challenging than in AC systems. This study proposes a novel protection algorithm using traveling waves (TWs) for fault detection and localization. The high‐order synchrosqueezing transform (FSSTH) is applied to precisely identify TWs at the relay location. FSSTH offers a sharp time–frequency representation, enhancing the accuracy and speed of fault detection. This method can accurately detect transient phenomena like TWs in DCMGs, even with noise and variable fault resistance. By using the spectral envelope with FSSTH, ridges in time–frequency representations are extracted, improving fault diagnosis. The approach differentiates external from internal faults and recognizes fault direction by assessing TW polarity. Testing on two different DCMGs showed this algorithm's high efficiency and accuracy, with fault location errors ranging from 1 to 50 meters in low‐voltage and 13 to 64 meters in medium‐voltage DCMGs, even under challenging conditions like high resistance (up to 500 Ω) and low signal‐to‐noise ratio (5 dB). These results demonstrate the method's superior accuracy and robustness compared to existing techniques.

Radial Kernels Collocation Method for the Solution of Volterra Integro-Differential Equations

Radial kernel interpolation is an advanced method in approximation theory for the construction of higher order accurate interpolants for scattered data up to higher dimensional spaces. In this manuscript, we formulate a radial kernel collocation approach for solving problems involving the Volterra integro-differential equations using two radial kernels: The Generalized Multi-quadrics and the linear Laguerre-Gaussians. This was achieved by simplifying the Volterra integral problem's solution to an algebraic system of equations. The impact of the shape parameter present in every kernel on the method's accuracy is examined and found to be significant. Two examples were used to illustrate the process; the numerical results are shown as tables and graphs. MATLAB 2018a was employed in the process.

Operational Modal Analysis of Self-excited Vibrations in Milling Considering Periodic Dynamics

Abstract A new method is presented to identify the dynamics of regenerative chatter from measured process vibrations in milling. This method combines the synchronous once-per-revolution sampling of process vibrations with Operational Modal Analysis to estimate the Floquet multipliers of the delayed linear time-periodic dynamics in milling, all from the natural process vibrations without external excitation. The identified multipliers quantify vibration stability, enabling chatter prediction before it occurs. In addition to this, they can be used to calibrate physics-based chatter models based on vibration measurements solely within the stable region. The method's accuracy in identifying Floquet multipliers is validated through extensive numerical simulations and two experimental case studies. The results show that chatter due to both Hopf and period-doubling bifurcations can be predicted from the process vibrations during stable cuts. Moreover, the experimental case studies demonstrate a vibration measurement system for implementing the presented method in standard milling operations and confirm its effectiveness in practice.

A new active learning surrogate model for time- and space-dependent system reliability analysis

This study introduces a novel method for time- and space-dependent system reliability analysis, integrating an active learning surrogate model with an innovative parallel updating strategy. A global Kriging model is developed to represent the signs of random samples using efficient global optimization. From a Bayesian perspective, the prediction probabilities of random sample signs within the time-space domain are calculated, and the sample with the lowest prediction probability is chosen to update the global Kriging model. The system extremum for each sample in the time-space domain is determined, and the corresponding random variables, time-space coordinates, and failure modes are selected. To further decrease iteration times, a parallel updating strategy that considers both the predicted probability and the correlation among candidate samples is proposed. Additionally, a new stopping criterion is introduced to balance accuracy and efficiency, terminating the updating process appropriately. The method's accuracy and efficiency are validated through three examples.

A distributed dynamic load identification approach for thin plates based on inverse Finite Element Method and radial basis function fitting via strain response

Dynamic load identification is crucial for structural design and health monitoring. Traditional methods for distributed dynamic loads (DDLs) typically involve establishing a transfer matrix, which often leads to ill-posed problems. In this paper, a novel method based on the inverse Finite Element Method (iFEM) and radial basis function (RBF) fitting was proposed to identify DDLs from measured strain responses. The method begins with the reconstruction of the displacement field from discrete strain data using iFEM, followed by applying RBF fitting to obtain a continuous displacement field. To enhance the performance of the RBF fitting, a constrained matrix for force boundary conditions is introduced, along with a selection rule for the RBF shape parameter. The identified loads are subsequently determined by incorporating the well-fitted RBFs into the motion equations of thin plates. This approach eliminates the need for a transfer matrix, thereby avoiding ill-posedness, and enables the simultaneous identification of the spatial distribution and time history of the loads. Three numerical examples for the identification of concentrated loads, uniformly distributed loads, and globally distributed loads demonstrate the method's effectiveness, accuracy, and noise resistance, with additional validation provided through a comparison with the classic time-domain method.

Effects of foundation pit excavation on adjacent metro stations and shield tunnel structures: a study on horizontal displacement

PurposeThe aim of this study is to investigate the horizontal displacement effects of foundation pit excavation on adjacent metro stations and shield tunnel composite structures. It seeks to develop a theoretical calculation method capable of accurately assessing these engineering impacts, aiming to provide practical assistance for engineering applications.Design/methodology/approachThis study introduces a model for shield tunnel segments incorporating rotation and misalignment, considering the constraints of metro stations. It establishes a displacement model for tunnel-station combinations during foundation pit excavation, deriving a formula for calculating station-proximal tunnel horizontal displacements. The method's accuracy is validated against field data from three engineering cases. The research also explores variations in tunnel displacement, inter-ring shear force, misalignment and rotation angle under different spatial relationships between pits, tunnels and stations.FindingsThis study models uneven deformation between stations and tunnels due to bending stiffness and shear constraints. It enhances the misalignment model with station-induced shear effects and introduces coefficients for their mutual interaction. Results show varied responses based on pit-station-tunnel positioning: minimal displacement near pit edges (coefficients around 0.1) and significant effects near pit centers (coefficients from 0.4 to 0.5). “Whip effect” from station constraints affects tunnel displacement, shear force, misalignment and rotation, with fluctuations decreasing with distance from excavation areas.Originality/valueThis study demonstrates significant originality and value. It introduces a novel displacement model for tunnel-station combinations considering station constraints, addressing theoretical calculations of horizontal displacement effects from foundation pit excavation on metro stations and shield tunnel structures. Through validation with field data and parameter studies, the concept of influence coefficients is proposed, offering insights into variations in structural responses under different spatial relationships. This research provides crucial technical support and decision-making guidance for optimizing designs and facilitating practical construction in similar engineering projects.

Expedient measurement of total protein in human serum and plasma via the biuret method using fiber optic probe for patient samples and certified reference materials.

The biuret method is currently recognized as a reference measurement procedure for serum/plasma total protein by the Joint Committee for Traceability in Laboratory Medicine (JCTLM). However, as the reaction involved in this method is highly time-dependent, to ensure identical measurement conditions for calibrator and samples for high accuracy, a fast and simple measurement procedure is critical to ensure the precision and trueness of this method. We measured serum/plasma total protein using a Cary 60 spectrophotometer coupled with a fiber optic probe, which was faster and simpler than the conventional cuvette method. The biuret method utilizing alkaline solutions of copper sulfate and potassium sodium tartrate was added to the sample and calibrator (NIST SRM 927e) incubated for 1h before measurement. A panel of samples consisting of pooled human serum, single donor serum, and certified reference materials (CRMs) from three sources were measured for method validation. Sixteen native patient samples were measured using the newly developed biuret method and compared against clinical analyzers. Additionally, the results of three cycles of a local External Quality Assessment (EQA) Programme submitted by participating clinical laboratories were compared against the biuret method. Our biuret method using fiber optic probe demonstrated good precision with within-day relative standard deviation (RSD) of 0.04 to 0.23% and between-day RSD of 0.58%. The deviations between the obtained values and the certified values for all three CRMs ranged from -0.38 to 1.60%, indicating good method trueness. The routine methods using clinical analyzers were also found to agree well with the developed biuret method using fiber optic probe for EQA samples and native patient samples. The biuret method using a fiber optic probe represented a convenient and reliable way of measuring serum total protein. It also demonstrated excellent precision and trueness using CRMs and patient samples, which made the method a simpler candidate reference method for serum protein measurement.

An improved implicit direct-forcing immersed boundary method (DF-IBM) around arbitrarily moving rigid structures

An improved implicit direct-forcing immersed boundary method (DF-IBM) is presented for simulating incompressible flows around complex rigid structures undergoing arbitrary motion. The current approach harnesses the pressure implicit with splitting of operators algorithm to handle the fluid–solid system's dual constraints in a segregated manner. As a result, the divergence-free condition is preserved throughout the Eulerian domain, and the no-slip velocity boundary condition is exactly enforced on the immersed boundary. A new pressure Poisson equation (PPE) is derived, incorporating the boundary force where the no-slip condition is already met, enabling the use of fast iterative PPE solvers without modifications. The improvement involves integrating Lagrangian weight methods having better reciprocity over the IBM-related linear operators with the implicit formulation. An additional force initialization scheme is introduced to further boost the algorithm's performance. The method's accuracy, efficiency, and capability are verified through various stationary and moving immersed boundary cases. The results are validated against experimental and numerical data from the literature. The proposed improvements seamlessly integrate into existing incompressible fluid solvers with minimal adjustments to the original system equations, highlighting their ease of implementation.

Enzyme-accelerated catalytic DNA circuits enable rapid and one-pot detection of bacterial pathogens

Catalytic DNA circuits, serving as signal amplification strategies, can enable simple and accurate detection of pathogenic bacteria in complex matrices but suffer from low reaction rates and depths. Herein, we design an enzyme-accelerated catalytic hairpin assembly (EACHA) in which duplex DNA products are converted into hairpin reactants to continue participating in the next circuit reaction with the assistance of RNase H. Profiting from the high recyclability of the reactants, EACHA exhibits an approximately 37.6-fold enhancement in the rate constant and a two-order-of-magnitude improvement in sensitivity compared to conventional catalytic hairpin assembly (CHA). By integrating an allosteric probe with EACHA, a one-pot method is developed for rapid and direct detection of S. enterica Enteritidis (S. Enteritidis). This method is capable of detecting 15 CFU mL−1 of S. Enteritidis within 20 min, which is superior to that of real-time PCR. By testing 60 milk samples, we demonstrate this method's high accuracy in discriminating contaminated samples, with an area under the curve (AUC) of 0.997. Moreover, this method can be employed to accurately diagnose early-stage infected mice, with an AUC of 1.00 for feces samples and 0.986 for serum samples. Therefore, this study offers a simple and feasible method for identifying pathogens in complex matrices.

STREAMLINING DIGITAL CORRELATION-INTERFEROMETRIC DIRECTION FINDING WITH SPATIAL ANALYTICAL SIGNAL

This study investigated a search-free digital method for radio direction-finding that utilizes spatial analytical signal reconstruction. The research focused on optimizing the method's accuracy for estimating the direction of a radio source. The analysis identified the separation distance and number of chosen antenna elements for signal reconstruction as the key parameters influencing accuracy. Analytical optimization and simulations were employed to determine the optimal values for these parameters. The research successfully modeled the impact of antenna element selection on direction-finding errors under specific signal-to-noise conditions. This model can be used to guide further development and optimization of this radio direction-finding method.