Verification Of Accuracy Research Articles

Investigation of risk factors and development and validation of a nomogram for predicting overall survival in breast invasive lobular carcinoma

Objective: Invasive lobular carcinoma (ILC) of breast is a common pathological subtype of breast cancer, ranks second in terms of incidence rate following invasive ductal carcinoma. The aim of this study is to construct a nomogram for predicting overall survival (OS) in patients with ILC and to identify risk factors that affect their survival prognosis. Methods: The patients diagnosed with ILC between 2010 and 2015 were extracted from Surveillance, Epidemiology, and End Results (SEER) database. They were randomly split into a training set with 18365 samples for model training and parameter tuning, and a validation set with 7872 samples for independent accuracy verification. The independent risk factors were screened by Lasso regression and multivariable Cox regression. A nomogram was constructed for the 3-year, 5-year, and 10-year overall survival rates based on these independent risk factors. Model efficiency was assessed through Harrell’s concordance index (C-index), Calibration curves, Receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results: A total of 26,237 patients diagnosed with ILC were include. The following factors were identified as independent risk factors associated with OS: Age, Marital status, Grade, Estrogen receptor (ER), Progesterone receptor (PR), Surgery, Radiation therapy, and Tumor size(T), Lymph Node (N), and Metastasis (M) stages. The C-index was 0.795 in the training set, while in the validation set it was 0.791. The corresponding Area Under Curves (AUC) for 3-year, 5-year, and 10-year were 0.837, 0.828, and 0.791 in the training set, and 0.832, 0.826 and 0.781 in the validation set, respectively. The calibration curve of the nomogram showed good consistency, and the DCA curves also suggested that it can provide valuable guidance for clinical decision making. Conclusions: The established nomogram predicting 3-year, 5-year, and 10-year OS for patients with ILC showed a good performance and it can help clinicians make more favorable clinical decisions.

Clinical application standard of dynamic navigation technology in implant surgery

Dynamic navigation technology can "real-time guide" the implantologist to place the implant in the alveolar bone of the missing tooth area according to the preoperative design of the optimal site and path, making the whole implant surgery process more safe and precise. In order to further promote the standardized application of oral implant dynamic navigation technology, China Association of Gerontology and Geriatrics has convened distinguished experts to engage in deliberations and develop the standard. This standard covers the basic requirements, indications and contraindications, operation procedures, common complications and treatment measures, and accuracy verification. This standard can be used as a reference for the use of dynamic navigation technology in implant surgery.

Decentralized worker-centred occupational management in health care: nationwide survey and alpha testing.

Occupational stress among healthcare workers negatively impacts job satisfaction and patient care quality, jeopardizing healthcare system sustainability. Traditional employer-driven approaches often fail to address these challenges comprehensively, leading to persistent gaps in work condition transparency and well-being. To elucidate the working conditions of health workers and introduce a worker-centred, technology-based strategy moving beyond traditional practices and entrenched medical culture. A nationwide survey of Belgian medical residents evaluated occupational conditions and perceptions of management practices. Additionally, the alpha version of a decentralized mobile application was tested to gather user satisfaction and feedback on its usability. The data were surveyed using Pearson's chi-squared and Kruskal-Wallis rank sum tests to assess associations between categorical and ordinal variables, respectively. Alpha-testing results were evaluated using descriptive statistics. The nationwide survey, involving 257 participants, revealed significant associations between medical specialty, work choices and compensation. Notably, 91% of participants expressed strong interest in our proposed open, decentralized solution. In the alpha testing phase, 12 testers reported high satisfaction regarding time-tracking accuracy and payroll verification, though challenges related to administrative burden were also identified. The findings underscore the need for innovative, worker-centred occupational management solutions. The proposed solution shows promise in improving autonomy and transparency, potentially enhancing healthcare delivery and sustainability.

Large Language Models as Evaluators in Education: Verification of Feedback Consistency and Accuracy

The recent advancements in large language models (LLMs) have brought significant changes to the field of education, particularly in the generation and evaluation of feedback. LLMs are transforming education by streamlining tasks like content creation, feedback generation, and assessment, reducing teachers’ workload and improving online education efficiency. This study aimed to verify the consistency and reliability of LLMs as evaluators by conducting automated evaluations using various LLMs based on five educational evaluation criteria. The analysis revealed that while LLMs were capable of performing consistent evaluations under certain conditions, a lack of consistency was observed both among evaluators and across models for other criteria. Notably, low agreement among human evaluators correlated with reduced reliability in LLM evaluations. Furthermore, variations in evaluation results were influenced by factors such as prompt strategies and model architecture, highlighting the complexity of achieving reliable assessments using LLMs. These findings suggest that while LLMs have the potential to transform educational systems, careful selection and combination of models are essential to improve their consistency and align their performance with human evaluators in educational settings.

Evaluation of dose calculation method with a combination of Monte Carlo method and removal-diffusion equation in heterogeneous geometry for boron neutron capture therapy.

Clinical research in boron neutron capture therapy (BNCT) has been conducted worldwide. Currently, the Monte Carlo (MC) method is the only dose calculation algorithm implemented in the treatment planning system for the clinical treatment of BNCT. We previously developed the MC-RD calculation method, which combines the MC method and the removal-diffusion (RD) equation, for fast dose calculation in BNCT. This study aimed to verify the partial-MC-RD calculation method, which utilizes the MC-RD calculation method for a portion of the entire neutron energy range, in terms of calculation accuracy and time as the dose calculation method. We applied the partial-MC-RD calculation method to calculate the total dose for head phantom, comprising soft tissue, brain tissue, and bone. The calculation time and accuracy were evaluated based on the full-MC method. Our accuracy verifications indicated that the partial-MC-RD calculation was mostly comparable with full-MC calculation in the accuracy. However, the assumptions and approximation used in the RD calculation mainly occurred the discrepancy from the full-MC calculation result. Additionally, the partial-MC-RD calculation reduced the time required to approximately 45% for the irradiation to the top and cheek region of head phantom, compared to the full-MC calculation. In conclusion, the MC-RD calculation method can be the basis of a fast dose calculation method in BNCT.

Robust Finger Vein Presentation Attack Detection Using XceptionNet-based Modified Depthwise Separable Convolutional Neural Network

Finger vein presentation attack detection (FVPAD) biometric systems have seen substantial enhancements through the application of deep learning convolutional neural networks (DCNN). This advancement led to increased complexity, parameters and resource requirements. To address these challenges, a novel modification to the first entry flow of the XceptionNet architecture based on customized depthwise separable convolution (DSC) CNN-based for extracting robust features from FV images to detect spoofing attacks is proposed in this paper. The proposed approach stands out for its simplicity in design, fewer parameters, reduced computational load, minimal resource and equipment needs, and minimum data overflow while maintaining high accuracy in verification and classification tasks. The developed FVPAD system includes FV image data preprocessing and augmentation, a modified XceptionNet architecture based on DSC to deeply extract robust features. Finally, the fully connected (FC) layers exclusively use the SoftMax activation function to normalize, predict and classify output classes. The model was evaluated on cropped FV images from the IDIAP and SCUT-SFVD datasets, achieving high accuracy rates of 100% and 99.499%, respectively. It also has the lowest number of trainable parameters at 131,106 acquired from fifteen convolutional and depth-separable convolution layers.

Practical Research on Machine Vision Technology in Automated Inspection of Precision Components in Intelligent Manufacturing

This study develops a stereoscopic vision system using a two-camera calibration method and BP neural networks combined with genetic algorithms to measure precision component dimensions. Images are processed using edge detection and Hough transform algorithms, and a machine vision-based inspection model is constructed. Bearing components are used as the research object to detect dimensions, edges, geometric parameters, and loose components under six angles. Maximum measurement deviation is 0.04 mm, and edge detection results are clear and concise. Geometric parameter deviations remain within [-5%, 5%], achieving high recognition accuracy. The detection model’s classification accuracy is 97.49%, with verification accuracy at 98.01%. Comprehensive false detection and leakage rates are 1.03% and 0.46%, respectively. The model demonstrates superior detection performance across various angles for bearing components.

Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning.

Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards.

Verification of normalized online signatures without calculating dynamic features

Objectives. Study of the method of verification of the authenticity of a human signature made on a tablet with a stylus and given three parameters: coordinates X, Y and pressure on the tablet P.Methods. N genuine dynamic human signatures are given. Data describing different signatures made by one person always have a different number of points. The main variants of normalization of the original signature data are investigated. A model of an individual image of human signatures is built without calculating dynamic features. The method of dynamic time transformation (DTW) is used to compare similar data of different signatures. The results of this transformation are DTW-distances between the data of pairs of signatures. These distances serve as coordinates of a point in the feature space describing the similarity of a pair of signatures. A set of such points represents a model describing the similarity of genuine human signatures. The parameters of the signature being verified are compared with each of the N authentic signatures used to build the model for their proximity to the model. If more than half of these pairs are further from the model than a certain threshold T, the signature is considered fake.Results. As a result of comparative experiments, a variant of normalization of initial data of dynamic human signatures was found, which allows verification of such signatures without calculating additional features usually calculated from the initial data X, Y, P.Conclusion. Experiments to generate individual signature models for each of 230 people from the publicly available MCYT dynamic signature database (a subset of the DeepSignDB database) and verify the authenticity of 11,500 signatures made on behalf of these people showed a verification accuracy of 99.82 %. Half of them were genuine, half were fake.

A Survey: Handwritten Signature Verification Using Different Methods

Nowadays, with the development of correspondence via the Internet and the crises that occurred after 2019, many institutions in various fields, whether public or private, and banks and financial transactions, began to carry out most of them via the Internet and needed to sign various documents electronically. There are many problems with the signature images, documents, transactions, or files that make the verification process difficult or require preprocessing for signature. One important part of biometric authentication is signature verification, which tries to separate genuine signatures from fraud. Both online and offline signature verification techniques are examined in this survey. In offline systems, properties like size, shape, and texture are extracted for comparison from static representations of signatures. On the other hand, online systems collect dynamic data such as pen pressure, acceleration, and velocity. This paper gives a comprehensive assessment of the current studies and outcomes in the last 8 years in the subject of online and offline handwritten signature verification. It is possible to improve verification accuracy by creating a hybrid system that combines the advantages of both methods. This study investigates cutting-edge strategies to enhance the functionality of signature verification systems, such as machine learning algorithms and feature extraction techniques. The ultimate objective is to create dependable and strong systems that can successfully thwart intricate forgery efforts. Whereas the accuracy in most offline research may reach 100%, and this may be mostly due to the fact that the matching is of one-to-one type, while in online methods is one-to-many. Index Terms— Online signature verification, offline signature verification, genuine, forgeries.

Assessing path accuracy in industrial robots via ballbar technology

Purpose This paper aims to present a methodology for evaluating the path accuracy of industrial robots using the telescoping ballbar measurement technology. The goal is to improve accuracy assessments in precision-driven manufacturing processes. Design/methodology/approach A single telescoping ballbar is used to assess the circle contouring performance of a KUKA KR210 R2700 prime robot. Experiments involve system setup, data collection and analysis in Matlab to derive performance metrics such as radial deviation, circularity and path accuracy error. This study investigates the impact of varying the operational conditions, including speed, payload and robot configuration, on these indexes through statistical analysis, and examines the relationship between joint errors and path deviations. Findings The results indicate that the robot behavior is influenced by the operating conditions, with notable error spikes at joint reversal positions due to factors such as joint backlash and transmission errors. This study evaluates various performance indexes from different standards, ISO 230 and ISO 9283, and identifies key operating parameters influencing each index. The findings suggest effective strategies for error compensation and performance enhancement. Originality/value This paper offers a novel approach to path accuracy verification and error source identification in industrial robots. It proposes methods to rapidly assess the correlation between performance and operating conditions, offering insights for better calibration and control strategies, especially in high-precision tasks.

Numerically predicting deflections of mat-sand interaction model under gravity loadings

This research proposes a novel mat-sand interaction model to evaluate limiting values of sand elastic modulus and super-imposed gravity load for a specific mat thickness to control vertical deflection of mat by using BNBC 2020 prescribed allowable deflection limit. For this reason, the linear elastic analysis is performed by using finite element-based software ETABS v.18.1.1, whereas the impact of sand on the mat is transferred by springs. The difference in results between present and previous studies is obtained to be 1.76%, which may indicate good accuracy of verification of the proposed model with the field study data. The difference in mat vertical deflections between two consecutive sand elastic moduli is obtained to be 7.1%, and it is found to be 32.8% in the case of the mat thickness variations. So, the difference in the case of the sand elastic modulus is reduced by 78.4% compared to the mat thickness. The limiting value of imposed gravity load is found to be 60 kPa for 150 mm mat thickness with a range of sand elastic modulus of (5–25) MPa. Similarly, it is obtained to be 120 kPa in the case of the 200 mm mat thickness for 5 MPa elastic modulus of sand. These findings may help engineers to select suitable mat thickness with corresponding sand elastic modulus and super-structure gravity load.

Efficient Model Verification at Runtime through Adaptive Dynamic Approximation

In dynamic environments, safety-critical autonomous systems must adapt to environmental changes without violating safety requirements. Model verification at runtime supports adaptation through the periodic analysis of continually updated models. A major limitation of the technique is the high overhead associated with the regular analyses of large state-space models. Our paper introduces an adaptive approximation strategy that tackles this limitation by delaying unnecessary model updates, significantly reducing the overheads of these analyses. The strategy is applicable to Markov decision processes (MDPs) and is partitioned into components that can be analyzed independently and approximately. Each component is assigned a priority that depends on its impact on the accuracy of verification, and only the highest-priority components affected by changes are scheduled for updating/approximating. A complete update and verification of the entire model is triggered infrequently when the accuracy drops below a predefined threshold. We provide theoretical guarantees and proofs which ensure that our strategy can be applied without impacting the overall safety of the verified autonomous system. The experimental results from a case study in which we applied the strategy to a rescue robot team show that it is fully robust against safety-critical errors and can achieve a decision accuracy of over 97%.

Accuracy verification of protoacoustic measurements in a heterogeneous phantom by an optical hydrophone.

Protoacoustics has emerged as a promising real-time range measurement method for proton therapy. Optical hydrophones (OHs) are considered suitable to detect protoacoustic waves owing to their ultracompact size and high sensitivity. In our previous research, we demonstrated that the time-of-arrival (TOA) measured by an OH showed good agreement with the simulated ground truth in a homogeneous medium. The purpose of the study was to experimentally evaluate the accuracy of the TOA and compression peak pressures detected by the OH. Protoacoustic waves that undergo the typical distortions occurring in the human body were investigated. In such cases, the use of small detectors such as OHs is desirable to minimize the effects of detector size and directivity. A 100-MeV proton pencil beam emitted from a fixed-field alternating gradient accelerator was irradiated onto a homogeneous water phantom and a water phantom with a half- or full-sized silicone plate downstream of the Bragg peak (BP) or a bone plate that covered half of the beam cross-section in the beam path. The OH was shifted 70mm laterally across the beam axis downstream of the BP to measure the protoacoustic waves. The k-WAVE acoustic wave transport simulation was employed as the ground truth. The TOA and the first compression peak pressures were compared between the simulation and experiment. The TOA deviation against the ground truth was primarily attributed to alignment errors of the measurement devices and phantoms, with deviations of<1mm. The peak pressure distribution closely resembled the ground truth, with FWHM differences of 0.0%-3.0% for the tested geometries. The OH was able to determine the TOA and peak pressures with sufficient accuracy in heterogeneous phantoms, even without considering the effect of the size of the detector or directivity on the measurements.

MODELING INFLUENCE OF URBAN SPRAWL ON URBAN HEAT ISLAND (UHI) ACTIVITY IN KOLAKA REGENCY

The expansion of urban areas into rural regions, known as Urban Sprawl, contributes to the Urban Heat Island (UHI) phenomenon, where urban areas exhibit higher temperatures than their rural counterparts. Kolaka Regency is an area with significant potential for Urban Sprawl and subsequent UHI activity. Therefore, it is essential to investigate the impact of Urban Sprawl on UHI in the Kolaka region. This study simulates these changes using the Weather Research and Forecasting (WRF) model, incorporating land cover data from the Geospatial Information Agency (BIG) with four urban schemes: SLUCM, BEM, and the default non-UCM/land cover. The simulation also includes the Kolaka 2042 development map for Urban Sprawl projections. Simulations were conducted over 48 non-rainfall events across 12 months. The results indicated that the WRF BIG-BEM model demonstrated the highest verification accuracy and the lowest errors, with a MAPE of 4.70%, CRMSE of 1.06°C, and a correlation coefficient of 87.62%. Including BIG land cover and the BEM urban scheme enhanced the model's performance, with a MAPE of 17.92%, CRMSE of 10.11%, and a correlation improvement of 3.18%. The UHI effect predominantly ranged from -2.0 to 2.5°C, with the highest values observed in the Pomalaa mining area and central Kolaka Regency. The UHI effect was most pronounced from evening to morning, peaking during the night and early morning hours, with increased intensity during the dry season from July to September. Regression analysis revealed a trend of increasing UHI following Urban Sprawl activity, with a trend rate of 0.91°C. The R-squared value of 96.69% indicates that Urban Sprawl activity accounted for 96.69% of the UHI intensity in Kolaka, while other unexamined variables influenced 3.31%.

A Bibliometric Analysis of English Teaching in Inclusive Education Using Scopus Data

As the need for equality in education is fundamental, this study conducts a comprehensive bibliometric analysis of research on English Teaching in Inclusive Education (ETIE) indexed in Scopus from 1997 to 2023. The aim is to systematically examine the scope and trends within ETIE literature, identifying gaps and opportunities for future research. Utilizing a methodical approach for 201 Scopus-indexed publications, the study progresses through four distinct phases: initial database search using Publish or Perish, rigorous filtering of publications based on predefined bibliometric criteria, verification of data accuracy with Mendeley Desktop, and detailed analysis employing VOSviewer. The findings reveal a concerning decline in ETIE publications over time, suggesting a waning academic interest in this crucial area of study. Despite the decrease in publications, the analysis highlights several key areas that remain underexplored and could significantly benefit from further scholarly attention. The study underscores the importance of increased research collaboration and the adoption of more inclusive education practices in English teaching. Implications of this research are far-reaching, offering critical insights for educators, researchers, and policymakers. By providing a clearer understanding of the current landscape of ETIE research, the study paves the way for enhancing educational practices and supports the development of more inclusive educational environments. This will, in turn, contribute to the broader goal of ensuring equitable education access for all students, particularly those within diverse learning contexts.

Simulation study of the effects of different materials used in the core of a TSV inductor on its behavior when integrated into a DC/DC converter

This research focuses on the design and modeling of a Through-Silicon via (TSV) inductor, tailored for integration within an interleaved boost converter. The TSV inductor features a winding structure designed with vertical TSVs linked to horizontal rectangular connections, creating an efficient topology. A comprehensive electrical model has been developed to account for all parasitic effects arising from the various physical components of the TSV inductor. MATLAB software was employed to analyze the influence of different materials used in the construction of the TSV inductor, with the goal of optimizing its inductance values and quality factor. This approach involved selecting materials that minimize energy losses, thereby enhancing the overall performance of the inductor. To validate the electrical parameters derived from the physical characteristics and material properties, the TINA simulation software was used. It simulated the interleaved boost converter circuit incorporating the TSV inductor’s equivalent electrical circuit for accuracy verification.

Theoretical advances in the smagorinsky model for turbulence simulations

This article proposes significant theoretical enhancements to the Smagorinsky model for turbulence simulations, presenting new theorems with complete mathematical proofs to increase the precision and efficiency of these simulations. Acknowledging the limitations of the original model, this work addresses these challenges through a rigorous analysis in Sobolev spaces and the filtered Navier-Stokes equations, which are essential for subgrid-scale simulation development. The study begins by introducing conditions of existence and uniqueness for weak solutions in the modified model, based on methods such as the Galerkin technique, which facilitates the analysis of behavior and stability under small initial perturbations. The application of these techniques enables a detailed verification of the model's accuracy in different turbulent flow scenarios. To validate the theoretical advancements, computational simulations are implemented to compare the performance of the original and modified Smagorinsky models. The results of these simulations demonstrate a significant improvement in both accuracy and computational efficiency, with the enhanced model achieving greater precision with less processing demand. These theoretical and numerical advancements in the Smagorinsky model open new perspectives for the use of turbulence simulations in various fields of engineering and applied sciences, allowing for more detailed analyses and more reliable predictions of complex turbulent phenomena. Thus, this work contributes to advancing the state of the art in turbulence modeling, providing a solid foundation for future research and practical evelopments in the field.

Verification of daily dose recalculation accuracy for an adaptive radiation therapy monitoring tool in helical tomotherapy for nasopharyngeal carcinoma.

PreciseART, an adaptive radiation therapy (ART) software for helical tomotherapy (HT), was integrated into the Precision treatment planning system (Accuray, Inc., Sunnyvale, CA). It supports automatic monitoring of dose variations to both the target and organs at risk (OARs) throughout the treatment course. This study aims to verify the accuracy of PreciseART's automatic dose recalculation and assess the effectiveness of its notification function. The Radixact X9's kVCT image-guided system (ClearRT) was used to acquire daily images for dose recalculations. We assessed the accuracy of PreciseART's automatic dose recalculation by comparing it with the treatment planning system (TPS) recalculation for the PTV70, spinal cord, and bilateral parotid glands. We also evaluated the efficacy of its notification function by comparing each dose metric for each notification color to TPS recalculation, assessing its role as a trigger tool. In the phantom study, dosimetric analysis indicated no statistically significant differences between TPS and PreciseART recalculations (p>0.05), with dose variations under 1.5%. Similarly, in the patient study (n=10), no significant dosimetric discrepancies were found (p>0.05), with a maximum variation of 2.3%. The notification system performed effectively, providing accurate notifications in accordance with predefined dose criteria. PreciseART's daily dose recalculation demonstrated good agreement with TPS recalculation, and its notification function is effective for identifying dose threshold compliance, supporting its use in clinical ART.

Comparative evaluation of two dose-volume histogram prediction tools for treatment planning: Treatment planning quality and dose verification accuracy

Comparative evaluation of two dose-volume histogram prediction tools for treatment planning: Treatment planning quality and dose verification accuracy