Data Preparation Time Research Articles

Automatic bridge inspection database construction through hybrid information extraction and large language models

Regular bridge inspections generate extensive reports that, while critical for maintenance, often remain underutilized due to their unstructured format. Traditional information extraction methods depend on intricate labeling systems that commonly require time-consuming and labor-intensive labeling. This paper presents a novel bridge inspection database construction method leveraging LLM-assisted information extraction. First, we introduce the pseudo-labelling method using a closed-source LLM to generate high-quality data. Then we propose the hybrid extraction pipeline to extract relevant information segments and process them by a generation-based IE model, fine-tuned on pseudo-labeled data. Finally, the extracted data is used to construct the bridge inspection database. The proposed method, validated with real-world data, not only demonstrates higher extraction precision than the closed-source LLM used for pseudo-labeling but also outperforms traditional methods in both data preparation time and extraction accuracy. This approach provides a scalable solution for more proactive and data-driven bridge maintenance strategies.

Self-supervised learning on MeerKAT wide-field continuum images

Context. Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in models that are adaptable to many different tasks. Until now, applications of SSL to astronomical images have been limited to Galaxy Zoo datasets, which require a significant amount of preprocessing to prepare sparse images centered on a single galaxy. With wide-field survey instruments at the forefront of the Square Kilometer Array (SKA) era, this approach to gathering training data is impractical. Aims. We demonstrate that continuum images from surveys such as the MeerKAT Galactic Cluster Legacy Survey (MGCLS) can be successfully used with SSL, without extracting single-galaxy cutouts. Methods. Using the SSL framework DINO, we experimented with various preprocessing steps, augmentations, and architectures to determine the optimal approach for this data. We trained both ResNet50 and Vision Transformer (ViT) backbones. Results. Our models match state-of-the-art results (trained on Radio Galaxy Zoo) for FRI/FRII morphology classification. Furthermore, they predict the number of compact sources via linear regression with much higher accuracy. Open-source foundation models trained on natural images such as DINOv2 also excel at simple FRI/FRII classification; the advantage of domain-specific backbones is much smaller models trained on far less data. Smaller models are more efficient to fine-tune, and doing so results in a similar performance between our models, the state-of-the-art, and open-source models on multi-class morphology classification. Conclusions. Using source-rich crops from wide-field images to train multi-purpose models is an easily scalable approach that significantly reduces data preparation time. For the tasks evaluated in this work, twenty thousand crops is sufficient training data for models that produce results similar to state-of-the-art. In the future, complex tasks like source detection and characterization, together with domain-specific tasks, ought to demonstrate the true advantages of training models with radio astronomy data over natural-image foundation models.

Data as infrastructure: Systematic data curation addressing fundamental data content differences across the UK

Objective and ApproachHealth Data Research UK, the UK national institute for health data science, is coordinating efforts alongside national academic partners to streamline data curation at disease, population, and data structure level to enhance data offerings and provide networked data infrastructure supporting whole-UK research. Due to clinical, coding, and system differences across the constituent countries of the UK, data is often not standardised for whole-UK analyses, creating burden on research teams and leading to long data preparation times in order to run even distributed analyses. The approach to solve this is multi-faceted, including deploying data curation and cohort creation algorithms into health data providers’ environments, and through the novel integration of federated analytics solutions (such as those piloted through recent national infrastructure programmes) improving data access and research deployment efficiency. ResultsStandardising data through clinical and structural data curation directly deployed to health data providers creates a framework for whole-UK studies to be readily achievable, and provide the data infrastructure base to integrate new technical federated analytics solutions to deploy and reproduce analytics without unnecessary large scale data migration. Conclusions and ImplicationsSystematic curation of health data within national data providing organisations provides flexibility and choice for researchers in terms of the data they will apply for to answer vital research questions affecting the UK populace. Such advances will improve the quality and efficiency of research for all corners of the UK, and create a community of practice in terms of developing data from health systems to research environments.

Direct slicing of microcellular structures for digital light processing (DLP) additive manufacturing

PurposeAdditive Manufacturing (AM) conventionally necessitates an intermediary slicing procedure using the standard tessellation language (STL) data, which can be computationally burdensome, especially for intricate microcellular architectures. This study aims to propose a direct slicing method tailored for digital light processing-type AM processes for the efficient generation of slicing data for microcellular structures.Design/methodology/approachThe authors proposed a direct slicing method designed for microcellular structures, encompassing micro-lattice and triply periodic minimal surface (TPMS) structures. The sliced data of these structures were represented mathematically and then convert into 2D monochromatic images, bypassing the time-consuming slicing procedures required by 3D STL data. The efficiency of the proposed method was validated through data preparations for lattice-based nasopharyngeal swabs and TPMS-based ellipsoid components. Furthermore, its adaptability was highlighted by incorporating 2D images of additional features, eliminating the requirement for complex 3D Boolean operations.FindingsThe direct slicing method offered significant benefits upon implementation for microcellular structures. For lattice-based nasopharyngeal swabs, it reduced data size by a factor of 1/300 and data preparation time by a factor of 1/8. Similarly, for TPMS-based ellipsoid components, it reduced data size by a factor of 1/60 and preparation time by a factor of 1/16.Originality/valueThe direct slicing method allows for bypasses the computational burdens associated with traditional indirect slicing from 3D STL data, by directly translating complex cellular structures into 2D sliced images. This method not only reduces data volume and processing time significantly but also demonstrates the versatility of sliced data preparation by integrating supplementary features using 2D operations.

Does emotional valence affect cognitive performance and neurophysiological response during decision making? A preliminary study.

This study investigated the impact of the emotional valence of external situations (EVES) on cognitive performance and electrophysiological (EEG) responses during decision-making. 26 healthy adults underwent a modified version of the Trier social stress test, performing five interview-style discourses. Each discourse entailed preparing a speech under increasingly stressful conditions. Participants were also exposed to gradually increasing EVES (i.e., an examining committee displaying progressively more negative-connoted emotional facial expressions). In addition, after each speech, participants completed an arithmetic task to test how emotional manipulation affected cognitive performance. Behavioral data (preparation times) and EEG data (frequency bands) were collected to assess stress regulation, stress resilience, and cognitive performance. The results indicate that EVES significantly influenced stress regulation and resilience, as reflected in the behavioral data. Neurophysiological findings showed increased parietal lobe activity (P4) in the theta and delta bands with rising emotional valence, plateauing from the preparation of the second discourse onward. This suggests enhanced emotional processing and attentional demands. However, gamma band activity decreased in P4 during the preparations for the two discourses following the first, indicating a shift of cognitive resources from higher cognitive functions to emotional processing. This highlights the cognitive cost of maintaining performance and stress regulation under emotionally charged conditions. Such findings suggest that emotional valence modulates cognitive performance and that specific neural mechanisms are involved in managing stress responses. The findings underscore the complex relationship between emotion, cognition, and neural mechanisms, offering valuable insights for stress regulation and resilience, and enhancing performance under pressure.

Representation of few-group homogenized cross section by multi-variate polynomial regression

In this paper, a representation of few-group homogenized cross section by multi-variate polynomial regression is presented. The method is applied on the few-group assembly homogenized cross sections of the assembly 22UA from the benchmark X2VVER[1], generated by the lattice transport code APOLLO3®[2], and conducted over a Cartesian grid of parametric state-points. The regression model [3, 4] allow to input a significantly larger number of points for training compared to the number of monomials, thus yielding higher accuracy than polynomial interpolation without being affected by the choice of points in the training set. Additionally, it can reduce data preparation time because the size of the training set can be smaller than the number of points in the complete Cartesian grid, while still providing a good approximation. Furthermore, its evaluation algorithm can be adapted for GPU utilization, similar to polynomial interpolation with the Newton method [5].

Trends in the development of digital subscription services in international markets

Companies are trying to harness the potential of the current stage of development of the digital industry and make it cost-effective, which requires not only technological innovation but also the transformation of many existing business models. Based on an analysis of examples of digital subscription companies, this article provides additional explanations, including definitions and constituent characteristics of digital subscription business models. The aim of the article was to identify trends in the development of digital subscription services by analysing the number of active subscribers and the impact of COVID-19 on this indicator. Autoregression and analysis methods were used in the study, and the implementation of the selected models was carried out using the Python-3 programming language. The article describes a theoretical basis that determine the feasibility of using the subscription model for the company under non-deterministic critical conditions. The main approaches to forecasting the company’s performance indicators are identified. The type of data to be forecasted is determined, and the necessity of using autoregressive models for further analysis is declared. Factors associated with the COVID-2019 incidence that should be considered as exogenous variables were identified. The multicriteria selection problem was constructed by restricting the family of autoregressive models and determining the criteria for forecasting and data preparation time, forecast accuracy, and the possibility of considering external factors in determining the algorithm efficiency. It is found that moving average autoregression is the most effective. Given the further application of this algorithm to predict the number of subscribers, the expediency of implementing a subscription strategy for the company under non-deterministic critical conditions is declared. It is possible to effectively apply the selected mathematical model to predict the performance of a subscription company. The practical significance of the work lies in the possibility of creating certain benchmarks for the development of the market and a business strategy for product promotion, which will be based on an understanding of the potential benefits and threats of the market for digital subscription products and services

Harnessing Machine Learning in Tackling Domestic Violence—An Integrative Review

We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.

Research-ready data: the C-Surv data model

Research-ready data (data curated to a defined standard) increase scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following stakeholder consultation, a standard data model (C-Surv) optimised for data discovery, was developed using data from 5 population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. Data preparation times were compared between cohort specific data models and C-Surv.It was concluded that adopting a common data model as a data standard for the discovery and analysis of research cohort data offers multiple benefits.

Data augmentation in extreme ultraviolet lithography simulation using convolutional neural network

BackgroundIn the previous work, we developed a convolutional neural network (CNN), which reproduces the results of the rigorous electromagnetic (EM) simulations in a small mask area. The prediction time of CNN was 5000 times faster than the calculation time of EM simulation. We trained the CNN using 200,000 data, which were the results of EM simulation. Although the prediction time of CNN was very short, it took a long time to build a huge amount of the training data. Especially when we enlarge the mask area, the calculation time to prepare the training data becomes unacceptably long.AimReducing the calculation time to prepare the training data.ApproachWe apply data augmentation technique to increase the number of training data using limited original data. The training data of our CNN are the diffraction amplitudes of mask patterns. Assuming a periodic boundary condition, the diffraction amplitudes of the shifted or flipped mask pattern can be easily calculated using the diffraction amplitudes of the original mask pattern.ResultsThe number of training data after the data augmentation is multiplied by 200 from 2500 to 500,000. Using a large amount of training data, the validation loss of CNN was reduced. The accuracy of CNN with augmented data is verified by comparing the CNN predictions with the results of EM simulation.ConclusionsData augmentation technique is applied to the diffraction amplitude of the mask pattern. The data preparation time is reduced by a factor of 200. Our CNN almost reproduces the results of EM simulation. In this work, the mask patterns are restricted to line and space patterns. It is a challenge to build several CNNs for specific mask patterns or ultimately a single CNN for arbitrary mask patterns.

Web-based Exploration of Annotated Multi-Layered Relightable Image Models

We introduce a novel approach for exploring image-based shape and material models registered with structured descriptive information fused in multi-scale overlays. We represent the objects of interest as a series of registered layers of image-based shape and material data. These layers are represented at different scales and can come out of a variety of pipelines. These layers can include both Reflectance Transformation Imaging representations, and spatially varying normal and Bidirectional Reflectance Distribution Function fields, possibly as a result of fusing multi-spectral data. An overlay image pyramid associates visual annotations to the various scales. The overlay pyramid of each layer is created at data preparation time by either one of the three subsequent methods: (1) by importing it from other pipelines, (2) by creating it with the simple annotation drawing toolkit available within the viewer, and (3) with external image editing tools. This makes it easier for the user to seamlessly draw annotations over the region of interest. At runtime, clients can access an annotated multi-layered dataset by a standard web server. Users can explore these datasets on a variety of devices; they range from small mobile devices to large-scale displays used in museum installations. On all these aforementioned platforms, JavaScript/WebGL2 clients running in browsers are fully capable of performing layer selection, interactive relighting, enhanced visualization, and annotation display. We address the problem of clutter by embedding interactive lenses. This focus-and-context-aware (multiple-layer) exploration tool supports exploration of more than one representation in a single view. That allows mixing and matching of presentation modes and annotation display. The capabilities of our approach are demonstrated on a variety of cultural heritage use-cases. That involves different kinds of annotated surface and material models.

Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications

The crop water stress index (CWSI) is one of the parameters measured in deficit irrigation and it is obtained from crop canopy temperature. However, image segmentation is required for non-leaf region exclusion in temperature measurement, as it is critical to obtain the temperature values for the calculation of the CWSI. To this end, two image-segmentation models based on support vector machine (SVM) and deep learning have been studied in this article. The models have been trained with different parameters (encoder depth, optimizer, learning rate, weight decay, validation frequency and validation patience), and several indicators (accuracy, precision, recall and F1 score/dice coefficient), as well as prediction, training and data preparation times are discussed. The results of the F1 score indicator are 83.11% for SVM and 86.27% for deep-learning models. More accurate results are expected for the deep-learning model by increasing the dataset, whereas the SVM model is worthwhile in terms of reduced data preparation times.

A Novel and Efficient Hybrid RSILU Preconditioner for the Parallel GMRES Solution of the Coarse Mesh Finite Difference Equations for Practical Reactor Simulations

The solution of the large, nonsymmetric, sparse linear system resulting from the discretization of the Boltzmann transport equation can be computationally intensive, even when innovative coarse mesh finite difference (CMFD) acceleration methods are applied on a high-performance computer. The research reported here describes the development and implementation of an innovative hybrid Reduced Symmetric Successive Over-Relaxation (RSOR) and Incomplete Lower Upper (ILU) (RSILU) preconditioner for the preconditioned Generalized Minimal RESidual (GMRES) solution of the three-dimensional, whole-core, pin-resolved transport calculation of a nuclear reactor core. The preconditioner was designed specifically to improve parallel computing capability and minimize the computational burden for solution of the CMFD method on a high-end computing platform. The hybrid RSILU preconditioner is applied to the preconditioned GMRES method without multicolor reordering by utilizing the ILU preconditioner for internal elements on each processor and the RSOR preconditioner for boundary elements that would require interprocessor communication. The construction of the RSILU preconditioner requires only that the diagonal elements be modified, factorized, and stored, which is identical to serial ILU. The computational cost of RSILU is minimized since the factorized diagonal block requires minimal data preparation time for interprocessor communication and exchanges information only once in parallel computation. This paper reports first the serial performance of RSOR, and numerical results show that the new proposed RSOR is an effective preconditioner even for serial computing applications. The parallel performance of RSILU is then assessed and compared to conventional multicolor ILU preconditioners. The results show that RSILU provides comparable convergence rates to the Standard Incomplete Lower Upper (SILU) preconditioners, but it is easier to implement since it does not require multicolor ordering. Although the required iterations of RSILU preconditioned GMRES increase as the number of processors increases, only slightly more iterations are required than the SILU preconditioner for a practical nuclear reactor application. The number of iterations required by the RSILU preconditioner increases only slightly and significantly less than the increased number of iterations required when using conventional Block-Jacobi Incomplete Lower Upper or Symmetric Successive Over-Relaxation preconditioners. Overall, the RSILU is shown to be an efficient and practical preconditioner for the GMRES method for improving the parallel computing performance for large-scale applications.

Semiautomated text analytics for qualitative data synthesis.

Approaches to synthesizing qualitative data have, to date, largely focused on integrating the findings from published reports. However, developments in text mining software offer the potential for efficient analysis of large pooled primary qualitative datasets. This case study aimed to (a) provide a step‐by‐step guide to using one software application, Leximancer, and (b) interrogate opportunities and limitations of the software for qualitative data synthesis. We applied Leximancer v4.5 to a pool of five qualitative, UK‐based studies on transportation such as walking, cycling, and driving, and displayed the findings of the automated content analysis as intertopic distance maps. Leximancer enabled us to “zoom out” to familiarize ourselves with, and gain a broad perspective of, the pooled data. It indicated which studies clustered around dominant topics such as “people.” The software also enabled us to “zoom in” to narrow the perspective to specific subgroups and lines of enquiry. For example, “people” featured in men's and women's narratives but were talked about differently, with men mentioning “kids” and “old,” whereas women mentioned “things” and “stuff.” The approach provided us with a fresh lens for the initial inductive step in the analysis process and could guide further exploration. The limitations of using Leximancer were the substantial data preparation time involved and the contextual knowledge required from the researcher to turn lines of inquiry into meaningful insights. In summary, Leximancer is a useful tool for contributing to qualitative data synthesis, facilitating comprehensive and transparent data coding but can only inform, not replace, researcher‐led interpretive work.

Hypersingular flux interface integral equation for multi-medium heat transfer analysis

In this paper, based on a direct method for evaluating hypersingular integrals, a heat flux interface integral equation is presented to calculate the hypersingular interface heat fluxes in multi-medium heat transfer analysis by using the interface integral boundary element method (IIBEM). The recently proposed IIBEM has a significant advantage of a single integral equation method for solving multi-medium problems, which makes up for the lack of boundary integral equations to solve multi-medium problems. Compared with conventional multi-domain boundary element method (MDBEM), the IIBEM is more efficient in input data preparation, program coding and computational time. However, a big issue encountered is the determination of interface heat fluxes in multi-medium heat transfer analysis through using IIBEM, since the derived flux interface integral equation involves hypersingular integrals on the interface, and no additional information can be utilized to recover the heat fluxes by using the in-direct “traction-recovery” methods. In this paper, based on a new efficient direct method for numerical evaluation of high order singular curved boundary integrals, the hypersingularities in flux interface integral equation are precisely evaluated, and based on the successful implementation of flux hypersingular integral equation, the interface heat fluxes are firstly calculated in the multi-medium heat transfer analysis by using IIBEM. Several two- and three-dimensional numerical examples are given to validate the correctness of the presented method.

Cap setting and allowance allocation in China's emissions trading pilot programmes: special issues and innovative solutions

Seven emissions trading scheme (ETS) pilots have been established in China. They have introduced some unique methods to set emissions caps and allocate allowances, different from textbook models and their counterparts in the EU, California, and many other regions. This article provides a detailed introduction to the methods for cap setting and allowance allocation adopted by the pilots, and presents detailed comparisons of these methods. In terms of cap setting, the pilots adopt flexible caps that can be adjusted where necessary, which primarily depends on the outcomes of the bottom-up approach, namely aggregating the allowances allocated to participants. As for allowance allocation, the pilots not only adopt such methods as grandfathering and benchmarking, which are also widely applied in other existing schemes, but also some special methods that require ex post adjustment, such as those based on enterprises’ historical emissions intensity (including both physical quantity and added-value intensity) and current production/output. The factors influencing the design are further analysed, including the impacts of theory and experience from foreign systems, concerns about economic development, traditions regarding intensity targets and policy, constraints from data availability and preparation time, tight regulation of the electricity and heat generation sector, and concerns regarding price stability. The practice of pilots presents an improvement opportunity and a challenge for China to further balance the theoretical and practical requirements in ETS design in establishing its national system.Policy relevanceChina is piloting emissions trading in seven regions, as part of efforts to try to rely more on market-based instruments to achieve GHG emissions control targets. All seven pilots have been confronted with special issues in the design process when compared with existing foreign schemes. This article analyses in depth the special issues related to cap setting and allowance allocation and the approaches adopted to address these issues. Flexible cap setting through a bottom-up approach and different types of allocation methods with or without ex post adjustment are adopted in the pilots. The flexible and innovative approaches the pilots have developed could provide useful experience for designing the nationwide ETS in China and promoting emissions trading policy in other parts of the world.

MPI–CUDA parallelization of a finite-strip program for geometric nonlinear analysis: A hybrid approach

A finite-strip geometric nonlinear analysis is presented for elastic problems involving folded-plate structures. Compared with the standard finite-element method, its main advantages are in data preparation, program complexity, and execution time. The finite-strip method, which satisfies the von Karman plate equations in the nonlinear elastic range, leads to the coupling of all harmonics. However, coupling of series terms dramatically increases computation time in existing finite-strip sequential programs when a large number of series terms is used. The research reported in this paper combines various parallelization techniques and architectures (computing clusters and graphic processing units) with suitable programming models (MPI and CUDA) to speed up lengthy computations. In addition, a metric expressing the computational weight of input sets is presented. This metric allows computational complexity comparison of different inputs.

Symbolic computation and differential quadrature method - A boon to engineering analysis

Nowadays computers can perform symbolic computations in addition to mere number crunching operations for which they were originally designed. Symbolic computation opens up exciting possibilities in Structural Mechanics and engineering. Classical areas have been increasingly neglected due to the advent of computers as well as general purpose finite element software. But now, classical analysis has reemerged as an attractive computer option due to the capabilities of symbolic computation. The repetitive cycles of simultaneous - equation sets required by the finite element technique can be eliminated by solving a single set in symbolic form, thus generating a truly closed-form solution. This consequently saves in data preparation, storage and execution time. The power of Symbolic computation is demonstrated by six examples by applying symbolic computation 1) to solve coupled shear wall 2) to generate beam element matrices 3) to find the natural frequency of a shear frame using transfer matrix method 4) to find the stresses of a plate subjected to in-plane loading using Levy`s approach 5) to draw the influence surface for deflection of an isotropic plate simply supported on all sides 6) to get dynamic equilibrium equations from Lagrange equation. This paper also presents yet another computationally efficient and accurate numerical method which is based on the concept of derivative of a function expressed as a weighted linear sum of the function values at all the mesh points. Again this method is applied to solve the problems of 1) coupled shear wall 2) lateral buckling of thin-walled beams due to moment gradient 3) buckling of a column and 4) static and buckling analysis of circular plates of uniform or non-uniform thickness. The numerical results obtained are compared with those available in existing literature in order to verify their accuracy.

Overview of the High-Level Trigger Electron and Photon Selection for the ATLAS Experiment at the LHC

The ATLAS experiment at the Large Hadron Collider (LHC) will face the challenge of efficiently selecting interesting candidate events in pp collisions at 14 TeV center-of-mass energy, whilst rejecting the enormous number of background events. The High-Level Trigger (HLT=second level trigger and Event Filter), which is a software based trigger will need to reduce the level-1 output rate of ap75 kHz to ap200 Hz written out to mass storage. In this talk an overview of the current physics and system performance of the HLT selection for electrons and photons is given. The performance has been evaluated using Monte Carlo simulations and has been partly demonstrated in the ATLAS testbeam in 2004. The efficiency for the signal channels, the rate expected for the selection, the global data preparation and execution times will be highlighted. Furthermore, some physics examples will be discussed to demonstrate that the triggers are well adapted for the physics programme envisaged at the LHC

Numerical Analysis on Steady State In-Cylinder-Flow by Partial Cells in Cartesian Coordinate Method and Verification with PIV Measurement

A computational fluid dynamics (CFD) code with partial cells in Cartesian coordinate (PCC) method has been developed. The merit of this method is that input data preparation time is extremely short because computational mesh data are separated from geometry data. However, a general Cartesian coordinate method has a big subject in calculation accuracy. One of the factors is precision in geometry expression around the complicated shape, and another is a turbulence model near the wall because mesh is independent from the boundary and not fitted with it. Therefore, discretization equations were derived based on Finite Volumetric Method, and a combination of the wall function and a low Reynolds number k-ε turbulence model was proposed here. This paper describes implementation of this method and shows the results compared with experimental data by particle image velocimetry (PIV) at the steady flow in the cylinder of a 4-valves S.I. engine. The comparison results of velocity vector maps show good agreement between CFD and PIV.