Analysis of a Prestressed Sleeper for Railway Turnout under the Effect Static Load with a Unilateral Contact Model and Support Stiffness of Existing Railway Bed of a Railway Section in Vietnam

In the process of analyzing prestressed sleepers for turnout on elastic foundations with large lengths, authors around the world often use the one-stiffness model of Winkler or the two-stiffness model of the Filonenko-Borodich and Pasternak. Railway bed stiffness is assumed to be a constant value from the design to the end of the operating process. In this study, the authors use a one-stiffness model to simulate the interaction between the prestressed sleepers for turnout and the railway bed, which helps to describe the contact and non-contact between the prestressed sleepers of turnout and the railway bed, the phenomenon that the many traditional models do not implement yet. In addition, the railway bed stiffness included in the analysis is obtained by testing at the field of a railway section in operation in Vietnam. The authors use the finite element method and combine it with the Newton iteration method. Accordingly, the analysis results of prestressed sleepers for turnout will be more consistent with actual behavior. We need to check the actual stiffness of the foundation before it is included in the analysis because the actual stiffness of the foundation gives more accurate results during analysis

Analysis of a Prestressed Sleeper for Railway Turnout under the Effect Static Load with a Unilateral Contact Model and Support Stiffness of Existing Railway Bed of a Railway Section in Vietnam

In the process of analyzing prestressed sleepers for turnout on elastic foundations with large lengths, authors around the world often use the one-stiffness model of Winkler or the two-stiffness model of the Filonenko-Borodich and Pasternak. Railway bed stiffness is assumed to be a constant value from the design to the end of the operating process. In this study, the authors use a one-stiffness model to simulate the interaction between the prestressed sleepers for turnout and the railway bed, which helps to describe the contact and non-contact between the prestressed sleepers of turnout and the railway bed, the phenomenon that the many traditional models do not implement yet. In addition, the railway bed stiffness included in the analysis is obtained by testing at the field of a railway section in operation in Vietnam. The authors use the finite element method and combine it with the Newton iteration method. Accordingly, the analysis results of prestressed sleepers for turnout will be more consistent with actual behavior. We need to check the actual stiffness of the foundation before it is included in the analysis because the actual stiffness of the foundation gives more accurate results during analysis

Oral Cancer: The State of the Art of Modern-Day Diagnosis and Treatment

Diagnosing and treating lesions of the mouth and gums is challenging for most clinicians because of the wide variety of disease processes that can present with similar appearing lesions and the fact that most clinicians receive inadequate training in mouth diseases. Oral cancer, a common lesion in oral cavity, is not correctly diagnosing a clinical picture of an early squamous cell carcinoma. The prevalence of oral cancer continues to rise worldwide, related to the increase in consumption of tobacco, alcohol and other carcinogenic products. However, there has also been a significant reduction in mortality due to increasing awareness, early diagnosis and advances in treatments. This chapter is an attempt to provide a comprehensive update encompassing the spectrum of etiologic/risk factors, current clinical diagnostic tools, management philosophies, and molecular biomarkers and progression indicators of oral cancer

Shape-dependent local strain in gold nanorods: data-driven atomic-resolution electron microscopy analysis

The local variation in inter-atomic distances, or local lattice strain often influences significantly material properties of nanoparticles. Strain measurement with ~1% precision is provided by recent atomic-resolution electron microscopy. However, the precision has been limited by noises in the experimental data. Here, we have applied one of the data-driven analyses, Gaussian process regression to predict true form of strain. The precision has been improved to be sub-percent of 0.2 % and more for detection of local strain. Rod-shaped nanoparticles have been revealed to contain characteristic lattice expansion ~+0.6 % around the subsurface cap tip area. The experimental results are reproduced by molecular dynamics simulations of the corresponding shaped atomic models. The strain peculiar to nanorods are explained in terms of curvature-dependent non-uniform surface stress due to shape anisotropy. The present results bring a hint to nanoscale engineering to optimize the strain in nanoparticles by shape control

BACH HO FIELD GEOLOGICAL FEATURES IDENTIFICATION USING WELL LOGGING DATA

Link for citation: Nguyen Xuan Kha, Pham Xuan Son, Hoang Van Quy, Nguyen Tuan, Truong Quoc Thanh, Mai Huu Xuan, Tran Van Xuan.  Bach ho field geological features identification using  well logging data. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 2023, vol. 334, no. 7, рр. 35-42. In Rus. The relevance. Well geophysics is considered as a typical method that can assist in determining the petrophysics properties of reservoirs and further location of the petroleum product-containing reservoirs. For reservoirs formed from fractured basement rock, studies on the petrophysics properties of fracture zones can contribute to the identification of petroleum products. The main aim. The study applied the cross-plotting method based on raw well-logging data to identify the possible correlation between the gamma-ray logging with well-logging including neutron porosity, sonic transient time, and bulk density in three wells (BH-433, BH-809, and BH-905) of Bach Ho field in the Cuu Long Basin, Vietnam. Methods. To deploy this study, well-logging data were integrated into formation of micro images and neutro, speed of sound, and density of the basement at the surveyed reservoir positions. Results. The results indicated that granite in the investigated zones responds to the two tight value ranges (no-oil exist zones) neutro (0,000–0,100) and speed of sound (46–64), and neutro (0,000–0,100) and density (2,375–2,750) while the value ranges deviate from 0,000–0,100, 46–64, and 2,375–2,750, respectively for neutro, speed of sound, and density are closely related to the good permeability and porosity zones (oil exist zones). Based on the findings, it can be confirmed that the cross-plotting analysis has contributed positively to the initial assessment of potential ranges of the oil reservoirs in Bach Ho field. The application of the cross-plotting method will contribute to enhancing the predictability of oil and gas in the reservoirs

A study on multi-criteria decision-making in powder mixed electric discharge machining cylindrical shaped parts

In life as well as in engineering, many times, it is necessary to choose the best option among many different options. That will be more difficult when the criteria given for the selection contradict each other. For example, when external cylindrical grinding, the minimum surface roughness requirement necessitates a small depth of cut and feed rate. The material removal rate will be reduced in this case, and this requirement will conflict with the maximum material removal rate requirement. To solve the above problem, a very useful tool is multi-criteria decision-making (MCDM). In this paper, for the first time, MCDM results for powder mixed discharge machining (PMEDM) cylindrical parts of SKD11 tool steel with copper electrodes have been presented. In this work, eighteen experiments with the L18 (16×53) design using the Taguchi method were conducted. Six main input process parameters include the powder concentration, the pulse current, the servo voltage, the pulse on time, and the pulse off time. To select an alternative that simultaneously ensures two criteria including minimum surface roughness (RS) and maximum material removal speed (MRS), four different MCDM methods including MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), TOPSIS (Technique for order of preference by similarity to ideal solution), and EAMR (Area-based Method of Ranking) and two methods of criteria weight calculation including MEREC (Method based on the Removal Effects of Criteria) and Entropy methods were selected. The results of MCDM when PMEDM SKD11 tool steel cylindrical parts with two methods for weight determination and four methods for solving MCDM problem were evaluated. In addition, the best alternative to ensure simultaneous minimum RS and maximum MRS was proposed

Antibiotic resistance of Streptococcus pneumoniae in Vietnamese children with severe pneumonia: a cross-sectional study

BackgroundStreptococcus pneumoniae is the most common bacterium that causes community-acquired pneumonia (CAP) in children. The rate of S. pneumoniae resistance to antibiotics is increasing, particularly in patients with severe CAP. Therefore, the level of antibiotic resistance of S. pneumoniae causing severe CAP in Vietnamese children requires regular monitoring.MethodsThis was a cross-sectional descriptive study. Nasopharyngeal aspiration specimens from children were cultured, isolated, and examined for S. pneumoniae. Bacterial strains were assessed for antimicrobial susceptibility, and the minimum inhibitory concentration (MIC) was determined.ResultsEighty-nine strains of S. pneumoniae were isolated from 239 children with severe CAP. The majority of isolates were completely non-susceptible to penicillin (1.1% intermediate, 98.9% resistant) and highly resistant to erythromycin (96.6%) and clarithromycin (88.8%); the rate of resistance to ceftriaxone was 16.9%, with the proportion of intermediate resistance at 46.0%; 100% of strains were susceptible to vancomycin and linezolid. For most antibiotics, MIC50 and MIC90 were equal to the resistance threshold according to the Clinical and Laboratory Standards Institute 2021; penicillin had an eight-fold increase in MIC90 (64 mg/L) and ceftriaxone had a 1.5-fold increase in MIC90 (6 mg/L).ConclusionStreptococcus pneumoniae isolates described in this study were resistant to many antibiotics. Penicillin should not be the first-line antibiotic of choice, and ceftriaxone at an enhanced dose should be used instead

Uncertainty Quantification in the Directed Energy Deposition Process Using Deep Learning-Based Probabilistic Approach

peer reviewedThis study quantifies the effects of uncertainty raised from process parameters, material properties, and boundary conditions in the directed energy deposition (DED) process of M4 High-Speed Steel using deep learning (DL)-based probabilistic approach. A DL-based surrogate model is first constructed using the data obtained from a finite element (FE) model, which was validated against experiment. Then, sources of uncertainty are characterized by the probabilistic method and are propagated by the Monte-Carlo (MC) method. Lastly, the sensitivity analysis (SA) using the variance-based method is performed to identify the parameters inducing the most uncertainty to the melting pool depth. Using the DL-based surrogate model instead of solely FE model significantly reduces the computational time in the MC simulation. The results indicate that all sources of uncertainty contribute to a substantial variation on the final printed product quality. Moreover, we find that the laser power, the convection, the scanning speed, and the thermal conductivity contribute the most uncertainties on the melting pool depth based on the SA results. These findings can be used as insights for the process parameter optimization of the DED process.EDPOM

Fast and accurate prediction of temperature evolutions in additive manufacturing process using deep learning

Typical computer-based parameter optimization and uncertainty quantification of the additive manufacturing process usually requires significant computational cost for performing high-fidelity heat transfer finite element (FE) models with different process settings. This work develops a simple surrogate model using a feedforward neural network (FFNN) for a fast and accurate prediction of the temperature evolutions and the melting pool sizes in a metal bulk sample (3D horizontal layers) manufactured by the DED process. Our surrogate model is trained using high-fidelity data obtained from the FE model, which was validated by experiments. The temperature evolutions and the melting pool sizes predicted by the FFNN model exhibit accuracy of 99% and 98%, respectively, compared with the FE model for unseen process settings in the studied range. Moreover, to evaluate the importance of the input features and explain the achieved accuracy of the FFNN model, a sensitivity analysis (SA) is carried out using the SHapley Additive exPlanation (SHAP) method. The SA shows that the most critical enriched features impacting the predictive capability of the FFNN model are the vertical distance from the laser head position to the material point and the laser head position.VINIF.2020.DA15 EDPOMP projec

Pro-poor intervention strategies in irrigated agriculture in Asia: poverty in irrigated agriculture: issues and options: Vietnam

Irrigated farming / Poverty / Farm income / Irrigation management / Institutions / Legal aspects / Water rates / User charges / Participatory management / Privatization / Participatory rural appraisal / Performance indexes / Irrigation programs / Irrigation systems / Pumping / Irrigation canals / Social aspects / Economic aspects / Rivers / Hydrology / Dams / Households / Income / Regression analysis / Drainage / Cooperatives / Water delivery / Water distribution / Rice / Financing / Drought / Vietnam / Red River Delta / Nam Duong Irrigation System / Nam Thach Han Irrigation System / Han River