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The strength and stiffness of steel deteriorate rapidly at elevated temperatures. Thus, the characteristics of steel structures exposed to fire have been concerned in recent years. Most studies on the fire response of steel structures were conducted at uniformly distributed temperatures. This study aims to evaluate the buckling capacity of steel H-beams subjected to different loading conditions under non-uniform heating.

A numerical investigation was conducted employing finite element analysis software, ABAQUS. A comparison between the numerical analysis results and the experimental data from previous studies was conducted to verify the beam model. Simply supported beams were loaded with several loading conditions including one end moment, end equal moments, uniformly distributed load and concentrated load at midspan. The effects of initial imperfections were considered. The buckling capacities of steel beams under fire using the existing fire design code and the previous study were also generated and compared.

The results showed that the length-to-height ratio and loading conditions have a great effect on the buckling resistance of steel beams under fire. The capacity of steel beams under non-uniform temperature distribution using the existing fire design code and the previous study can give unconservative values or too conservative values depending on loading conditions. The maximum differences of unconservative and conservative values are −44.5 and 129.2% for beams subjected to end equal moments and one end moment, respectively.

This study provides the buckling characteristics of steel beams under non-uniform temperature considering the influences of initial imperfections, length-to-height ratios, and loading conditions. This study will be beneficial for structural engineers in properly evaluating structures under non-uniform heating conditions.

The paper aims to sketch out the scenario of money laundering (ML) in Vietnam and the harm ML may cause to the country.

This paper first, based on the general concept of ML, scrutinizes actual and potential ML in Vietnam as well as ML threat to Vietnam. The typical cases of predicate offences, which were associated with ML activity, will be provided to illustrate the fact. Then a brief of Vietnam's response to ML will be examined.

ML has actually occurred since early in Vietnam. The potential for ML in Vietnam is substantial and poses growing harm to Vietnam in both the respects of economy and security. Although Vietnam has the primary legal framework of AML and set out AML countermeasures, the implementation has been hindered by several factors.

This paper would attract the attention of people who are concerned about ML in Vietnam.

Structural health monitoring (SHM) has gained significant attention due to its capability in providing support for efficient and optimal bridge maintenance activities. However, despite the promising potential, the effectiveness of SHM system might be hindered by unprecedented factors that impact the continuity of data collection. This research presents a framework utilising convolutional neural network (CNN) for estimating structural response using environmental variations.

The CNN framework is validated using monitoring data from the Suramadu bridge monitoring system. Pre-processing is performed to transform the data into data frames, each containing a sequence of data. The data frames are divided into training, validation and testing sets. Both the training and validation sets are employed to train the CNN models while the testing set is utilised for evaluation by calculating error metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Comparison with other machine learning approaches is performed to investigate the effectiveness of the CNN framework.

The CNN models are able to learn the trend of cable force sensor measurements with the ranges of MAE between 10.23 kN and 19.82 kN, MAPE between 0.434% and 0.536% and RMSE between 13.38 kN and 25.32 kN. In addition, the investigation discovers that the CNN-based model manages to outperform other machine learning models.

This work investigates, for the first time, how cable stress can be estimated using temperature variations. The study presents the first application of 1-D CNN regressor on data collected from a full-scale bridge. This work also evaluates the comparison between CNN regressor and other techniques, such as artificial neutral network (ANN) and linear regression, in estimating bridge cable stress, which has not been performed previously.