Perceptions and practices of pesticides safety measures of rice farmers in the central region of Vietnam

The use of pesticides is increasing rapidly and the pesticide use crisis is badly damaging the environment, the economy, and public health in Vietnam. However, the country is yet to become successful in reducing pesticide use mostly because of policy implementation and inadequate understanding of farmers. This study examined and discussed the perceptions and safety level of using pesticides by applying a widely used index of 39 indicators equivalent to 39 safety measures grouped into four categories to assess the safety behaviour of rice farmers in the central region of Vietnam. A field survey of 320 rice farmers and 12 local leaders was conducted in Quang Tri and Thua Thien Hue provinces. The result revealed that there exists a significant difference (p0.001) between the perception and practices of pesticide safety measures of rice farmers in the study area. The overall score appears relatively high (4.09 and 3.89 out of 5.0 for perception and practices, respectively), indicating that farmers believe what they are doing is safe, though there are significant variations among the categories and among farmers in practicing pesticide safety measures. Regarding the farmers’ safety level, it was observed that there are still 18.1% and 34.4% of rice farmers are under unsafe and potentially unsafe conditions, respectively. Hence, an effective extension and communication program regarding the management and safety use of pesticides is the most vital policy solution to protect the rice farmers from potential health risks and ensure the sustainability of agriculture

Some results in studying multilayered composite plates

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Transformer-Based Deep Learning Detector for Dual-Mode Index Modulation 3D-OFDM

In this paper, we propose a deep learning-based signal detector called TransD3D-IM, which employs the Transformer framework for signal detection in the Dual-mode index modulation-aided three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM) system. In this system, the data bits are conveyed using dual-mode 3D constellation symbols and active subcarrier indices. As a result, this method exhibits significantly higher transmission reliability than current IM-based models with traditional maximum likelihood (ML) detection. Nevertheless, the ML detector suffers from high computational complexity, particularly when the parameters of the system are large. Even the complexity of the Log-Likelihood Ratio algorithm, known as a low-complexity detector for signal detection in the DM-IM-3D-OFDM system, is also not impressive enough. To overcome this limitation, our proposal applies a deep neural network at the receiver, utilizing the Transformer framework for signal detection of DM-IM-3D-OFDM system in Rayleigh fading channel. Simulation results demonstrate that our detector attains to approach performance compared to the model-based receiver. Furthermore, TransD3D-IM exhibits more robustness than the existing deep learning-based detector while considerably reducing runtime complexity in comparison with the benchmarks

Deep Neural Network-Based Detector for Single-Carrier Index Modulation NOMA

In this paper, a deep neural network (DNN)-based detector for an uplink single-carrier index modulation nonorthogonal multiple access (SC-IM-NOMA) system is proposed, where SC-IM-NOMA allows users to use the same set of subcarriers for transmitting their data modulated by the sub-carrier index modulation technique. More particularly, users of SC-IMNOMA simultaneously transmit their SC-IM data at different power levels which are then exploited by their receivers to perform successive interference cancellation (SIC) multi-user detection. The existing detectors designed for SC-IM-NOMA, such as the joint maximum-likelihood (JML) detector and the maximum likelihood SIC-based (ML-SIC) detector, suffer from high computational complexity. To address this issue, we propose a DNN-based detector whose structure relies on the model-based SIC for jointly detecting both M-ary symbols and index bits of all users after trained with sufficient simulated data. The simulation results demonstrate that the proposed DNN-based detector attains near-optimal error performance and significantly reduced runtime complexity in comparison with the existing hand-crafted detectors

Estimating Disturbance Torque Effects on the Stability and Control Performance of Two-Axis Gimbal Systems

Introduction. Two-axis gimbal systems are applied for stabilizing and controlling the line of sight (LOS) of an optical or imaging system mounted on a moving vehicle. Gimbal systems are intended to isolate various disturbance torques and control the LOS toward the direction of a target. Two-axis gimbals can be of two main types, namely Yaw-Pitch and Swing-Roll type. In this article, we focus on investigating mathematical models of two-axis gimbals, which describe the impact of cross-disturbance torques on their stability and control performance. Simulations were conducted to compare advantages and disadvantages of the two types of two-axis gimbals.Aim. To study mathematical models describing the impact of cross-disturbance torques on the stability and control performance of two-axis gimbals.Materials and methods. Mathematical models of two-axis gimbal systems were synthesized by the Lagrange method. The operation of two-axis gimbal systems was simulated in the Matlab-Simulink environment. Results. Mathematical models and structural diagrams of the synthesized Yaw-Pitch and Swing-Roll gimbals were obtained. The conducted simulations of typical cases revealed different cross-disturbance effects.Conclusion. Motion equations for Swing-Roll and Yaw-Pitch gimbals were derived using similar methodology. The impact of cross-disturbance torques on gimbal systems was evaluated. The obtained results form a basis for selecting an optimal structure of tracking systems meeting the desired characteristics.Introduction. Two-axis gimbal systems are applied for stabilizing and controlling the line of sight (LOS) of an optical or imaging system mounted on a moving vehicle. Gimbal systems are intended to isolate various disturbance torques and control the LOS toward the direction of a target. Two-axis gimbals can be of two main types, namely Yaw-Pitch and Swing-Roll type. In this article, we focus on investigating mathematical models of two-axis gimbals, which describe the impact of cross-disturbance torques on their stability and control performance. Simulations were conducted to compare advantages and disadvantages of the two types of two-axis gimbals.Aim. To study mathematical models describing the impact of cross-disturbance torques on the stability and control performance of two-axis gimbals.Materials and methods. Mathematical models of two-axis gimbal systems were synthesized by the Lagrange method. The operation of two-axis gimbal systems was simulated in the Matlab-Simulink environment. Results. Mathematical models and structural diagrams of the synthesized Yaw-Pitch and Swing-Roll gimbals were obtained. The conducted simulations of typical cases revealed different cross-disturbance effects.Conclusion. Motion equations for Swing-Roll and Yaw-Pitch gimbals were derived using similar methodology. The impact of cross-disturbance torques on gimbal systems was evaluated. The obtained results form a basis for selecting an optimal structure of tracking systems meeting the desired characteristics

Factors affecting learner’s satisfaction towards online learning during COVID-19 pandemic: A case study of Vietnam

Online learning is being considered a new model of knowledge exchange in modern education. In parallel with the incredible impacts of the global pandemic, this is considered an opportunity to promote the development of online learning globally. Therefore, this study proposed a research framework including four factors affecting learner satisfaction towards online learning during the COVID-19 pandemic at a university, which are system quality, service quality, transformational leadership, and self-efficacy. A questionnaire was conducted online to assess which 131 respondents were representative students from two large private universities in Da Nang: FPT University and Duy Tan University. The results from the regression analysis show that three factors have a positive impact on learner satisfaction during COVID-19. This study concludes that students at private universities in Da Nang prioritize system quality as the most significant factor in their satisfaction with the online learning system, followed by transformational leadership and the last one is self-efficacy. Therefore, it can be more strategic for private organizations, developers, software designers, or even transformation-trained trainers to be emphasized to build a system of processes for implementing online learning for students effectively

Dynamic response analysis of truss bridges under the effect of moving vehicles

With the characteristics of heavy and concentrated loads, the influence of moving loads on the dynamic response of the bridges is significant. Therefore, in this paper, the dynamic response of a large-scale truss bridge is studied to consider the effect of the various parameters of moving loads. The considered main parameters consist of moving mass, moving velocity, and type of moving loads. The nonlinear dynamics of the bridge based on time history analysis are obtained using the Wilson-  method. four time history – based dynamic analysis method including modal superposition in frequency domain, modal superposition in time domain; direct time integration, and direct solution in the frequency domain are employed to analysis the obtained results. To compare the effectiveness of the aforementioned method. A large-scale railway truss bridge is employed for dynamic response analysis. The obtained results give more insight into the nature of the problem and help to determine the significant parameters of moving load affecting the bridge response

Model Updating for Large-Scale Railway Bridge Using Grey Wolf Algorithm and Genetic Alghorithms

This paper proposes a novel hybrid algorithm to deal with an inverse problem of a large-scale truss bridge. Grey Wolf Optimization (GWO) Algorithm is a well-known and widely applied metaheuristic algorithm. Nevertheless, GWO has two major drawbacks. First, this algorithm depends crucially on the positions of the leading Wolf. If the position of the leaderis far from the best solution, the obtained results are poor. On the other hand, GWO does not own capacities to improve the quality of new generations if elements are trapped into local minima. To remedy the shortcomings of GWO, we propose a hybrid algorithm combining GWO with Genetic Algorithm (GA), termed HGWO-GA. This proposed method contains two key features (1) based on crossover and mutation capacities, GA is first utilized to generate the high-quality elements (2) after that, the optimization capacity of GWO is employed to seek the optimal solutions. To assess the effectiveness of the proposed approach, a large-scale truss bridge is employed for model updating. The obtained results show that HGWO-GA not only provides a good agreement between numerical and experimental results but also outperforms traditional GWO in terms of accuracy

Model Updating for Large-Scale Railway Bridge Using Grey Wolf Algorithm and Genetic Alghorithms

This paper proposes a novel hybrid algorithm to deal with an inverse problem of a large-scale truss bridge. Grey Wolf Optimization (GWO) Algorithm is a well-known and widely applied metaheuristic algorithm. Nevertheless, GWO has two major drawbacks. First, this algorithm depends crucially on the positions of the leading Wolf. If the position of the leaderis far from the best solution, the obtained results are poor. On the other hand, GWO does not own capacities to improve the quality of new generations if elements are trapped into local minima. To remedy the shortcomings of GWO, we propose a hybrid algorithm combining GWO with Genetic Algorithm (GA), termed HGWO-GA. This proposed method contains two key features (1) based on crossover and mutation capacities, GA is first utilized to generate the high-quality elements (2) after that, the optimization capacity of GWO is employed to seek the optimal solutions. To assess the effectiveness of the proposed approach, a large-scale truss bridge is employed for model updating. The obtained results show that HGWO-GA not only provides a good agreement between numerical and experimental results but also outperforms traditional GWO in terms of accuracy

Deep Learning-Based Signal Detection for Dual-Mode Index Modulation 3D-OFDM

In this paper, we propose a deep learning-based signal detector called DuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D- OFDM is a subcarrier index modulation scheme which conveys data bits via both dual-mode 3D constellation symbols and indices of active subcarriers. Thus, this scheme obtains better error performance than the existing IM schemes when using the conventional maximum likelihood (ML) detector, which, however, suffers from high computational complexity, especially when the system parameters increase. In order to address this fundamental issue, we propose the usage of a deep neural network (DNN) at the receiver to jointly and reliably detect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fading channels in a data-driven manner. Simulation results demonstrate that our proposed DNN detector achieves near-optimal performance at significantly lower runtime complexity compared to the ML detector