Application of chitosan solutions for rice production in Vietnam

Preparing chitosan solutions from shrimp shells for rice production was investigated. The chitosan produced from shrimp shells using dilute acetic acid proved effective in controlling plants infection by microbial agents leading to higher yields. The field data of our studies showed that the yields of rice significantly increased (~31%) after applying chitosan solution. In general, applying chitosan increased rice production and reduced cost of production significantly.Keywords: Chitosan solution, rice production, common brown backed rice plant hoppersAfrican Journal of Biotechnology Vol. 12(4), pp. 382-38

Determination of dynamic impact factor for continuous girder bridge due to vehicle braking force by finite element method and experimental

In this study, the finite element method (FEM) is used to investigate the dynamic response of continuous girder bridge due to moving three-axle vehicle . Vertical reaction forces of axles that change with time make bending vibration of girder significantly  increase. The braking in the first span is able to create response in other spans. In addition, the dynamic impact factors are investigated by both FEM and experiment for Hoa Xuan bridge. The results of this study provide an improved understanding of the bridge dynamic behavior and can be used as additional references for bridge codes by practicing engineers

Applying Simplex Algorithm for Ship’s Motion Simulation Optimization by Using Maneuvering Tests Data

This article demonstrates an effective method to find OHCs (optimal hydrodynamic coefficients) by applying the Simplex algorithm to reduce the errors of the ship’s motion simulation. The solution is to determine OHCs, which are also the coefficients of the ship’s motion equations. A ship’s motion simulation model was programed by contributing the mathematical model of the ship’s motion, applying the numerical method and MATLAB. In the optimization procedure, the form of Objective Function was contributed corresponding to the type of maneuvering test. The Sensitivity Analysis technique and Simplex algorithm are applied to filter and optimize the most sensitive hydrodynamic coefficients. The numerical model was validated by experimental maneuvering test data, including Turning Circle and Zigzag tests of Esso Bernicia 193000DWT Tanker. A good optimization solution was obtained: for Turning Circle test, after optimization, the ship’s simulation trajectory is close to the experimental trajectory with a RMSD of 5.8m, which reduced from an original value of 69m. In the Zigzag test, the RMSD between the ship’s simulation yaw angle and experimental data was reduced 17.3deg to 5.9deg. The other optimization results, such as the convergence of Objective Function, the number of iteration of Optimization Variables, calculated time, etc. are accepted. Therefore, the Simplex algorithm can be applied quite effectively to optimize ship movement (ship’s trajectory, the ship’s yaw angle, etc.). By defining a common set of values by merging the optimal value of the most sensitive coefficients of two tests, which may be used for the other ship’s motion simulation applications

On the holographic phase transitions at finite topological charge

Exploring the significant impacts of topological charge on the holographic phase transitions and conductivity we start from an Einstein - Maxwell system coupled with a charged scalar field in Anti - de Sitter spacetime. In our set up, the corresponding black hole (BH) is chosen to be the topological AdS one where the pressure is identified with the cosmological constant. Our numerical computation shows that the process of condensation is favored at finite topological charge and, in particular, the pressure variation in the bulk generates a mechanism for changing the order of phase transitions in the boundary: the second order phase transitions occur at pressures higher than the critical pressure of the phase transition from small to large BHs while they become first order at lower pressures. This property is confirmed with the aid of holographic free energy. Finally, the frequency dependent conductivity exhibits a gap when the phase transition is second order and when the phase transition becomes first order this gap is either reduced or totally lost.Comment: 8 pages, 9 figure

Primarily Results of a Real-Time Flash Flood Warning System in Vietnam

In recent years, losses and damages from flash floods have been steadily increasing worldwide as well as in Vietnam, due to physical factors, human activities, especially under a changing climate. This is a hotspot issue which requires immediate response from scientists and policy-makers to monitor and mitigate the negative impacts of flash floods. This study presents a way to reduce losses through increasing the accuracy of real-time flash flood warning systems in Vietnam, a case study developed for Ha Giang province where the topography is relatively complex with severe flash floods observed. The objective of this paper is to generate the real-time flash flood system based on bankfull discharge threshold. To do this, HEC-HMS model is applied to calibrate and validate observer inflow to the reservoir with nine automatic rain gauges installed. More importantly, on the basic of measured discharge at 35 locations from the fieldtrips, an empirical equation constructed is to identify the bankful discharge values. It bases on the relationship between basin characteristics of river length, basin area and bankfull discharge. The results indicate an effective approach to determine bankfull threshold with the established-empirical equation. On the scale of a small basin, it depicts the consistency of flood status and warning time with the reality. Doi: 10.28991/cej-2021-03091687 Full Text: PD

Conditional Support Alignment for Domain Adaptation with Label Shift

Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabelled ones in the target domain. The dominant existing methods in the field that rely on the classical covariate shift assumption to learn domain-invariant feature representation have yielded suboptimal performance under the label distribution shift between source and target domains. In this paper, we propose a novel conditional adversarial support alignment (CASA) whose aim is to minimize the conditional symmetric support divergence between the source's and target domain's feature representation distributions, aiming at a more helpful representation for the classification task. We also introduce a novel theoretical target risk bound, which justifies the merits of aligning the supports of conditional feature distributions compared to the existing marginal support alignment approach in the UDA settings. We then provide a complete training process for learning in which the objective optimization functions are precisely based on the proposed target risk bound. Our empirical results demonstrate that CASA outperforms other state-of-the-art methods on different UDA benchmark tasks under label shift conditions

How scientific research changes the Vietnamese higher education landscape: Evidence from social sciences and humanities between 2008 and 2019

Background: In the context of globalization, Vietnamese universities, whose primary function is teaching, there is a need to improve research performance. Methods: Based on SSHPA data, an exclusive database of Vietnamese social sciences and humanities researchers’ productivity, between 2008 and 2019 period, this study analyzes the research output of Vietnamese universities in the field of social sciences and humanities. Results: Vietnamese universities have been steadily producing a high volume of publications in the 2008-2019 period, with a peak of 598 articles in 2019. Moreover, many private universities and institutions are also joining the publication race, pushing competitiveness in the country. Conclusions: Solutions to improve both quantity and quality of Vietnamese universities’ research practice in the context of the industrial revolution 4.0 could be applying international criteria in Vietnamese higher education, developing scientific and critical thinking for general and STEM education, and promoting science communication

On Inference Stability for Diffusion Models

Denoising Probabilistic Models (DPMs) represent an emerging domain of generative models that excel in generating diverse and high-quality images. However, most current training methods for DPMs often neglect the correlation between timesteps, limiting the model's performance in generating images effectively. Notably, we theoretically point out that this issue can be caused by the cumulative estimation gap between the predicted and the actual trajectory. To minimize that gap, we propose a novel \textit{sequence-aware} loss that aims to reduce the estimation gap to enhance the sampling quality. Furthermore, we theoretically show that our proposed loss function is a tighter upper bound of the estimation loss in comparison with the conventional loss in DPMs. Experimental results on several benchmark datasets including CIFAR10, CelebA, and CelebA-HQ consistently show a remarkable improvement of our proposed method regarding the image generalization quality measured by FID and Inception Score compared to several DPM baselines. Our code and pre-trained checkpoints are available at \url{https://github.com/VinAIResearch/SA-DPM}.Comment: Oral presentation at AAAI 202