Online Learning Management in Covid-19 Pandemic: Case in Vietnam

The world is dealing with the COVID-19 epidemic, which has created enormous impacts on education, especially on learning management, requiring the adjustment and implementation of an online learning system effectively.The process of digital transformation has been promoted rapidly to adapt to new conditions in Education.Nowadays the online system has facilitated students of all levels to study at anytime and anywhere owing to the development of technologies.However, the existence and limits ofonline teaching and learning have been mentioned such as the complicated implementation or limited communication of active teaching methods compared to an actual classroom. Therefore, innovative teaching strategies and models are now required to be more diverse to encourage learners to focus on the lesson.In this study, we proposed an optimal paradigm of online learning management: the combination of several supported programs to effectivelymanage the teaching process. The approach has been appliedtothe Basic Informatics course for students majoring in Primary Education at the Thai Nguyen University of Education, Vietnam, and suggesteda flexible approach to stimulate learners' enthusiasm, express opinions, and actively participate in activities for enhancing their achievements. In addition, the investigation and discussion from lecturers and students about the digital transformation challenges in learning management also were presented and proposed the necessary information for the educational process

1M parameters are enough? A lightweight CNN-based model for medical image segmentation

Convolutional neural networks (CNNs) and Transformer-based models are being widely applied in medical image segmentation thanks to their ability to extract high-level features and capture important aspects of the image. However, there is often a trade-off between the need for high accuracy and the desire for low computational cost. A model with higher parameters can theoretically achieve better performance but also result in more computational complexity and higher memory usage, and thus is not practical to implement. In this paper, we look for a lightweight U-Net-based model which can remain the same or even achieve better performance, namely U-Lite. We design U-Lite based on the principle of Depthwise Separable Convolution so that the model can both leverage the strength of CNNs and reduce a remarkable number of computing parameters. Specifically, we propose Axial Depthwise Convolutions with kernels 7x7 in both the encoder and decoder to enlarge the model receptive field. To further improve the performance, we use several Axial Dilated Depthwise Convolutions with filters 3x3 for the bottleneck as one of our branches. Overall, U-Lite contains only 878K parameters, 35 times less than the traditional U-Net, and much more times less than other modern Transformer-based models. The proposed model cuts down a large amount of computational complexity while attaining an impressive performance on medical segmentation tasks compared to other state-of-the-art architectures. The code will be available at: https://github.com/duong-db/U-Lite.Comment: 6 pages, 7 figure

Application of hybrid control algorithm for the vehicle active suspension system to reduce the vibration

This research proposes a hybrid control algorithm to enhance smoothness in a vehicle’s motion. The control signal is synthesized from two separate controllers, Proportional Integral Derivative (PID) and Sliding Mode Control (SMC), to achieve superior control performance. The novelty of the proposed control algorithm lies in using a double-loop algorithm to determine the controller parameters. The algorithm proposed in this research involves two computational processes to determine the model's optimal values including the raw value and the acceptable value. The proposed control algorithm has been simulated considering three specific cases corresponding to the three types of road stimuli. The results demonstrate that the values of sprung mass displacement and acceleration dropped considerably with the application of the proposed algorithm. Moreover, the change in vertical force at the wheel is also reduced with the application of the algorithm particularly in the third case where the vertical force at the wheel has reached to zero. The average values of vehicle body displacement are found to be 166.17mm (for passive case), 54.20mm (for PID) and 42.52mm (for SMC). The proposed control algorithm managed to reduce this value to 8.95mm as evidenced by simulation results. Finally, the response of the control system when subjected to an excitation signal from the road surface further demonstrates efficacy of the proposed hybrid control algorithm

Effect of Graphene-Gold Nanocomposites on the Photocatalytic Activity Of TiO2_2

The synthesis of graphene (Gr) - gold (Au) nanoparticle (NP) composite was achieved using continuous ultraviolet wave exposure. The functional groups were investigated with FT-IR spectra. From the Raman spectra, D-band and G-band of graphene were identified. As a result, the uniform deposition of nanometer-sized Au NPs on the graphene sheets was observed from the field emission scanning electron microscope (FE-SEM) images. The photodegradation of methylene blue in aqueous solutions is studied using various photocatalysts, including neat TiO2, Gr/TiO2 and Gr-Au/TiO2 composites. The Gr weight ratio in this research is 2%. The Gr2%-Au0.1%/TiO2 composite had the highest photoactivity

Random Lasing from a Layer of ZnO Powder Painted on Glass Substrate under Excitation of Nanosecond and Picosecond Laser Pulses

The ZnO powder which consists of monodisperse colloidal spheres with average diameter of 100-300 nm was synthesized by hydrolysis of Zinc acetate dehydrate. The random lasing around 380 nm was produced from a layer of the ZnO powder painted on a glass substrate and optically pumped at 355 nm. Our experimental results show that random laser action of a layer of ZnO spherical nanoparticles and the characteristics of these random lasers under excitation of nanosecond and picosecond laser pulses