In this paper, a structure-specified mixed H2/H∞ controller design using particle swarm optimization (PSO) is proposed for control balancing of Bicyrobo, which is an unstable system associated with many sources of uncertainties due to un-model dynamics, parameter variations, and external disturbances. The structure-specified mixed H2/H∞ control is a robust and optimal control technique. However, the design process normally comes up with a complex and non-convex optimization problem which is difficult to solve by the conventional optimization methods. PSO is a recently useful meta-heuristic search method used to solve multi-objective and non-convex optimization problems. In the method, PSO is used to search for parameters of a structure-specified controller which satisfies mixed H2/H∞ performance index. The simulation and experimental results show the robustness of the proposed controller in compared with the conventional proportional plus derivative (PD) controller, and the efficiency of the proposed algorithm in compared with the genetic algorithm (GA).

We presented synthesis and physical characterization of iron oxide magnetic nanoparticles (Fe3O4) for biomedical applications in the size range of 10-30 nm. Magnetic nanoparticles were synthesized by the coprecipitation method, and the particles’ size was controlled by two different injection methods of sodium hydroxide (NaOH). The synthesized magnetic nanoparticles were then modified by using series of linkers including tetraethyl orthosilicate (TEOS), 3-aminopropyltriethoxysilane (APTES), and glutaraldehyde (GA) to generate the structure of Fe3O4/SiO2/NH2/CHO, which can be used for immobilization of protein A. Additionally, we used transmission electron microscopy (TEM), X-ray powder diffraction (XRD), vibrating-sample magnetometry (VSM), and Fourier-transform infrared spectroscopy (FTIR), for characterization of properties and structure of the nanoparticles. An immobilization of protein A on magnetic nanoparticles was studied with a UV-Vis spectrum (UV-Vis) and fluorescence electron microscopy and Bradford method. Results showed that an XRD spectrum with a peak at (311) corresponded to the standard peak of magnetic nanoparticles. In addition, the magnetic nanoparticles with d≥30 nm have higher saturation magnetizations in comparison with the smaller ones with d≤10 nm. However, the smaller magnetic nanoparticles offered higher efficiency for binding of protein A, due to the high surface/volume ratio. These particles with functional groups on their surface are promising candidates for biomedical applications, e.g., drug delivery, controlled drug release, or disease diagnosis in point-of-care test.

The Fourth Industrial Revolution is opening up new opportunities and challenges for all industries, professions, and fields, aiming to bring humanity more optimal tools and services. During the Fourth Industrial Revolution, digital transformation has been one of the most critical problems. Artificial Intelligence (AI) and the Internet of Things (IoT) are two technologies that have the potential to cause the biggest breakout to evolve in the educational domain. In recent years, digital transformation has seen implementation across all sectors, including education, healthcare, agriculture, transportation, and other smart ecosystems. Among those areas, education, especially higher education, is among the most challenging due to the diversity in training programs, duration, and subjects. The Internet of Things makes it possible to create smart and ubiquitous learning environments, while artificial intelligence can completely transform the way we learn and teach. In this paper, we present the digital transformation process in higher education in Vietnam and internationally and analyze some characteristics of Vietnamese higher education in the digital transformation process. Moreover, we present the vision, approach, and challenges to digital transformation at universities of low- and middle-income countries from the perspective of the Hung Yen University of Technology and Education in Vietnam.

The paper focuses on faulty actuator problems in an industrial robot using servomotors, and provides an adaptive sliding mode control law to overcome this circumstance. Because of multifarious reasons, robot actuators can undergo a variety of failures, such as locked or stuck joints, free-swinging joints, and partial or total loss of actuation effectiveness. The robot behavior will become worsen if the system controller has not been designed with adequate faulty tolerance. The proportional degradation of actuator torque at unknown degrees of loss, which is one type of partial loss of actuation effectiveness, is considered in this study to design a suitable controller. The robot model is constructed with uncertain parameters and unknown friction, whereas the controller uses only the approximate parameters. Symmetry and skew-symmetry give important contributions in robot modeling and transformation, as well as in the process of proving the system stability. An adjustable coefficient vector of the proposed controller can adaptively reach the upper bounds of an uncertain parametric vector, which guarantees the criterion of Lyapunov stability. In the numerical simulation stage, the selected industrial robot is a Serpent 1 robot with three degrees of freedom. A quasi-physical model based on MATLAB/Simscape Multibody for the robot is built and used in order to increase the reliability of the simulation performance closer to reality. Simulation results illustrate the efficiency of the proposal control methodology in the presence of the mentioned failure. The controller can still deliver satisfactory responses to the robot system under reasonable levels of actuator torque degradation.