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The purpose of this research is to evaluate the results of arthroscopic suture fixation with fiber wires used as treatment for ACL avulsion fracture, and to determine how effective such a technique is when it comes to restoring of knee function. This prospective study involves 28 patients, who underwent arthroscopic fixation of displaced ACL avulsion fractures at Saint Paul Hospital (Hanoi) from January 2014 to March 2018. The first 3 weeks were not marked with any abnormalities associated with postoperative sutures and hematomas; infectious complications were not detected either. Postoperative displacement of fracture fragments did not take place among the patients involved in the study. At the 3-month follow-up, the average IKDC score was 90.7 (range 76–100), and the average Lysholm score was 93.6 (range 82–100). The percentage of excellent scores was 42.9% (12 patients), good scores accounted for 50% (14 patients), while fair/poor scores accounted for 3.6% each (one patient on each score). The percentage of excellent/good scores was 92.9% in total. This study shows that ACL avulsion fracture can be treated effectively by arthroscopic suture fixation with fiber wires. In fact, this technique may restore knee function and stability.

We report the fabrication and characterization of surface acoustic wave (SAW) hydrogen sensors using palladium-graphene (Pd-Gr) nanocomposite as sensing material. The Pd-Gr nanocomposite as sensing layer was deposited onto SAW delay line sensor-based interdigitated electrodes (IDTs)/aluminum nitride (AlN)/silicon (Si) structure. The Pd-Gr nanocomposite was synthesized by a chemical route and deposited onto SAW sensors by air-brush spraying. The SAW H2 sensor using Pd-Gr nanocomposite as a sensing layer shows a frequency shift of 25 kHz in 0.5% H2 concentration at room temperature with good repeatability and stability. Moreover, the sensor showed good linearity and fast response/recovery within ten seconds with various H2 concentrations from 0.25 to 1%. The specific interaction between graphene and SAW transfer inside AlN/Si structures yields a high sensitivity and fast response/recovery of SAW H2 sensor based on Pd-Gr/AlN/Si structure.

The original version of this article unfortunately contained a mistake. One affiliation was incorrect.

An Electric propulsion (E-propulsion) system for ASEAN (Association of Southeast Asian Nations) long-tail boat is proposed in this article. It offers several advantages over a traditional internal combustion engine propulsion system. Besides low noise and zero-emission, characteristics of electric engine (E-engine) allow regenerative braking and starting the propeller in the water. A design of E-engine has been achieved through finite element analyses and lump-parameter thermal simulations. It shows better performances than Honda GX270 internal combustion engine in terms of volume, weight, torque, and power. A full scale prototype of E-engine was manufactured. Experiments have been conducted on an engine test bench. Torque, power, efficiency and temperatures were well aligned with the simulation results.

We report the construction and testing of a resistive-type H 2 sensor composed of a monolayer of platinum nanoparticles (Pt NPs) deposited using an immersion method on single-layer graphene. It was found that the Pt NP monolayer significantly reduced the response/recovery time of the graphene-based H 2 sensor. The very rapid response of the sensor is attributed to the short diffusion length between the monolayer Pt nanoparticles and single-layer graphene. The sensor showed response time of 6 s and recovery time of 69 s at the optimal working temperature of 150°C. In addition, the fabricated device exhibited good repeatability at 10,000 ppm H 2 , detection range of 10 ppm to 10,000 ppm, and good thermal stability, satisfying the requirements for H 2 sensors for safety applications.

A self-bearing motor (SBM) is an electric motor with a magnetically integrated bearing function, that is, it can provide levitation and rotation simultaneously as a single actuator. This paper presents the design, operating principle and control system for the slotless self-bearing motor (SSBM). In this design, the stator has no iron core but includes six-phase coils. The rotor consists of a permanent magnet and an enclosed iron yoke. Magnetic forces generated by the interaction between stator currents and the magnetic field of the permanent magnet are used to control the rotational speed and radial position of the rotor. In this paper, the torque and radial bearing forces are analyzed theoretically with the aim to develop an improved control system. In order to confirm the proposed control method, an experimental system was constructed and tested. Simulation and measurement results show that the SSBM can work stably in modes such as start, reverse, rotation load and external radial pulse forces.

The search for efficient deep learning architectures for emotion recognition using EEG signals has drawn great interest due to applications in healthcare, education, and intelligent interaction. These models must meet three key requirements: achieving high accuracy with fewer electrodes (32, 14, or even 5), maintaining stable performance across frequency bands, and being lightweight enough for deployment on low-resource devices. This paper proposes EEG_SICNET, an enhanced 1D-CNN integrated with Squeeze and Excitation and Inception blocks to optimize EEG signal processing. Experiments on DEAP, DREAMER, and AMIGOS datasets demonstrate EEG_SICNET's compact size (40.14 MB), stable performance across frequency bands, and accuracy up to 83% with 5 electrodes. Additionally, it achieves over 72% accuracy when deployed on a Raspberry Pi 4 with 14-channel input, outperforming recent methods on the DEAP dataset.

This paper proposes a new fixed-time disturbance observer (FTDOB) based on the fractional-order state observer (FOSOB) and a super-twisting sliding-mode control (STSMC) for a slotless self-bearing motor (SSBM). First, the slotless self-bearing motor with fully embedded disturbances and uncertainties is analyzed, the imperfection of the perturbation sources. Second, a fractional-order state observer was designed to estimate the velocities and accelerations of movement on x - , y - , and ω - axes, respectively. Third, a new concept of fixed-time disturbance observer was proposed for estimating the perturbations on three axes of the bearing motor system. Fourth, the super-twisting sliding-mode control was designed to control the positions and the rotational speed of the bearing motor system. Final, the stability of the proposed algorithms was theoretically verified via the Lyapunov condition. To visually present the effectiveness and originality of the proposed theories, the MATLAB simulation was used to show that the proposed control system consisted small overshoots, small settling times, and stable tracking error values in narrow gaps around zero.

In this paper, an appropriate control strategy is proposed to handle the nonlinear dynamics ofan active magnetic bearing (AMB). The goal of the control design is to drive the AMB rotor to the origin with improved transient response. In order to achieve this task, back stepping control technique with a barrier Lyapunov function are employed to keep the tracking error trajectory inside a predefined zone to avoid possible mechanical contact between rotor and stator. Besides, a speed observer is also used since information about rotor speed is not always available. The stability of the closed-loop system is proven. The effectiveness of the proposed control strategy is verified by numerical simulations.