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The musculature of the face is innervated by cranial nerves, each with a motor, sensory and/or both function. The Facial nerve (FN) is responsible for the motor innervation of the muscles of the face. Some branches of the trigeminal nerve are responsible for the sensory part of the facial muscles and other branches act on the motor part of the chewing muscles. Traumatic Facial Paralysis (TFP) is the one where there was section or traction or compression or ischemia of the FN, in surgery for tumor resection or trauma in general. In this case occurs the nerve’s section in one surgery. Facial Paralysis (FP) can be evaluated subjectively through the House and Brackmann classification scale (HB) [1]. It is considered a chronic FP when it persists for a period longer than 6 months and leaves sequelae, such as synkinesis, contractures and lack of complete innervation of some nerve branches. Some patients who evolve with chronic FP may also evolve with alteration of the occlusal plane. The occlusal plane is the meeting point between the antagonist teeth, plane that is in the final stop of the masticatory cycle. The rehabilitation of this plan is performed according to the needs of each patient, in this case was made through implant prostheses.

Friction nonlinearity and uncertainty are the main factors affecting the highly performance control of electromagnetic actuators. In this paper, a nonlinear adaptive robust control strategy is proposed of electromagnetic actuators with friction nonlinearity and uncertainty compensation. First, the dynamical model of the electromagnetic actuator is established considering nonlinearity and uncertainty. Then, an adaptive robust controller is designed based on the continuously differentiable friction model to ensure that the control input is continuously and bounded. In the design of the controller, the unfavorable effects of unknown parameters in the electromagnetic actuator are eliminated by constructing a parameter adaptive law. Meanwhile, in order to improve the tracking accuracy of the electromagnetic actuator, a nonlinear robust control law is designed to ensure the robustness of the controller. The stability analysis by Lyapunov function shows that the asymptotic tracking effect can be obtained when only parameter uncertainty exists in the closed-loop system of the electromagnetic actuator, and the consistent bounded stability can be ensured when the system also exists uncertain nonlinearity. Extensive comparative results verify the effectiveness of the proposed control method.

The objective of this study is to investigate the symptoms, types, etiology, and assessment methods of motion sickness in autonomous vehicles in order to gain a comprehensive understanding of its occurrence mechanism and emphasize the significance of enhancing autonomous vehicle algorithms for improved ride comfort. Thus, this paper provides a synthesis and discussion of various theories while exploring strategies for mitigating motion sickness from three perspectives: passengers, vehicles, and external equipment. Firstly, it summarizes the clinical manifestations and classification of motion sickness while conducting an in-depth analysis of associated factors. Secondly, it evaluates different approaches for quantitatively measuring the severity and extent of motion sickness. Subsequently, it analyzes the reasons behind increased motion sickness caused by autonomous vehicles and emphasizes the importance of algorithmic improvements to enhance travel comfort. Finally, mitigation strategies are proposed considering passengers' needs as well as advancements in accurate motion prediction models and optimization techniques for autonomous planning and control algorithms that can effectively reduce the risk of motion sickness. As application scenarios for autonomous technology continue to expand, meeting user requirements while ensuring safety has become a benchmark for assessing technical proficiency. Therefore, promoting unmanned travel services necessitates a thorough analysis of existing issues related to autonomous technology along with prioritizing algorithm design enhancements through effective means to achieve an enhanced user experience.

Cardiovascular diseases are the leading cause of death globally accounting for 32 % of all global deaths. Addressing modifiable risk factors, including hypertension and stress, remains paramount in the prevention of CVD. This study aimed to assess the impact of the balneotherapy on blood pressure, heart rate, electrocardiogram parameters, utilization of antihypertensive medications, and stress levels. Methodology. Multicenter randomized controlled single-blind parallel group trial was made in 6 Lithuania resort centers. 373 participants with the stress level > 3 (10, VAS) randomly divided into 6 groups in two clusters for receiving a 6- or 11-day complex treatment using natural resources – mineral waters, peloids, salt, nature therapy; follow-up period was 6 months. The BP, HR, ECG, stress level and other parameters were analyzed in 131 participants who passed a full course of investigation. Results. The significant reduction of systolic blood pressure after treatment (7.8 mmHg) and during 6 months (7.3 mmHg) together with diastolic 5.5 after 6 month and heart rate (4 b/min) was achieved in 11-day ambulatory balneotherapy group. Increase in the activity of the parasympathetic nervous system was observed, which is reflected by an increase in the duration of the ECG indicators. The stress level decreased by up to 43%, and stress management improved by up to 41% following inpatient treatment. Conclusion. Therapy using natural resources could stand for additional cardiovascular disease prevention and management method in integrative therapy.

Considering the detrimental impact of thermal phenomena on the geometric precision of machine tools, a machine tool ball screw’s omni-directional error model is created using the LSTM neural network algorithm. Subsequently, the machine tool ball screw's omni-directional error compensation module is devised by combining the core functions of the Huazhong numerical control system with the visual programming environment of QT and the numerical computation capability of Matlab. To enhance the practicality and accuracy of the compensation model, this study has employed the Whale Optimization Algorithm (WOA) to optimize the parameters of the LSTM model. This has resulted in an improvement in the model's generalization ability and prediction performance, making it more effective. During the experimental validation phase, the Z-axis error of the machine tool was practically operated and analyzed using the compensation method. Results manifestly show that, after employing the compensation method, the peak amplitude of the Z-axis error fluctuations have been notably curtailed to ±0.006 mm – a considerable reduction compared to the initial error bandwidth of ±0.0145 mm. These empirical findings substantiate the efficacy of the proposed compensation strategy in substantially boosting the machining precision of products, thus furnishing a substantial and instructive benchmark for future inquiries into CNC machine tool error compensation technologies.

Aiming to address the low efficiency of current deep learning algorithms for segmenting citrus in complex environments, this paper proposes a study on citrus segmentation algorithms based on a multi-scale attention mechanism. The DeepLab V3+ network model was utilized as the primary framework and enhanced to suit the characteristics of the citrus dataset. In this paper, we will introduce a more sophisticated multi-scale attention mechanism to enhance the neural network’s capacity to perceive information at different scales, thus improving the model’s performance in handling complex scenes and multi-scale objects. The DeepLab V3+ network addresses the challenges of low segmentation accuracy and inadequate refinement of segmentation edges when segmenting citrus in complex scenes, and the experimental results demonstrate that the improved algorithm in this paper achieves 96.8 % in the performance index of MioU and 98.4 % in the performance index of MPA, which improves the segmentation effectiveness to a significant degree.

One of the primary sources of noise and vibration in automobiles is gearboxes. Shafts, gears, and bearings are the main causes of noise and vibration in vehicle gearboxes. Various studies have reported that vibrations’ root cause is bearing excitation. Besides bearing fatal defects or extreme structure resonance amplification, gear mesh is the primary source of high-frequency vibration and noise, even in newly built units. Gear damage detection is frequently crucial in automotive gearboxes and vehicle safety. Furthermore, vibrations caused by shaft imbalances, shaft misalignments, and other factors can cause noise and vibrations in the drivetrain's transfer path. In addition, the vibration of an automobile gearbox is closely related to poor design, construction quality, and production accuracy. This paper reviewed previous research and methods on car gearboxes for conventional vehicles. It was obvious that frequency analysis and order analysis were commonly used in noise and vibration analysis on car gearboxes. Envelope analysis is usually used to analyze bearing faults. Finally, rolling-element bearing diagnostic techniques were also reviewed.

Advanced aerospace structures are subjected to extremely harsh working environment, including of mechanical, acoustic, thermal, and aerodynamic loads. These combined loads can make the structures exhibit complex response, which has already attracted increasing concern in the design of advanced aerospace structures. To tackle the problem, a finite element model (FEM) by discretizing the theoretical differential equation is established to calculate the dynamic response of thin-walled structures under combined thermal and acoustic loads. The numerical analysis indicates that three types of nonlinear responses of the thin plate under combined thermal-acoustic loads are obtained, and the mechanism of snap-through is revealed through two ways: thermal buckling analysis and thermal modal analysis. which can be used to explain the nonlinear response characteristics.