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This paper proposes a design and implementation scheme for an automatic screwdriver cluster management system based on the Kubernetes cloud platform. To address issues such as low efficiency and unreasonable task allocation in the traditional manual management model, this study deploys the Machinekit system in LXD containers to achieve efficient cluster management and monitoring of multiple screwdrivers. An improved monotonic rate scheduling algorithm, QRM, is introduced to meet the real-time control requirements of the automatic screwdrivers. Experimental results show that, compared to manual management, the Kubernetes management model can significantly improve production efficiency by 42.86 %, validating the effectiveness of this scheme in enhancing the production efficiency and stability of automatic screwdrivers. This provides technical support for the development of automation and intelligence in the manufacturing industry.

Bimetallic structures are heterogeneous systems that combine the advantages of two different metallic materials, thereby providing tailored physical and mechanical properties for specific applications. In recent years, wire arc additive manufacturing has emerged as a promising technology for producing bimetallic structures, thanks to its high deposition rates and material efficiency. The use of wire arc additive manufacturing in the fabrication of bimetallic structures enables the production of different alloys within a single component, thereby paving the way for functionally graded and multi-material designs. The microstructures of bimetallic components produced using this method exhibit heterogeneities depending on the heat input, interlayer thermal stresses, intermetallic phase formation, and processing parameters. Mechanical properties, such as tensile strength, yield strength, and hardness, are directly dependent on the interfacial bonding conditions. At interfaces where brittle intermetallic phases form, a loss of ductility and an increased tendency to fracture are observed. A sufficient metallurgical bonding between two metallic materials results in acceptable mechanical performance in bimetallic structures built using wire arc additive manufacturing. Alloy compatibility, heat input control, and interface properties are critical for the successful application of bimetallic structures produced using this method. This review comprehensively evaluates the microstructural properties, mechanical behavior, and challenges of bimetal structures produced by wire arc additive manufacturing. The novelty of this review lies in its integrative analysis of interface-microstructural evolution and mechanical response, providing a unified perspective that has not been explicitly addressed in earlier studies. In addition, this study aims to provide a guiding framework for future research by presenting the relationship between the microstructure and mechanical properties of bimetallic structures fabricated using wire-arc additive manufacturing.

As a crucial component in aircraft power transmission, gear transmission systems are subjected to time-varying additional inertial loads in a non-inertial environment during aircraft maneuvers. Current dynamic analyses of these systems mostly depend on the inertial coordinate system, which assumes the gearbox is fixed to the ground and ignores the extra effects of base motion. Notably, existing models often use a rigid-flexible coupling approach – treating only key components like shafts as flexible while assuming others such as casings and gear teeth as rigid – which may deviate from the actual dynamic behavior of gear transmissions under maneuvering conditions. To address this limitation, this study establishes a full-flexible coupled multibody dynamics model for gear transmissions under overload level-flight maneuvers. By varying maneuvering acceleration magnitudes, the mechanisms by which maneuvering acceleration affects internal excitation and force characteristics in the system were explored. Results show that maneuvering acceleration induces shaft deformation, causing time-varying fluctuations in the center distance between meshing gears. This further leads to changes in meshing stiffness, transmission error, and tooth backlash. Correspondingly, bearing support force, gear meshing force, and Hertzian contact dynamic stress vary with maneuvering overload-especially the bearing force aligned with the overload direction, which is significantly affected by acceleration. This finding provides a critical theoretical basis for the structural design and dynamic optimization of high-maneuverability aircraft gear transmissions.

This study analyzed the evolution of physical self-concept (SCA), body self-concept (BCA), and perceived physical fitness (PFA) in elementary school adolescents at two different times during the COVID-19 pandemic. The sample consisted of 84 participants, divided by gender and whether or not they practiced physical exercise (PE). The Physical Self-Description Questionnaire (PSDQ), a validated questionnaire, was used to assess the variables under study, and the data was analyzed using descriptive and inferential statistical methods. The results showed that physically active boys had higher levels of SCA, mainly influenced by the SCAF, with significant differences compared to sedentary boys. In girls, there were improvements in various dimensions of BSC and SCAF, regardless of whether they practiced PE. There was also a positive association between BSC and SCAF only among active boys, especially towards the end of the pandemic period. The findings may indicate that PE is associated with more favorable perceptions of physical fitness among boys, although causal relationships cannot be inferred from the present data.

This study presents a numerical investigation of the aerodynamic behavior of the three-element high-lift airfoil 30P30N under low-speed flow conditions relevant to takeoff and landing operations. The analysis is performed using the Reynolds-averaged Navier-Stokes (RANS) equations coupled with the Shear Stress Transport (SST) turbulence model, implemented within a finite-element framework. The primary objective of the work is to assess the capability and limitations of a two-dimensional steady RANS-SST approach for modeling complex multi-element airfoil flows. Numerical simulations are conducted for several discrete angles of attack, and the resulting flow fields and pressure coefficient distributions on all airfoil elements are analyzed in detail. The numerical model used was validated by comparing the obtained results with experimental data obtained in a wind tunnel for the 30P30N configuration. A comparative analysis revealed satisfactory agreement between the calculated and experimental pressure coefficient distributions, particularly at low and moderate angles of attack. This demonstrates the ability of the SST model to accurately describe key aerodynamic processes, including boundary layer formation and development, the influence of adverse pressure gradients, and the interaction of flows between airfoil elements. As the angle of attack increases, localized discrepancies are revealed in zones with pronounced pressure gradients and the potential onset of flow separation. Such deviations are likely due to limitations of the stationary two-dimensional formulation of the problem, which does not take into account spatial and unsteady flow characteristics, the role of which increases as more intense aerodynamic regimes are approached. Despite these limitations, the study provides a systematic evaluation of the finite-element RANS–SST methodology for high-lift airfoil analysis and offers insights into its applicability as a computationally efficient tool for preliminary aerodynamic assessment and validation of multi-element wing configurations.

This study aimed to evaluate whether inter-arch space, expressed as oral cavity volume, increases following malocclusion treatment. A retrospective analysis was conducted on 10 patients (aged 6-28 years) with mixed and permanent dentitions presenting different types of malocclusion. All patients underwent treatment involving transverse and/or sagittal expansion, mandibular posture modification, and occlusal harmonization using Jaw Functional Orthopedics appliances. Inter-arch volume was assessed using dental casts obtained before and after treatment. A standardized acrylic resin filling technique was applied to delimit the intraoral space, and volume measurements were obtained using the fluid displacement method based on Archimedes’ principle. All patients showed an increase in inter-arch volume after treatment, with a mean increase of 30.4 % (range: 14.28 %-4.54 %). The mean volume increased from 16.8 mL pre-treatment to 21.9 mL post-treatment, corresponding to an average gain of 5.1 mL. These findings suggest that malocclusion treatment using Jaw Functional Orthopedics may increase intraoral space, potentially improving conditions for dental alignment and tongue posture. Further studies with larger samples and controlled designs are required to confirm these results.

This study presents a multi-scale finite-discrete element modeling approach for blasting-induced fracture in polycrystalline granite, with explicit consideration of grain boundary effects, to accurately reproduce the mesoscopic heterogeneity and dynamic fracture responses of granite under ultra-small diameter borehole blasting. A Voronoi-based polycrystalline geometric model is established via Neper software to characterize mineral distribution and microstructural anisotropy. Cohesive elements are simultaneously inserted into intragranular and grain boundary regions in Abaqus with differentiated mechanical parameters, and the Jones-Wilkins-Lee (JWL) equation of state is used to apply the dynamic blasting load of PETN explosive. Numerical results agree well with laboratory blasting tests, showing typical failure zones including a crushing zone, a radial fracture zone, and a circumferential tensile fracture zone. The polycrystalline model exhibits prominent non-uniformity and dynamic anisotropy in crack propagation, which is strongly governed by grain morphology and grain boundary properties. Grain boundary strength is identified as a key factor controlling the dynamic fracture mode: with decreasing grain boundary strength, the failure pattern gradually shifts from transgranular fracture to mixed fracture and then to intergranular fracture. Under moderate grain boundary strength, blasting energy is first transmitted inside grains and then released and dissipated at weak grain boundaries, forming a chain-type dynamic failure mechanism: intragranular energy transfer to grain boundary fracture. The proposed method reveals the micro-dynamic evolution mechanism of granite damage under ultra-small diameter blasting and provides a reliable theoretical basis for blasting parameter optimization, rock fragmentation control, and blast-induced vibration prediction in precision rock blasting engineering.

Due to non-stationary noise, the low-voltage power line communication (LPCC) encounters significant challenges in smart grid applications. Conventional denoising techniques, such as wavelet thresholding and adaptive filtering, exhibit limited performance in complex industrial environments, while emerging deep learning models often suffer from insufficient real-time capability. In response to the noise characteristics of low-voltage power line channels and the limitations of traditional impedance matching algorithms, we propose a hybrid CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory Network) architecture. A dual-branch feature fusion mechanism is introduced, which employs parallel processing of time-frequency features via STFT+WVD (Short-Time Fourier Transform+Wigner-Ville Distribution) to enhance noise identification accuracy. A dynamic impedance matching module, optimized in real time using a deep reinforcement learning (DRL)-based gradient descent algorithm, is developed to overcome the poor adaptability of conventional fixed-parameter approaches. Furthermore, a joint noise suppression and signal reconstruction framework is designed to effectively preserve useful signal components while suppressing noise. The experimental results verify that the proposed model achieves an SNR (Signal-to-Noise Ratio) improvement of up to 18.2 dB, outperforming the conventional DnCNN (Denoising Convolutional Neural Network) method by 16.7 %. Under harmonic interference conditions (THD = 15 %), the waveform distortion rate is only 2.1 %, with latency optimized to 9.2 ms, meeting real-time requirements. By incorporating multi-scale feature fusion and dynamic gating mechanisms, the model effectively mitigates mixed interference composed of switching impulse noise and additive white Gaussian noise, which will offer a viable solution for enhancing the reliability of LPCC systems.