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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.

Efficient water management is a key requirement for the sustainability of modern agroecological systems, particularly in drip irrigation networks where emitter clogging caused by suspended solids remains a critical problem. Centrifugal hydrocyclones represent an energy-efficient and low-maintenance pre-filtration technology that separates solid particles from irrigation water without using filter media. However, the optimization of hydrocyclone design for small-scale irrigation applications requires a comprehensive understanding of internal hydrodynamics and particle behavior. In this study, a novel high-efficiency centrifugal hydrocyclone was developed and compared with a conventional design using three-dimensional CFD simulations in COMSOL Multiphysics 6.1. The v2-f turbulence model was employed to accurately resolve near-wall effects and capture the vortex-core dynamics, while the motion of suspended particles was simulated using a Lagrangian particle-tracking approach. The numerical results reveal that the proposed design improves the separation efficiency by up to 71 % compared to the classical configuration, with only a marginal increase (≈ 3 %) in pressure drop. The enhanced flow symmetry and stabilized vortex structure in the new hydrocyclone contribute to more uniform velocity and pressure distributions. These findings demonstrate the effectiveness of the proposed model in improving water quality and operational reliability of drip irrigation systems, offering practical implications for sustainable water management in agroecological applications.

Existing motorcycle aerodynamic studies focus primarily on determining the aerodynamic drag coefficient or conducting experimental wind tunnel studies. Detailed analysis of the flow structure, turbulent kinetic energy distribution, and flow characteristics in various cross-sections of the computational domain using open CFD platforms is underrepresented. Furthermore, the literature provides a limited number of studies comprehensively analyzing the evolution of vortex structures behind a motorcycle using the SST turbulence model in the OpenFOAM environment. This paper presents a numerical study of the aerodynamic characteristics of a motorcycle using the OpenFOAM software package based on the RANS system of equations in conjunction with the SST turbulence model. To solve the discretized equations, the SIMPLE algorithm was used in conjunction with the GAMG multigrid solver, ensuring stability and accelerated convergence of the computational process. The distributions of the velocity, pressure, and turbulent kinetic energy fields in the longitudinal and transverse cross-sections of the computational domain were obtained. The analysis revealed flow stagnation zones, low-pressure areas, and the formation of recirculation structures behind the motorcycle, which significantly influence aerodynamic drag. The scientific novelty of this study lies in a comprehensive, layer-by-layer analysis of the turbulent flow structure around the motorcycle's full geometry, with a detailed study of the distribution of turbulent kinetic energy in various cross-sections using the open-source OpenFOAM software. The results obtained can be used in further aerodynamic optimization of the motorcycle design to reduce drag and improve stability.

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.

Nasal breathing plays a fundamental role in the functional balance of the Stomatognathic System and in the harmonious development of craniofacial structures. However, obstruction of the upper airways can lead to the establishment of predominantly oral breathing, a condition frequently associated with functional and morphological alterations during childhood. The aim of this study was to conduct a narrative literature review on the repercussions of mouth breathing on craniofacial growth in children. Studies addressing the etiology of mouth breathing, its craniofacial and functional repercussions, diagnostic methods, and therapeutic possibilities were analyzed. The literature shows that mouth breathing is associated with alterations in the facial growth pattern, including increased facial height, maxillary atresia, lip incompetence, mandibular retrognathism, open and crossed bites, as well as narrowing of the upper airways. Functional impairments related to chewing, swallowing, speech, sleep quality, school performance, and cognitive development were also observed. Early diagnosis and interdisciplinary intervention are shown to be essential for preventing or minimizing the repercussions of mouth breathing on children’s growth and development. It is concluded that mouth breathing can exert a significant influence on the craniofacial and functional development of children, reinforcing the importance of early identification and intervention.

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.

Optimization plays a crucial part in the plan, control, and operation of modern power electronic systems. Traditional methods, viz. Genetic Algorithm, Particle Swarm Optimization (PSO), and Differential Evolution have been widely used to optimize converter efficiency, stability, and performance. However, the increasing complexity of renewable energy systems, electric vehicles, and smart grids necessitate advanced optimization frameworks. This paper discovers the incorporation of Artificial Intelligence, Machine Learning, and Quantum Machine Learning into power electronics optimization. Reinforcement Learning is investigated for adaptive control of converters and motor drives, while Neural Networks are explored for predictive control. Hybrid optimization methods, viz Fuzzy with PSO and Artificial Neural Networks with Genetic Algorithm, are presented to improve convergence speed and accuracy. Simulation platforms, like MATLAB and Python are leveraged to evaluate optimization frameworks. Finally, we introduce a novel deep learning-based predictive controller augmented with QML techniques for converters in EV and renewable systems. We propose a DL and QML-based predictive controller that attains around 15 % lower converter losses compared to classical methods.

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.