Search Results (4,895)

Adolescence represents a critical period of neurodevelopment during which brain-derived neurotrophic factor (BDNF) plays a fundamental role in neuronal survival and synaptic plasticity. While exercise-BDNF relationships are well-documented in adults, evidence in adolescents remains limited and inconsistent. This systematic review examined the effects of exercise modalities on circulating BDNF concentrations in adolescent populations. A systematic search was conducted following PRISMA guidelines across multiple databases (FECYT, PubMed, SPORTDiscus, ProQuest Central, SCOPUS, Cochrane Library) through June 2025. Inclusion criteria comprised adolescents, exercise interventions, BDNF outcomes, and randomized controlled trial design. Methodological quality was assessed using the PEDro scale. From 130 initially identified articles, 8 randomized controlled trials were included, with 4 rated as excellent and the other 4 as good quality. Exercise modalities included aerobic, resistance, concurrent, high-intensity interval training, Taekwondo, and whole-body vibration, with durations ranging 6–24 weeks. Four studies demonstrated statistically significant BDNF increases following exercise interventions, four showed no significant changes, and one reported transient reduction. Positive outcomes occurred primarily with vigorous-intensity protocols implemented for a minimum of six weeks. Meta-analysis was not feasible due to high heterogeneity in populations, interventions, and control conditions. Moreover, variation in post-exercise sampling timing further limited comparability of BDNF results. Future research should standardize protocols and examine longer interventions to clarify exercise-BDNF relationships in adolescents. Full article

This study presents the first investigation into the application of ultrasonic vibration-assisted electrical discharge machining (UV-EDM) using graphite electrodes for external cylindrical surface machining—an essential surface in the production of tablet punches and sheet metal-forming dies. A custom ultrasonic horn was designed and fabricated using 90CrSi material to operate effectively at a resonant frequency of 20 kHz, ensuring stable vibration transmission throughout the machining process. A Box–Behnken experimental design was employed to explore the effects of five process parameters—vibration amplitude (A), pulse-on time (Ton), pulse-off time (Toff), discharge current (Ip), and servo voltage (SV)—on two key performance indicators: material removal rate (MRR) and surface roughness (Ra). The optimization process was conducted in two stages: single-objective analysis to maximize MRR while ensuring Ra < 4 µm, followed by a hybrid multi-objective approach combining NSGA-II and the Analytic Hierarchy Process (AHP). The optimal solution achieved a high MRR of 9.28 g/h while maintaining Ra below the critical surface finish threshold, thus meeting the practical requirements for punch surface quality. The findings confirm the effectiveness of the proposed horn design and hybrid optimization strategy, offering a new direction for enhancing productivity and surface integrity in cylindrical EDM applications using graphite electrodes. Full article

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article

This study presents a detailed experimental comparison of the rotordynamic and thermal performance of automotive turbochargers supported by two distinct hydrodynamic bearing configurations: fully floating ring bearings (FFRBs) and semi-floating ring bearings (SFRBs). While both designs are widely used in commercial turbochargers, they exhibit significantly different dynamic behaviors due to differences in ring motion and fluid film interaction. A cold air-driven test rig was employed to assess vibration and temperature characteristics across a range of controlled lubricant conditions. The test matrix included oil supply pressures from 2 bar (g) to 4 bar (g) and temperatures between 30 °C and 70 °C. Rotor speeds reached up to 200 krpm (thousands of revolutions per minute), and data were collected using a high-speed data acquisition system, triaxial accelerometers, and infrared (IR) thermal imaging. Rotor vibration was characterized through waterfall and Bode plots, while jump speeds and thermal profiles were analyzed to evaluate the onset and severity of instability. The results demonstrate that the FFRB configuration is highly sensitive to oil supply parameters, exhibiting strong subsynchronous instabilities and hysteresis during acceleration–deceleration cycles. In contrast, the SFRB configuration consistently provided superior vibrational stability and reduced sensitivity to lubricant conditions. Changes in lubricant supply conditions induced a jump speed variation in floating ring bearing (FRB) turbochargers that was approximately 3.47 times larger than that experienced by semi-floating ring bearing (SFRB) turbochargers. Furthermore, IR images and oil outlet temperature data confirm that the FFRB system experiences greater heat generation and thermal gradients, consistent with higher energy dissipation through viscous shear. This study provides a comprehensive assessment of both bearing types under realistic high-speed conditions and highlights the advantages of the SFRB configuration in improving turbocharger reliability, thermal performance, and noise suppression. The findings support the application of SFRBs in high-performance automotive systems where mechanical stability and reduced frictional losses are critical. Full article

Repairing veneer defects is the key to ensuring the quality of plywood. In order to improve the maintenance quality and material utilization efficiency during the maintenance process, this paper proposes a bidirectional maintenance method based on gear rack transmission and its related equipment. Based on the working principle, a geometric relationship model was established, which combines the structural parameters of the mold, punch, and gear system. Simultaneously, it solves the problem of motion attitude analysis of conjugate tooth profiles under non-standard meshing conditions, aiming to establish a constraint relationship between stamping motion and structural design parameters. On this basis, a constrained optimization model was developed by integrating multi-objective optimization theory to maximize maintenance efficiency. The NSGA-III algorithm is used to solve the model and obtain the Pareto front solution set. Subsequently, three optimal parameter configurations were selected for simulation analysis and experimental platform construction. The simulation and experimental results indicate that the veneer repair time ranges from 0.6 to 1.8 seconds, depending on the stamping speed. A reduction of 28 mm in die height decreases the repair time by approximately 0.1 seconds, resulting in an efficiency improvement of about 14%. The experimental results confirm the effectiveness of the proposed method in repairing veneer defects. Vibration measurements further verify the system’s stable operation under parametric modeling and optimization design. The main vibration response occurs during the meshing and disengagement phases between the gear and rack. Full article

The main bearing of a tunnel boring machine (TBM) is a critical component of the main driving system that enables continuous excavation, and its performance is crucial for ensuring the safe operation of the TBM. Currently, there are few testing technologies for TBM main bearings, and a comprehensive testing and evaluation system has yet to be established. This study presents an experimental investigation using a self-developed, full-scale TBM main bearing test bench. Based on a representative load spectrum, both operational condition tests and life cycle tests are conducted alternately, during which the signals of the main bearing are collected. The observed vibration signals are weak, with significant vibration attenuation occurring in the large structural components. Compared with the test bearing, which reaches a vibration amplitude of 10 g in scale tests, the difference is several orders of magnitude smaller. To effectively utilize the selected evaluation indicators, the entropy weight method is employed to assign weights to the indicators, and a comprehensive analysis is conducted using grey relational analysis. This strategy results in the development of a comprehensive evaluation method based on entropy weighting and grey relational analysis. The main bearing performance is evaluated under various working conditions and the same working conditions in different time periods. The results show that the greater the bearing load, the lower the comprehensive evaluation coefficient of bearing performance. A multistage evaluation method is adopted to evaluate the performance and condition of the main bearing across multiple working scenarios. With the increase of the test duration, the bearing performance exhibits gradual degradation, aligning with the expected outcomes. The findings demonstrate that the proposed performance evaluation method can effectively and accurately evaluate the performance of TBM main bearings, providing theoretical and technical support for the safe operation of TBMs. Full article

Underground blasting vibrations are a critical factor influencing the stability of mine slopes. However, existing studies have yet to establish a quantitative relationship or clarify the underlying mechanisms linking blasting-induced vibrations and slope deformation. Taking the Shilu Iron Mine as a case study, this research develops a dynamic mechanical response model of slope stability that accounts for blasting loads. By integrating slope radar remote sensing data and applying the Pearson correlation coefficient, this study quantitatively evaluates—for the first time—the correlation between underground blasting activity and slope surface deformation. The results reveal that blasting vibrations are characterized by typical short-duration, high-amplitude pulse patterns, with horizontal shear stress identified as the primary trigger for slope shear failure. Both elevation and lithological conditions significantly influence the intensity of vibration responses: high-elevation areas and structurally loose rock masses exhibit greater dynamic sensitivity. A pronounced lag effect in slope deformation was observed following blasting, with cumulative displacements increasing by 10.13% and 34.06% at one and six hours post-blasting, respectively, showing a progressive intensification over time. Mechanistically, the impact of blasting on slope stability operates through three interrelated processes: abrupt perturbations in the stress environment, stress redistribution due to rock mass deformation, and the long-term accumulation of fatigue-induced damage. This integrated approach provides new insights into slope behavior under blasting disturbances and offers valuable guidance for slope stability assessment and hazard mitigation. Full article

Grains and their derivatives play a crucial role as staple foods for the global population. Identifying grains in the food chain that are free from mycotoxin contamination is essential. Researchers have explored various traditional detection methods to address this concern. However, as grain consumption becomes increasingly time-sensitive and dynamic, traditional approaches face growing limitations. In recent years, emerging techniques—particularly molecular-based vibrational spectroscopy methods such as visible–near-infrared (Vis–NIR), near-infrared (NIR), Raman, mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI)—have been applied to assess fungal contamination in grains and their products. This review summarizes research advances and applications of vibrational spectroscopy in detecting mycotoxins in grains from 2019 to 2025. The fundamentals of their work, information acquisition characteristics and their applicability in food matrices were outlined. The findings indicate that vibrational spectroscopy techniques can serve as valuable tools for identifying fungal contamination risks during the production, transportation, and storage of grains and related products, with each technique suited to specific applications. Given the close link between grain-based foods and humans, future efforts should further enhance the practicality of vibrational spectroscopy by simultaneously optimizing spectral analysis strategies across multiple aspects, including chemometrics, model transfer, and data-driven artificial intelligence. Full article

To address the issue of low accuracy in identifying the transition states of rolling bearing performance degradation when relying solely on vibration signals, this study proposed a vibration–temperature fusion-based adaptive method for bearing performance degradation assessments. First, a multidimensional time–frequency feature set was constructed by integrating vibration acceleration and temperature signals. Second, a novel composite sensitivity index (CSI) was introduced, incorporating the trend persistence, monotonicity, and signal complexity to perform preliminary feature screening. Mutual information clustering and regularized entropy weight optimization were then combined to reselect highly sensitive parameters from the initially screened features. Subsequently, an adaptive feature fusion method based on auto-associative kernel regression (AFF-AAKR) was introduced to compress the data in the spatial dimension while enhancing the degradation trend characterization capability of the health indicator (HI) through a temporal residual analysis. Furthermore, the entropy weight method was employed to quantify the information entropy differences between the vibration and temperature signals, enabling dynamic weight allocation to construct a comprehensive HI. Finally, a dual-criteria adaptive bottom-up merging algorithm (DC-ABUM) was proposed, which achieves bearing life-stage identification through error threshold constraints and the adaptive optimization of segmentation quantities. The experimental results demonstrated that the proposed method outperformed traditional vibration-based life-stage identification approaches. Full article

This study proposes a vibration-based approach for detecting and quantifying sub-slab corner voids in airport cement concrete pavement. Scaled down slab models were constructed and subjected to controlled moving load simulations. Acceleration signals were collected and analyzed to extract time–frequency domain features, including power spectral density (PSD), skewness, and frequency center. A finite element model incorporating contact and nonlinear constitutive relationships was established to simulate structural response under different void conditions. Based on the simulated dataset, a random forest (RF) model was developed to estimate void size using selected spectral energy indicators and geometric parameters. The results revealed that the RF model achieved strong predictive performance, with a high correlation between key features and void characteristics. This work demonstrates the feasibility of integrating simulation analysis, signal feature extraction, and machine learning to support intelligent diagnostics of concrete pavement health. Full article

This study aims to develop an integrated wireless monitoring system named MANTiS-32, which leverages an open-source platform to enable autonomous modular operation, high-speed large-volume data transmission via Wi-Fi, and the integration of multiple complex sensors. The MANTiS-32 system is composed of ESP32-based MANTiS-32 hubs connected to eight MPU-6050 sensors each via RS485. Four MANTiS-32 hubs transmit data to a main PC through an access point (AP), making the system suitable for real-time monitoring of modal information necessary for structural performance evaluation. The fundamental performance of the developed MANTiS-32 system was validated to demonstrate its effectiveness. The evaluation included assessments of acceleration and frequency response measurement performance, wireless communication capabilities, and real-time data acquisition between the MANTiS-32 hub and the eight connected MPU-6050 sensors. To assess the feasibility of using MANTiS-32 for performance monitoring, a flexible model cable-stayed bridge, representing a mid- to long-span bridge, was designed. The system’s ability to perform real-time monitoring of the dynamic characteristics of the bridge model was confirmed. A total of 26 MPU-6050 sensors were distributed across four MANTiS-32 hubs, and real-time data acquisition was successfully achieved through an AP (ipTIME A3004T) without any bottleneck or synchronization issues between the hubs. Vibration data collected from the model bridge were analyzed in real time to extract dynamic characteristics, such as natural frequencies, mode shapes, and damping ratios. The extracted dynamic characteristics showed a measurement error of less than approximately 1.6%, validating the high-precision performance of the MANTiS-32 wireless monitoring system for real-time structural performance evaluation. Full article

Material extrusion additive manufacturing (MEAM) has emerged as a versatile and widely adopted 3D printing technology due to its cost-effectiveness and ability to process a diverse range of materials. However, achieving consistent part quality and repeatability remains a challenge, mainly due to variations in process parameters and material behavior during fabrication. In-situ monitoring and advanced process control systems have been increasingly integrated into MEAM to address these issues, enabling real-time detection of defects, optimization of printing conditions, reliability of fabricated parts, and enhanced control over mechanical properties. This review examines the state-of-the-art in-situ monitoring techniques, including thermal imaging, vibrational sensing, rheological monitoring, printhead positioning, acoustic sensing, image recognition, and optical scanning, and their integration with process control strategies, such as closed-loop feedback systems and machine learning algorithms. Key challenges, including sensor accuracy, data processing complexity, and scalability, are discussed alongside recent advancements and their implications for industrial applications. By synthesizing current research, this work highlights the critical role of in-situ monitoring and process control in advancing the reliability and precision of MEAM, paving the way for its broader adoption in high-performance manufacturing. Full article

Endoscopic dacryocystorhinostomy (DCR) is a widely recognized and highly effective procedure. This surgical procedure is performed globally, with minimal modifications across different regions. Background/Objectives: The fundamental goal of DCR is to marsupialize the lacrimal sac into the nasal cavity, which helps eliminate epiphora (excessive tearing) and recurrent dacryocystitis (inflammation of the tear sac). With advancements in technology, new instruments are being developed to minimize risks and maximize efficacy, ultimately improving surgeon convenience, patient safety, and quality of life. One such innovation is piezosurgery, a method of bone cutting that utilizes ultrasound vibrations. Originally prevalent in oral and maxillofacial surgery, piezosurgery is now being applied in many clinical applications. Its primary advantages include the preservation of soft tissues, precise bone cutting, and the ability to work effectively in narrow spaces. Methods: This article outlines the standard technique used at our facility for performing endoscopic dacryocystorhinostomy (DCR) with a piezoelectric system. We describe the preoperative evaluation, intraoperative techniques, and postoperative care to present what we consider the standard procedure in our clinic. Results and Conclusions: Piezosurgery’s selective cutting prevents damage to surrounding soft tissues, making it theoretically advantageous in DCR by preserving tissue integrity. Additional case–control and multicenter studies are necessary to compare its outcomes with those of traditional osteotomy, particularly in relation to the potential increase in operative time. Full article

In this study, we report the development of a novel magnetized coal fly ash-supported nano-silver composite (AgNPs/MCFA) for dual-functional applications in wastewater treatment: the efficient degradation of methyl orange (MO) dye and broad-spectrum antibacterial activity. The composite was synthesized via a facile impregnation–reduction–sintering route, utilizing sodium citrate as both a reducing and stabilizing agent. The AgNPs/MCFA composite was systematically characterized through multiple analytical techniques, including Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and vibrating sample magnetometry (VSM). The results confirmed the uniform dispersion of AgNPs (average size: 13.97 nm) on the MCFA matrix, where the formation of chemical bonds (Ag-O-Si) contributed to the enhanced stability of the material. Under optimized conditions (0.5 g·L⁻¹ AgNO₃, 250 °C sintering temperature, and 2 h sintering time), AgNPs/MCFA exhibited an exceptional catalytic performance, achieving 99.89% MO degradation within 15 min (pseudo-first-order rate constant k_a = 0.3133 min⁻¹) in the presence of NaBH₄. The composite also demonstrated potent antibacterial efficacy against Escherichia coli (MIC = 0.5 mg·mL⁻¹) and Staphylococcus aureus (MIC = 2 mg·mL⁻¹), attributed to membrane disruption, intracellular content leakage, and reactive oxygen species generation. Remarkably, AgNPs/MCFA retained >90% catalytic and antibacterial efficiency after five reuse cycles, enabled by its magnetic recoverability. By repurposing industrial waste (coal fly ash) as a low-cost carrier, this work provides a sustainable strategy to mitigate nanoparticle aggregation and environmental risks while enhancing multifunctional performance in water remediation. Full article

Coatings that are tolerant of poor surface preparation are often used for rapid, real-time maintenance of aging steel surfaces. In this study, a modified epoxy (EP) anti-rust coating was proposed, utilizing methyl gallate (MG) as a rust conversion agent, graphene oxide (GO) as an active functional material, and epoxy resin as the film-forming material. The anti-rust mechanism was investigated using potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM), laser scanning confocal microscopy (LSCM), and the scanning vibration electrode technique (SVET). The results demonstrated that over a period of 21 days, the impedance of the coating increases while the corrosion current density decreases with prolonged soaking time. The coating exhibited a maximum impedance of 2259 kΩ, and a lower corrosion current density of 8.316 × 10⁻³ A/m², which demonstrated a three-order magnitude reduction compared to the corrosion current density observed in mild steel without coating. LSCM demonstrated that MG can not only penetrate the tiny gap between the rust particles, but also effectively convert harmful rust into a complex. SVET showed a much more uniform current density distribution in the micro-zones of mild steel with the anti-rust coating compared to uncoated mild steel, indicating that the presence of GO not only enhanced the electrical conductivity of the coating, but also improved the structure of the coating, which contributed to the high performance of the modified epoxy anti-rust coating. This work highlights the potential application of anti-rust coating in the protection of metal structures in coastal engineering. Full article