Search Results (87)

The layout of acoustic emission sensors plays a critical role in non-destructive structural testing. This study proposes a grid-based optimization method focused on multi-source location results, in contrast to traditional sensor layout optimization methods that construct a correlation matrix based on sensor layout and one source location. Based on the seismic source travel time theory, the proposed method establishes a location objective function based on minimum travel time differences, which is solved through the particle swarm optimization (PSO). Furthermore, based on location accuracy across various configurations, the method systematically evaluates potential optimal sensor locations through grid search. Synthetic tests and laboratory pencil-lead break (PLB) experiments are conducted to compare the effectiveness of PSO, genetic algorithm (GA), and simulated annealing (SA), with the following conclusions. (1) In the synthetic tests, the proposed method achieved an average location error of 1.78 mm, outperforming that based on the traditional layout, GA and SA. (2) For different noise cases, the location accuracy separately improved by 24.89% (σ = 0.5 μs), 12.59% (σ = 2 μs), and 15.06% (σ = 5 μs) compared with the traditional layout. (3) For the PLB experiments, the optimized layout achieved an average location error of 9.37 mm, which improved the location accuracy by 59.15% compared with the traditional layout. Full article

Park golf, introduced to Korea in 2000, has become a popular leisure activity, especially among older people. However, sudden shock between the ball and carbon fiber-reinforced plastic (CFRP) face can increase the risk of injuries, highlighting the need for enhanced damping material. However, restitution and damping are critical properties of golf clubs and often exhibit a conflicting relationship; thus, a method is needed to address this challenge. Therefore, this study aimed to develop a CFRP with an enhanced restitution and damping ratio by incorporating carbon nanotubes and graphene oxide nanofillers into the existing CFRP face material. A drop test apparatus was set up to measure the coefficient of restitution, and the damping properties were evaluated using a pencil lead-breaking test. CNTs exhibited high rebound properties due to their stiffness and hardness. In contrast, GO provided a modest increase in rebound while demonstrating a superior damping ratio, attributed to its layered structure and high internal friction. Based on these results, the optimal nanofiller content was determined as GO 0.025 wt%, showing a minor improvement in rebound performance, a 1033% improvement in vibration damping, and an 84% improvement in acoustic damping. Notably, this finding implies the importance of nanomaterial shape and its interaction with the composite matrix. A double-masked user test with a prototype confirmed enhanced comfort and reduced vibration feedback. The low-vibration components developed in this study are expected to be applicable in future research for controlling the damping ratio under impact or vibrations, such as UAM and helicopters. Full article

This paper summarizes the main findings of a study which aimed to examine the electrochemical oxidation of homovanillic acid (HVA), the final metabolite of dopamine. A pencil graphite electrode (PGE) was used as working electrode and the measurements were performed by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The type and the composition of the graphite leads used as PGE, the pH of the supporting electrolyte, as well as the scan rates were optimized by CV. The analyte was irreversibly oxidized in Britton–Robinson buffer (BRB) solutions. The interpretation of the voltammetric signals and the correlation of the acquired information were the key to addressing the electrode process undergone by HVA at the PGE. The outcomes of the pH and scan rate studies led to the conclusion that two electrons and two protons were involved in the diffusion-controlled process. Using the PGE, a linear relationship between peak current and HVA concentration was obtained between 1.0 × 10⁻⁶ M and 5.0 × 10⁻⁵ M by DPV in BRB with pH 2.0. The detection limit of 3.84 × 10⁻⁷ M was calculated. The accuracy, the precision, and the selectivity of the quantitative method have successfully undergone evaluation. The practical application of the developed voltammetric method was checked by determining the HVA concentration in spiked plasma samples, yielding good recovery values. Full article

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder, characterised by cardinal motor features and a multitude of non-motor manifestations. Among them, cognitive impairment in PD has been recognised as a defined clinical entity, and it might lead to an increased risk of developing dementia. Consequently, the present review aimed to ascertain the available interventions for the training of cognitive abilities in persons with PD (PwPD). Methods: PRISMA guidelines were followed to select studies in the following databases: PubMed, PsycINFO, and Web of Science. Two independent reviewers conducted the different phases of the review, and a third expert was called in to address any doubts/conflicts. Randomised controlled trials and randomised clinical trials concerning cognitive training with cognitive outcomes in PwPD were selected. Results: A total of 28 articles were included. The considered studies applied various experimental interventions for the training of cognitive functions in PwPD: computer-based platforms, exergames, paper-and-pencil programmes, dual-task or treadmill training with action observation therapy, motor imagery, and virtual reality components, interventions targeting precise cognitive domain, tele-rehabilitation, transcranial direct current stimulation, structured cognitive training, and multimodal treatments. Cognitive functions were assessed employing neuropsychological tests, self-report questionnaires, and computerised batteries. Conclusions: Overall, the review reported better performances in the experimental groups compared to the control groups, in several cognitive domains. Structured cognitive training emerged as the most effective strategy to enhance cognitive functioning in PwPD. However, further studies are necessary to determine the most appropriate and useful training and to develop interventions that also consider patients’ quality of life. Full article

In this paper, we propose a novel approach to coordinate-based acoustic emission (AE) source localization to address the challenges of limited and imbalanced datasets from fiber-optic AE sensors used for structural health monitoring (SHM). We have developed a hybrid deep learning model combining four generative adversarial network (GAN) variants for data augmentation with an adapted inception neural network for regression-based prediction. The experimental setup features a single fiber-optic AE sensor based on a tightly coiled fiber-optic Fabry-Perot interferometer formed by two identical fiber Bragg gratings. AE signals were generated using the Hsu-Nielsen pencil lead break test on a grid-marked thin aluminum plate with 35 distinct locations, simulating real-world structural monitoring conditions in bounded isotropic plate-like structures. It is demonstrated that the single-sensor configuration can achieve precise localization, avoiding the need for a multiple sensor array. The GAN-based signal augmentation expanded the dataset from 900 to 4500 samples, with the Wasserstein distance between the original and synthetic datasets decreasing by 83% after 2000 training epochs, demonstrating the high fidelity of the synthetic data. Among the GAN variants, the standard GAN architecture proved the most effective, outperforming other variants in this specific application. The hybrid model exhibits superior performance compared to non-augmented deep learning approaches, with the median error distribution comparisons revealing a significant 50% reduction in prediction errors, accompanied by substantially improved consistency across various AE source locations. Overall, this developed hybrid approach offers a promising solution for enhancing AE-based SHM in complex infrastructures, improving damage detection accuracy and reliability for more efficient predictive maintenance strategies. Full article

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time. Full article

This work presents the study of a reproducible acoustic emission method based on the launching of a metallic sphere and low-cost piezoelectric diaphragm. For this purpose, tests were first conducted on a carbon fiber-reinforced polymer structure, and then on an aluminum structure for comparative analysis. The pencil-lead break (PLB) tests were also conducted for comparisons with the proposed method. Different launching heights and elastic deformations of the structures were investigated. The results show higher repeatability for the sphere impact method, as the PLB is more affected by human inaccuracy, and it was also effective in damage detection. Full article

In this study, we developed a cost-effective and rapid method for fabricating force-sensing resistor (FSR) sensors as an alternative to commercial force sensors. Our aim was to achieve performance characteristics comparable to existing commercial products while significantly reducing costs and fabrication time. We analyzed the material composition of two widely used commercial force sensors: Interlink FSR-402 and Flexiforce A201-1. Based on this analysis, we selected 4B and 9B pencils, which contain high concentrations of graphite, and silicone sealant to replicate these material properties. The fabrication process involved creating piezoresistive sheets by shading A4 copy paper with 4B and 9B pencils to form a uniform layer of graphite. Additionally, we prepared a mixture of 9B pencil lead powder and silicone sealant, ensuring a consistent application on the paper substrate. Measurement results indicated that the force sensor fabricated using a mixture of 9B pencil powder and silicone sealant exhibited electrical and mechanical characteristics closely resembling those of commercial sensors. Load tests revealed that the hand-made sensors provided a proportional voltage output in response to increasing and decreasing loads, similar to commercial FSR sensors. These results suggest that our fabrication method can produce reliable and accurate FSR sensors suitable for various applications, including wearable technology, robotics, and force-sensing interfaces. Overall, this study demonstrates the potential for creating cost-effective and high-performance FSR sensors using readily available materials and simple fabrication techniques. Full article

Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures. Full article

The production of graphene from cost-effective and readily available sources remains a significant challenge in materials science. This study investigates the potential of common pencil leads as precursors for graphene synthesis using the Flash Joule Heating (FJH) process. We examined 6H, 4B, and 14B pencil grades, representing different graphite-to-clay ratios, under varying voltages (0 V, 200 V, and 400 V) to elucidate the relationships among initial composition, applied voltage, and resulting graphene quality. Samples were characterized using Raman spectroscopy, electrical resistance measurements, and microscopic analysis. The results revealed grade-specific responses to applied voltages, with all samples showing decreased electrical resistance post-FJH treatment. Raman spectroscopy indicated significant structural changes, particularly in I_D/I_G and I_2D/I_G ratios, providing insights into defect density and layer stacking. Notably, the 14B pencil lead exhibited unique behavior at 400 V, with a decrease in the I_D/I_G ratio from 0.135 to 0.031 and an increase in crystallite size from 143 nm to 612 nm, suggesting potential in situ annealing effects. In contrast, harder grades (6H and 4B) showed increased defect density at higher voltages. This research contributes to the development of more efficient and environmentally friendly methods for graphene production, potentially opening new avenues for sustainable and scalable synthesis. Full article

Acoustic Emission (AE) testing is a non-destructive evaluation technique that has gained significant attention in pipeline monitoring. Pencil-lead breaks (PLBs) are commonly used in reproducing and characterising sensors used in AE applications and have emerged as a valuable tool for calibration processes. This technique involves breaking a pencil lead by pressing it on the surface of the test structure and applying a bending moment at a given angle on a surface. The applied force produces a local deformation on the test surface, which is released when the lead breaks. The fracture in these PLBs is assumed to be a step unload; however, this is not the case. In this work, a series of PLB source experiments complemented with parallel numerical simulations were carried out to investigate the actual unload rate by correlating the relationship between AE speed, frequency, and power from PLBs. This was achieved by varying the simulation unload rates recorded over a duration of 2 s on a steel pipe and comparing to the experiment. Analysis of the investigated results from the experimental and numerical models suggests that although the AE line structure of a PLB can be reproduced by simulation for short times only (1 µs), the actual unload rate for PLBs is in the region of 10–8 s. It is concluded that FEA has the potential to help in the recovery of the temporal structure from real AE structures. The establishment of this model will provide a theoretical basis for future studies on the monitoring of non-impulsive AE sources such as impact on pipelines using finite element analysis. Full article

Hydrogen atoms can enter into metallic materials through penetration and diffusion, leading to the degradation of the mechanical properties of the materials, and the application of hydrogen barrier coatings is an effective means to alleviate this problem. Zirconia coatings (ZrO₂) have been widely studied as a common hydrogen barrier coating, but zirconia undergoes a crystalline transition with temperature change, which can lead to volumetric changes in the coating and thus cause problems such as cracking and peeling of the coating. In this work, ZrO₂ coating was prepared on a Q235 matrix using a sol-gel method, while yttria-stabilized zirconia (YSZ) coatings with different contents of rare earth elements were prepared in order to alleviate a series of problems caused by the crystal form transformation of ZrO₂. The coating performances were evaluated by the electrochemical hydrogen penetration test, pencil hardness test, scratch test, and high-temperature oxidation test. The results show that yttrium can improve the stability of the high-temperature phase of ZrO₂, alleviating the cracking problem of the coating due to the volume change triggered by the crystalline transition; improve the consistency of the coating; and refine the grain size of the oxide. The performance of YSZ coating was strongly influenced by the yttria doping mass, and the coating with 10 wt% yttria doping had the best hydrogen barrier performance, the best antioxidant performance, and the largest adhesion. Compared with the matrix, the steady-state hydrogen current density of the YSZ coating decreased by 72.3%, the antioxidant performance was improved by 65.8%, and the ZrO₂ coating hardness and adhesion levels were B and 4B, respectively, while YSZ coating hardness and adhesion were upgraded to 2H and 5B. With the further increase in yttrium doping mass, the hardness of the coating continued to improve, but the defects of the coating increased, resulting in a decrease in the hydrogen barrier performance, antioxidant performance, and adhesion. In this work, the various performances of ZrO₂ coating were significantly improved by doping with the rare earth element, which provides a reference for further development and application of oxide coatings. Full article

The aim of this paper is to discuss the effect of the sensor on the acoustic emission (AE) signature and to develop a methodology to reduce the sensor effect. Pencil leads are broken on PMMA plates at different source–sensor distances, and the resulting waves are detected with different sensors. Several transducers, commonly used for acoustic emission measurements, are compared with regard to their ability to reproduce the characteristic shapes of plate waves. Their consequences for AE descriptors are discussed. Their different responses show why similar test specimens and test conditions can yield disparate results. This sensor effect will furthermore make the classification of different AE sources more difficult. In this context, a specific procedure is proposed to reduce the sensor effect and to propose an efficient selection of descriptors for data merging. Principal Component Analysis has demonstrated that using the Z-score normalized descriptor data in conjunction with the Krustal–Wallis test and identifying the outliers can help reduce the sensor effect. This procedure leads to the selection of a common descriptor set with the same distribution for all sensors. These descriptors can be merged to create a library. This result opens up new outlooks for the generalization of acoustic emission signature libraries. This aspect is a key point for the development of a database for machine learning. Full article

Radiation therapy (RT) is an effective treatment for stage IIA and select stage IIB seminomas. However, given the long life expectancy of seminoma patients, there are concerns about the risk of secondary cancers from RT. This study assessed differences in secondary cancer risk for stage II seminoma patients following proton pencil-beam scanning (PBS) and photon VMAT, compared to 3D conformal photon RT. Ten seminoma patients, five with a IIA staging who received 30 GyRBE and five with a IIB staging who received 36 GyRBE, had three RT plans generated. Doses to organs at risk (OAR) were evaluated, and secondary cancer risks were calculated as the Excess Absolute Risk (EAR) and Lifetime Attributable Risk (LAR). PBS reduced the mean OAR dose by 60% on average compared to 3D, and reduced the EAR and LAR for all OAR, with the greatest reductions seen for the bowel, liver, and stomach. VMAT reduced high doses but increased the low-dose bath, leading to an increased EAR and LAR for some OAR. PBS provided superior dosimetric sparing of OAR compared to 3D and VMAT in stage II seminoma cases, with models demonstrating that this may reduce secondary cancer risk. Therefore, proton therapy shows the potential to reduce acute and late side effects of RT for this population. Full article

This study aims to examine the potential mediating role of procrastination in the relationship between fear of missing out and internet addiction. Employing a cross-sectional research design, this study utilized a paper–pencil form to collect data from 315 students (65.4% undergraduate and 66.3% females) between 18 and 32 (Mage = 22.43; SD = 3.81) studying at a state university in Türkiye. Data were collected through the convenience sampling method. The study was designed as a survey model. This design was tested via mediation analysis. The participants completed self-report assessments using the Fear of Missing Out Scale (FoMOs), General Procrastination Scale (GPS-9), and Young Internet Addiction Scale (short form). The findings showed that FoMO had a significant positive direct effect on both procrastination (R² = 0.13) and internet addiction (R² = 0.33). Procrastination also had a significant positive direct effect on internet addiction (R² = 0.34). Additionally, procrastination mediated the relationship between the fear of missing out and internet addiction (β = 0.156, p < 0.001). These findings not only extend the scope of existing research but also hold practical implications for the development of sustainable interventions. It is believed that the findings will contribute to the consideration of procrastination when preparing psychoeducation or group guidance programs for internet addiction. These interventions can effectively address the process through which fear of missing out leads to internet addiction by considering the significant role of procrastination in students. Findings typically suggest that procrastination behavior is a key factor in explaining the association between FoMO and internet addiction. Full article