Search Results (91)

Poor sleep is strongly associated with stress; however, the mediators of this relationship are not well understood. Cognitive arousal in the form of rumination and worry may mediate this relationship, but previous studies have primarily focused on patients with insomnia or employed cross-sectional designs. This study aimed to explore the causal relationship between sleep quality and stress using two-wave longitudinal data and examining the theoretical assumptions of insomnia models in a healthy, non-clinical student population. Research was conducted using a paper-and-pencil method. The sample included 302 undergraduate students from the University of Zagreb. Most of the participants were female (79.8%) and aged between 18 and 33 years, representing various fields of study. The research problem was examined through multiple mediation analysis. The results confirm our initial hypothesis regarding the bidirectional relationship between stress and sleep quality, which is partially mediated by rumination. The mediating effect of worry is significant only in parallel mediation; poor sleep quality leads to increased rumination (p < 0.01), which in turn predicts more worry (p < 0.01), and greater worry predicts more stress (p < 0.01). The effect sizes indicate that stress has a more significant impact on sleep problems (β = 0.345, p < 0.01) than sleep problems have on stress (β = 0.203, p < 0.01). These findings are important for planning preventive activities and therapeutic interventions. Full article

To extend the spectral utilisation of In₂S₃, an In₂S₃/C₃N₄ nanocomposite was prepared. The effects of different sulphur sources, electrodes, and bias voltages on the optoelectronic performance were examined. Photoelectric properties in response to light sources with wavelengths of 405, 532, 650, 780, 808, 980, and 1064 nm were investigated using Au electrodes and the carbon electrodes with 5B pencil drawings. This study shows that the aggregation states of the In₂S₃/C₃N₄ nanocomposite possess photocurrent switching responses in the broadband region of the light spectrum. Combining two types of partially visible light-absorbing material extends utilisation to the near-infrared region. Impurities or defects embody an electron-donating effect. Since the energy levels of defects or impurities with an electron-donating effect are close to the conduction band, low-energy lights (especially NIR) can be utilised. The non-equilibrium carrier concentration (photogenerated electrons) of the nanocomposites increases significantly under NIR photoexcitation conditions. Thus, photoconductive behaviour is manifested. A good photoelectric signal was still measured when zero bias was applied. This demonstrates self-powered photoelectric response characteristics. Different sulphur sources significantly affect the photoelectric performance, suggesting that they create different defects that affect charge transport and base current noise. It is believed that interfacial interactions in the In₂S₃/C₃N₄ nanocomposite create a built-in electric field that enhances the separation and transfer of electrons and holes produced by light stimulation. The presence of the built-in electric field also leads to energy band bending, which facilitates the utilisation of the light with longer wavelengths. This study provides a reference for multidisciplinary applications. Full article

As an intrinsic characteristic of the target, the pole characteristic of the resonant region is solely determined by the target itself and remains invariant with respect to external factors such as the incident direction and polarization of electromagnetic waves. Consequently, it serves as a critical foundation for target identification. The Matrix Pencil Method (MPM) is currently a widely adopted technique for extracting target poles; however, it typically processes single targets. When multiple targets produce time-domain echoes in the echo signal, the MPM fails to distinguish between individual targets, leading to extracted pole information that does not adequately represent the relevant characteristics of each target. In this paper, we propose a Short-Time Matrix Pencil Method (STMPM), which introduces sliding time windows to differentiate multiple targets in time-domain echoes. By analyzing the variations in poles across each sliding time window, the STMPM can accurately extract the poles corresponding to each target. Full article

Foreign bodies in urological patients are commonly found in the lower urinary tract, especially in the bladder and urethra. Items such as pens, pencils, wires, and cables are often inserted for erotic stimulation, typically associated with alcohol intoxication or psychiatric disorders. The aim of this study is to present the adverse events of non-conventional sexual manipulation and the harmful effects of using atypical objects for masturbation purposes. The study presents a series of case reports describing patients with self-inflicted foreign bodies in the lower urinary tract, collected over 20 years at a single medical center. Most patients were admitted to the hospital with lower urinary tract symptoms (LUTSs) and signs of infection, such as abdominal pain and fever. Only 6 out of 12 patients revealed their intention regarding foreign body insertion. Additionally, 9 out of 12 patients did not attend the follow-up visit. Self-inflicted foreign bodies in the lower urinary tract vary in type and motive. Patients may be reluctant to disclose these circumstances, complicating diagnosis and treatment, which can lead to serious health risks and a reduced quality of life. Full article

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