Search Results (763)

Selecting optimal measurement points to capture maximum vertical vibration levels induced by metro systems on adjacent buildings is a crucial yet often overlooked task. In this study, an on-site vibration test and simulation analysis of a building near the Nanjing metro line were conducted. A vibration wave screening method based on machine learning algorithms was introduced, with decision trees used to filter anomalous data and supervised learning models to identify data damaged by environmental vibration and to obtain representative vibration inputs. Subsequently, vertical vibration analysis was used to examine the influence of structural components, span lengths, and vertical height on vibration propagation and to quickly determine peak vibration locations. The results showed a positive correlation between span length and maximum vibration levels. Slabs are more sensitive to vibration than columns, with higher levels at the center of slabs than at the edges. Additionally, the vibration amplitude increases and then decreases as the vertical height increases. These findings were confirmed by on-site vibration tests and offer insights for sustainable vibration management in metro-adjacent buildings, supporting resilient infrastructure development. The study also provides guidance for selecting vibration measurement points, enhancing human discomfort assessments to reduce health risks and promote socially sustainable communities. Full article

In recent years, apatite-based materials have garnered significant interest, particularly for applications in tissue engineering. Apatite is most commonly employed as a coating for metallic implants, as a component in composite materials, and as scaffolds for bone and dental tissue regeneration. Among its various forms, hydroxyapatite (HAP) is the most widely used, owing to its natural occurrence in human and animal hard tissues. An emerging area of research involves the use of fluoride-substituted apatite, particularly fluorapatite (FAP), which can serve as a direct fluoride source at the implant site, potentially offering several biological and therapeutic advantages. However, substituting HAP with FAP may lead to unforeseen changes in material behavior due to the differing physicochemical properties of these two calcium phosphate phases. This study investigates the effects of replacing hydroxyapatite with fluorapatite in ceramic–polymer composite materials incorporating β-1,3-glucan as a bioactive polymeric binder. The β-1,3-glucan polysaccharide was selected for its proven biocompatibility, biodegradability, and ability to form stable hydrogels that promote cellular interactions. Nitrogen adsorption analysis revealed that FAP/glucan composites had a significantly lower specific surface area (0.5 m²/g) and total pore volume (0.002 cm³/g) compared to HAP/glucan composites (14.15 m²/g and 0.03 cm³/g, respectively), indicating enhanced ceramic–polymer interactions in fluoride-containing systems. Optical profilometry measurements showed statistically significant differences in profile parameters (e.g., Rp: 134 μm for HAP/glucan vs. 352 μm for FAP/glucan), although average roughness (Ra) remained similar (34.1 vs. 27.6 μm, respectively). Microscopic evaluation showed that FAP/glucan composites had smaller particle sizes (1 μm) than their HAP counterparts (2 μm), despite larger primary crystal sizes in FAP, as confirmed by TEM. XRD analysis indicated structural differences between the apatites, with FAP exhibiting a reduced unit cell volume (524.6 ų) compared to HAP (528.2 ų), due to substitution of hydroxyl groups with fluoride ions. Spectroscopic analyses (FTIR, Raman, ³¹P NMR) confirmed chemical shifts associated with fluorine incorporation and revealed distinct ceramic–polymer interfacial behaviors, including an upfield shift of PO₄³⁻ bands (964 cm⁻¹ in FAP vs. 961 cm⁻¹ in HAP) and OH vibration shifts (3537 cm⁻¹ in FAP vs. 3573 cm⁻¹ in HAP). The glucan polymer showed different hydrogen bonding patterns when combined with FAP versus HAP, as evidenced by shifts in polymer-specific bands at 888 cm⁻¹ and 1157 cm⁻¹, demonstrating that fluoride substitution significantly influences ceramic–polymer interactions in these bioactive composite systems. Full article

Vibrotactile stimulation has applications in a variety of fields, including medicine, virtual reality, and human–computer interaction. Eccentric Rotating Mass (ERM) vibrating motors are widely used in wearable haptic devices owing to their small size, low cost, and low-energy features. User experience with vibrotactile stimulation is an important factor in ergonomic design for these applications. The effects of ERM motor vibrations on upper-extremity sensation and perception, which are important in the design of better wearable haptic devices, have not been thoroughly studied previously. Our study focuses on the relationship between user sensation and perception and on different vibration parameters, including frequency, location, and number of motors. We conducted experiments with vibrotactile stimulation on 15 healthy participants while the subjects were both at rest and in motion to capture different use cases of haptic devices. Eight motors were placed on a consistent set of muscles in the subjects’ upper extremities, and one motor was placed on their index fingers. We found a significant correlation between voltage and sensation intensity (r = 0.39). This finding is important in the design and safety of customized haptic devices. However, we did not find a significant aggregate-level correlation with the perceived pleasantness of the simulation. The sensation intensity varied based on the location of the vibration on the upper extremities (with the lowest intensities on the triceps brachii and brachialis) and slightly decreased (5.9 ± 2.9%) when the participants performed reaching movements. When a single motor was vibrating, the participants’ accuracy in identifying the motor without visual feedback increased as the voltage increased, reaching up to 81.4 ± 14.2%. When we stimulated three muscles simultaneously, we found that most participants were able to identify only two out of three vibrating motors (41.7 ± 32.3%). Our findings can help identify stimulation parameters for the ergonomic design of haptic devices. Full article

Microplastics (MPs) are increasingly recognized as widespread environmental contaminants, with confirmed presence in human tissues and biological fluids through ingestion, inhalation, and direct systemic exposure. Their potential impacts on human health have become an important subject of scientific investigation. The detection and quantification of MPs, particularly nanoplastics, in complex biological matrices remain challenging because of their low concentrations, diverse physicochemical properties, and interference from organic and inorganic matter. This review presents a critical assessment of current methods for the separation and detection of MPs from human-relevant samples. It examines pre-treatment, separation, and analytical approaches including physical filtration, density-based separation, chemical and enzymatic digestion, vibrational spectroscopy, thermal analysis, and electron microscopy, highlighting their principles, advantages, and limitations. Key challenges such as low sample throughput, absence of standardized procedures, and the difficulty of nanoplastic detection are identified as major barriers to accurate exposure assessment and risk evaluation. Recent advances, including functionalized adsorbents, improved anti-fouling membranes, integrated microfluidic systems, and artificial intelligence-assisted spectral analysis, are discussed for their potential to provide sensitive, scalable, and standardized analytical workflows. By integrating current challenges with recent innovations, this review aims to guide multidisciplinary research toward the development of reliable and reproducible detection strategies that can support MPs exposure assessment and inform evidence-based health policies. Full article

Motion perception is a fundamental function of the tactile system, essential for object exploration and manipulation. While human studies have largely focused on discrete or pulsed stimuli with staggered onsets, many natural tactile signals are continuous and rhythmically patterned. Here, we investigate whether phase differences between “simultaneously” presented, “continuous” amplitude-modulated vibrations can induce the perception of motion across fingertips. Participants reliably perceived motion direction at modulation frequencies up to 1 Hz, with discrimination performance systematically dependent on the phase lag between vibrations. Critically, trial-level confidence reports revealed the lowest certainty for anti-phase (180°) conditions, consistent with stimulus ambiguity as predicted by the mathematical framework. I propose two candidate computational mechanisms for tactile motion processing. The first is a conventional cross-correlation computation over the envelopes; the second is a probabilistic model based on the uncertain detection of temporal reference points (e.g., envelope peaks) within threshold-defined windows. This model, despite having only a single parameter (uncertainty width determined by an amplitude discrimination threshold), accounts for both the non-linear shape and asymmetries of observed psychometric functions. These results demonstrate that the human tactile system can extract directional information from distributed phase-coded signals in the absence of spatial displacement, revealing a motion perception mechanism that parallels arthropod systems but potentially arises from distinct perceptual constraints. The findings underscore the feasibility of sparse, phase-coded stimulation as a lightweight and reproducible method for conveying motion cues in wearable, motion-capable haptic devices. Full article

Background/Objectives: Cataract surgery often reveals preexisting corneal astigmatism, which can be corrected using a toric intraocular lens (T-IOL). However, postoperative T-IOL rotation may compromise correction. We investigated T-IOL rotation, focusing on deployment time and proximity to the posterior capsule (PC), using intraoperative optical coherence tomography (iOCT). Methods: Six different T-IOL models were inserted into acrylic simulated lens capsule models under different tacking durations (5 s, 30 s, and 60 s) and temperature conditions (23 °C, 28 °C, and 32 °C). The selection criteria for porcine lenses for examination required that they match human lens dimensions, typical of those used to train cataract surgeons. T-IOL misalignment due to vibration was assessed. Additionally, the impact of temporary intraocular pressure (IOP) reduction on T-IOL proximity to the PC was measured using iOCT in porcine eyes. Results: Tacking time and temperature independently affected T-IOL deployment, with shorter tacking durations and higher temperatures leading to faster deployment. Among lenses tested under identical tacking time and temperature conditions, iSert Micro Toric Aspheric 1-Piece IOL (355T3) had the slowest expansion time, while Avansee™ Preload 1-Piece Toric (YP-T3) had the fastest. Porcine eyes with a corneal white-to-white major axis < 16.0 mm fell within the 95% confidence interval for matching human lens size. Temporarily reducing IOP during surgery improved T-IOL adhesion to the PC, reducing both the occurrence and degree (from 14.0° to nearly 0°) of postoperative rotation. Conclusions: Optimal T-IOL deployment, temporary IOP reduction during surgery, and enhanced adhesion to the PC can reduce the risk and degree of T-IOL rotation. Intraoperative iOCT aids in monitoring T-IOL positioning, which is essential to prevent rotation. Accumulated fluid between the T-IOL and PC may contribute to rotation, which requires further investigation. These findings provide practical strategies for enhancing T-IOL stability and improving the effectiveness of astigmatism correction in cataract surgery. Full article

This paper proposes a hybrid predictive maintenance framework that combines the discriminative power of XGBoost with the interpretability of a Bayesian Network automatically learned from sensor data. Targeted at textile manufacturing equipment operating under Industry 4.0 conditions, the system addresses the trade-off between early fault detection and decision transparency. Sensor data, including vibration, temperature, and electric current, were collected from a multi-needle quilting machine using a custom IoT-based platform. A degradation-aware labeling scheme was implemented using historical maintenance logs to assign semantic labels to sensor readings. A Bayesian Network structure was learned from this data via a Hill Climbing algorithm optimized with the Bayesian Information Criterion, capturing interpretable causal dependencies. In parallel, an XGBoost model was trained to improve classification accuracy for incipient faults. Experimental results demonstrate that XGBoost achieved an F1-score of 0.967 on the high-degradation class, outperforming the Bayesian model in raw accuracy. However, the Bayesian Network provided transparent probabilistic reasoning and root cause explanation capabilities—essential for operator trust and human-in-the-loop diagnostics. The integration of both models yields a robust and interpretable solution for predictive maintenance, enabling early alerts, visual diagnostics, and scalable deployment. The proposed architecture is validated in a real production line and demonstrates the practical value of hybrid AI systems in bridging performance and interpretability for predictive maintenance in Industry 4.0 environments. Full article

Assessing the driver’s state in real time is a critical challenge in modern vehicle safety systems, as human factors account for the vast majority of traffic accidents. Heart rate (HR) is a key physiological indicator of the driver’s condition, yet contactless measurements in dynamic in-vehicle environments remain difficult due to motion artifacts, vibrations, and varying operational conditions. This paper presents a novel two-stage method for HR estimation using a commercial 60 GHz frequency-modulated continuous wave (FMCW) radar sensor, specifically designed and validated for in-vehicle applications. In the first stage, coarse HR estimation is performed using the discrete wavelet transform (DWT) and autoregressive (AR) spectral analysis. The second stage refines the estimate using an inverse application of the relevance vector machine (RVM) approach, leveraging a narrowed frequency window derived from Stage 1. Final HR estimates are stabilized through sequential Kalman filtering (SKF) across time segments. The system was implemented using an Infineon BGT60TR13C radar module installed in the sun visor of a passenger vehicle. Extensive data collection was conducted during real-world driving across diverse traffic scenarios. The results demonstrate robust HR estimations with an accuracy comparable to that of commercial wearable devices, validated against a Polar H10 chest strap. This method offers several advantages over prior work, including short measurement windows (5 s), operation under varying lighting and clothing conditions, and validation in realistic driving environments. In this sense, the method contributes to the field of biomimetics by transferring the biological principles of continuous vital sign perception to technical sensorics in the automotive domain. Future work will explore the fusion of sensors with visual methods and potential extension to heart rate variability (HRV) estimations to enhance driver monitoring systems (DMSs) further. Full article

Rolling bearing failures cause significant production downtime and economic losses. Traditional diagnostic methods suffer from low efficiency, suboptimal accuracy, and susceptibility to human subjectivity. To address these limitations, this paper proposes a novel bearing fault diagnosis (BFD) approach leveraging a Gated Recurrent Unit (GRU) network. Key contributions include: (1) Employing Bayesian optimization to automate the search for the optimal GRU architecture (layers, hidden units) and hyperparameters (learning rate, batch size, epochs), significantly enhancing diagnostic performance (achieving 97.9% accuracy). (2) Integrating a self-attention mechanism to further improve the GRU’s feature extraction capability from vibration signals, boosting accuracy to 99.6%. (3) Demonstrating the robustness of the optimized GRU with self-attention across varying motor speeds (1772 rpm, 1750 rpm, 1730 rpm), consistently maintaining diagnostic accuracy above 97%. Comparative studies with Bayesian-optimized Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models confirm the superior accuracy (97.9% vs. 95.1% and 90.0%) and faster inference speed (0.27 s) of the proposed GRU-based method. The results validate that the combination of Bayesian optimization, GRU, and self-attention provides an efficient, accurate, and robust intelligent solution for automated BFD. Full article

The quality change process of table grapes during cold chain logistics is complex and highly susceptible to vibration-induced damage. Traditional monitoring techniques not only consume significant human and material resources but also cause destructive effects on the fruit structure of table grapes, making them difficult to apply in practical scenarios. Based on this, this paper focuses on table grapes in cold chain business processes and designs a flexible wireless vibration sensor for monitoring the quality of table grapes during cold chain transportation. The hardware component of the system fabricates a flexible wireless vibration sensing for monitoring the quality of the table grape cold chain. In contrast, the software component develops corresponding data acquisition and processing functionalities. Using Summer Black table grapes purchased from Tianjin Hongqi Agricultural Market as the research subject, correlation and quality monitoring models for the cold chain process of table grapes were constructed. After Z-score standardization, the prediction results based on the MLR model achieved R² values all greater than 0.87 and RPD values all exceeding 2.7. Comparisons with other regression models demonstrated its optimal fitting performance for monitoring the quality of the cold chain for table grapes. This achieves non-destructive and high-precision data acquisition and processing during the cold chain process of table grapes, wirelessly transmitting results to terminal devices for real-time visual monitoring. Full article

Tactile sensation is a crucial sensory pathway for humans to acquire information from the environment, and vibration feedback is one form of tactile feedback, offering advantages such as low cost, ease of integration, and high comfort. Avoiding mechanical crosstalk without changing the spacing between vibration units is a significant challenge in the design of haptic interfaces. This work focuses on the joint optimization design of vibration source characteristics and packaging materials of vibration units. From a theoretical modeling perspective, we explore the correlation between material properties and the amplitude of vibrations generated on the skin surface. A three-layer vibration unit optimization design scheme using a pogo pin structure is thus proposed. Parameters are optimized through finite element analysis, and experimental results prove that the three-layer vibration unit with pogo pins has amplitude modulation capabilities, laying the foundation for the design of array-based vibration tactile feedback interfaces and human-inspired grasp control. Full article

This study presents a human-centric, data-driven modeling framework for the intelligent evaluation and classification of vibration-reducing (VR) gloves used in hand-transmitted vibration environments. Recognizing the trade-offs between protection and functionality, the integrated performance assessment incorporates three critical and often conflicting metrics: manual dexterity, grip strength, and distributed vibration transmissibility at the palm and fingers. Three independent experiments involving fifteen participants were conducted to evaluate the individual performance of ten commercially available VR gloves fabricated from air bladders, polymers, and viscoelastic gels. The effects of VR gloves on manual dexterity, grip strength, and distributed vibration transmission were investigated. The resulting experimental data were used to train and tune seven different machine learning models. The results suggested that the AdaBoost model demonstrated superior predictive performance, achieving 92% accuracy in efficiently evaluating the integrated performance of VR gloves. It is further shown that the proposed data-driven model could be effectively applied to classify the performances of VR gloves in three workplace conditions based on the dominant vibration frequencies (low-, medium-, and high-frequency). The proposed framework demonstrates the potential of AI-enhanced intelligent actuation systems to support personalized selection of wearable protective equipment, thereby enhancing occupational safety, usability, and task efficiency in vibration-intensive environments. Full article

Predictive maintenance is a crucial component of smart manufacturing in Industry 4.0, utilizing data from IoT sensor networks and machine learning algorithms to predict equipment failures before they happen. This proactive approach enables timely maintenance of equipment and machinery, reducing unplanned downtime, extending equipment lifespan, and enhancing overall system reliability, ultimately leading to more efficient and cost-effective operations. Conventional machinery and equipment maintenance approaches often rely on periodic manual inspections, human observations, and monitoring, which can be time-consuming, inefficient, and resource-intensive. Therefore, implementing automation through predictive models based on IoT and machine learning techniques is crucial for optimizing the maintenance of machinery and equipment. This paper aims to leverage machine learning techniques to develop predictive maintenance models, including electric motor temperature and vibration prediction, using data from established sensor networks and production data from ERP systems. The models are designed to predict potential issues within the next ten minutes, such as whether temperature or vibration levels will exceed predefined thresholds. Full article

A triboelectricity-enhanced piezoelectric nanogenerator (PENG) based on pressure-processed multi-electrospun polymeric layers is herein developed for efficient vibrational energy harvesting. The hybridization of piezoelectric and triboelectric mechanisms through electrospinning has been utilized to enhance electrical output by increasing contact areas and promoting alignment within piezoelectric materials. A multi-layer structure comprising alternating poly (vinylidene fluoride) (PVDF) and poly (hexamethylene adipamide) (PA 6/6) exhibits superior electrical performance. A lateral Janus configuration, providing distinct positive and negative triboelectric polarities, has further optimized device efficiency. This approach introduces a novel operational mechanism, enabling superior performance compared to conventional methods. The fiber-based architecture ensures exceptional flexibility, low weight, and a high surface-to-volume ratio, enabling enhanced energy harvesting. Experimentally, the PENG achieved an open-circuit voltage of 14.59 V, a short-circuit current of 205.7 nA, and a power density of 7.5 mW m⁻² at a resistance of 30 MΩ with a five-layer structure subjected to post-processing under pressure. A theoretical model has mathematically elucidated the output results. Long-term durability (over 345,600 cycles) has confirmed its robustness. Demonstrations of practical applications include monitoring human joint motion and respiratory activity. These results highlight the potential of the proposed triboelectricity-enhanced PENG for vibrational energy harvesting in flexible and wearable electronic systems. Full article

Cancer is still the world’s most prevalent cause of death, and the limited efficacy of current treatments highlights the requirement for new therapeutic approaches. In this study, neodymium (Nd)-doped cobalt ferrite (CoFe_2₋zNd_zO₄, z = 0; 0.01; 0.02; 0.03; 0.05; 0.1) nanoparticles (Nd0-Nd5) were synthesized via the combustion method. The structural, morphological, and magnetic properties were characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), vibrating sample magnetometry (VSM), and scanning transmission electron microscopy (STEM) analysis. The synthesized compounds demonstrated single-phase spinel structures, with morphological differences observed between undoped and Nd-doped samples. The biological activity of the nanoparticles was evaluated on immortalized human keratinocytes (HaCaT) and on cancer cell lines: melanoma (A375), breast adenocarcinoma (MCF-7), and pancreatic carcinoma (PANC-1). The cytotoxic effects of Nd0-Nd5 (50–1000 μg∙mL⁻¹) were assessed through Alamar Blue and lactate dehydrogenase (LDH) release assays. The results indicated a dose-dependent cytotoxic effect in cancer cell lines. Changes in cell morphology, suggesting the induction of the apoptotic processes, were observed through immunofluorescence staining of F-actin and nuclei. These findings highlight the potential of Nd-doped cobalt ferrite nanoparticles as selective anticancer agents, warranting further investigation to fully elucidate their mechanisms of action and therapeutic applicability. Full article