Search Results (1,421)

Distributed optical vibration sensing (DOVS) provides dense spatiotemporal measurements for pavement and traffic monitoring, but nonstationary background noise, spatially confined responses, and data-quality anomalies complicate vehicle-event detection. This study presents a deterministic, training-free, and interpretable detector for single-lane highway DOVS matrices. The algorithm forms a detrended absolute-amplitude representation and combines percentile-based temporal candidate detection, robust background estimates based on the median and median absolute deviation (MAD), a candidate spatial-width fraction derived from channel-specific thresholds, track-direction evidence, and explicit decision rules. Evaluation on 3085 manually labeled matrices acquired from 2023 to 2026 yielded 87.23% accuracy, 87.42% precision, 87.31% recall, and an F1-score of 87.36%. In a secondary analysis, excluding 98 quality-flagged matrices increased precision to 91.28% and F1-score to 89.20%; the exclusion removed 67 of 196 false positives and no false negatives. Relative to the diagnostic-refinement configuration, the final rule set increased F1-score by 2.173 percentage points, with a matrix-level bootstrap 95% confidence interval of 1.437–2.963 percentage points. The exact McNemar test for paired correctness differences gave p = 6.60 × 10⁻⁹. A sensitivity configuration changed only four classifications and produced no meaningful gain. These results quantify performance at the tested site; narrow responses, upward-like tracks, data-quality anomalies, single-annotator labels, and post hoc rule selection limit broader inference. Full article

This study aimed to verify the morphology, biochemical composition, and thermal characterization of hydrated human amniotic membrane (HAM) processed in an ozonated water hydrodynamic system. This is an in vitro experimental study in which HAM samples were divided into two groups: in natura (IN) and ozonated (O₃). Analyses were performed using histology, Fourier-transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA/DTG), and differential scanning calorimetry (DSC/dDSC). Ozonation for 40 min preserved the biochemical integrity of HAM, maintaining the characteristic vibrational bands of Amides I, II, and III. Histological analysis showed morphological changes in epithelial cells, with partial removal in some regions, while the basement membrane and the scaffold remained preserved. Thermal analysis revealed that the in natura sample presented a bimodal dehydration profile, with a first event occurring between 60 and 65 °C associated with the evaporation of free or weakly bound water, and a second event peaking around 80 °C related to the removal of structural water. In contrast, the ozonated HAM exhibited a unimodal profile, with the mass loss peak shifted to approximately 70 °C. These findings were corroborated by DSC analysis, which showed a reduction in denaturation temperature from approximately 85 °C in the in natura sample to around 75 °C in the ozonated sample. The dDSC analysis confirmed the transition from a bimodal to a unimodal behavior after treatment, indicating a reduced energy barrier for protein denaturation and lower thermal stability of the collagen matrix. These results suggest that ozonation promotes alterations in the epithelial layer, which may favor the loss of both free and bound water. It is concluded that processing with ozonated water induces structural modifications, especially in the epithelial layer, and reduces the thermal stability of hydrated HAM without significantly altering the biochemical signature of collagen. This approach shows potential as an alternative method for membrane processing; however, functional evaluations are required to confirm its clinical applicability. Full article

This work presents the design evolution, fabrication, and characterisation of an additively manufactured ring-type thermal airflow sensor for in-pipe flow monitoring, developed employing exclusively Fused Deposition Modelling (FDM) additive manufacturing technology and a commercially available Carbon Nanotube (CNT)-enriched Biopolymer Polylactic Acid (PLA) composite filament. The design evolution proceeds through three progressive stages. In the first stage, a flat heater element is characterised through Constant-Current (CC) Joule heating experiments in order to derive the corresponding Temperature Coefficient of Resistance (TCR) and Thermal Resistance from the obtained experimental data. Consequently, a Finite Element Method (FEM) model implemented in COMSOL Multiphysics^® and calibrated with the extracted material parameters validates the experimental temperature–power relationship and predicts the convective cooling behaviour at various airflow velocities. In the second stage, the geometry is optimised by introducing a conductive trace with a reduced-cross-section central region; as a result, an equivalent thermal localisation is achieved at approximately 26% lower supplied power with respect to the initial heating element, enabled by the design freedom inherent in the FDM process. We should note that the specific sensing geometry can also be directly embedded into any 3D-printed structural component (e.g., a bracket or housing), enabling simultaneous local thermal heating and/or thermal monitoring together with structural functionality within a single printed part. In the third and final stage—the target device—a fully monolithic ring-type airflow sensor is directly integrated into a 3D-printed pipe segment during the printing process. Under constant-current excitation at 40 mA, the device exhibits a monotonically decreasing resistance with increasing airflow (ΔR ≈ 117 Ω over 0–4 m/s) due to convective cooling, while in a single flow-interruption cycle, approximately 79% of the flow-induced resistance change was recovered upon flow removal, with a residual offset of approximately 3% of the heated baseline. A coupled electrothermal FEM model of the device further supports the experimental response by comparing the simulated temperature rise with the values inferred from resistance measurements, while also clarifying the role of the effective internal convective cooling conditions imposed by the pipe geometry. Key features of the proposed device are low raw-consumables cost, fast on-site manufacturing employing a commercially available desktop 3D printer, monolithic construction free of wire-bonded interconnections, and simplicity, indicating its potential for flow monitoring and condition-based maintenance systems aboard vessels as well as in a wide range of industrial sectors. We should note that the present characterisation was performed under laboratory conditions employing a single prototype per design stage; the effects of humidity, salt exposure, vibration, temperature cycling, and material-batch variability remain to be assessed prior to shipboard deployment. Full article

The aim of this study is to determine the reliability level (the probability of failure-free operation) of a masonry building with load-bearing walls based on the conducted experimental investigations. The objective of the study is to compare the obtained reliability and failure risk values with the corresponding values calculated using the results of the structural certification. In 2017–2018, and subsequently in 2023–2024, a comprehensive structural certification of the multi-apartment residential building stock was carried out for the first time in the city of Almaty. A total of 1609 multi-story masonry buildings with heights of two to four stories were identified. Based on the certification results, quantitative estimates of the prior and posterior probabilities of failure and reliability for masonry buildings were obtained for the first time. The recurrence of earthquakes was taken into account. The novelty of the study lies in the experimental investigation of a three-story masonry building of series 308. The dynamic excitation was generated by an inertial vibration machine installed on the floor slab. As the inertial load increased, the resonant vibration period changed by a factor of three. This indicates that the building underwent significantly nonlinear deformation. The structure sustained substantial damage. Using statistical simulation methods based on the experimental data, the prior probabilities of failure for masonry buildings were calculated. In this case, the seismic action was modeled as a non-stationary random process with the deterministic envelope proposed by F. F. Aptikaev. Probabilistic estimates of the reliability of masonry buildings were obtained from the certification results both with and without taking into account the recurrence of earthquakes. The obtained estimates of reliability and failure probability can be used to develop practical recommendations aimed at reducing risk and expected losses in the event of possible earthquakes. It is recommended that masonry buildings with load-bearing brick walls either be structurally strengthened or be demolished. Full article

This study focuses on the longitudinal dynamic stability of swifts in cruising forward flight, which is critical for their high maneuverability but remains insufficiently investigated. Understanding longitudinal dynamic stability is the essential prerequisite for revealing the physical mechanism underlying their maneuverability: it is the dynamic stability characteristics that determine how the flight state responds to disturbances and control inputs, thereby laying a foundation for subsequent flight control during agile maneuvers. Conventional studies mostly adopt steady or quasi-steady assumptions, which cannot accurately reflect the influence of periodic body vibration. This study combines CFD numerical simulation and dynamic modeling to systematically analyze the unsteady dynamic stability of swifts. A bio-inspired dynamic model is established using the BE3357B airfoil with a 5° sweep angle, and the flapping-wing motion is decomposed into three degrees of freedom: sweeping, pitching, and flapping. Numerical reliability is assessed through grid independence and time-step independence verification. Aerodynamic force and moment trimming are performed on fixed-DOF and free-DOF models, where the latter considers coupled heaving–pitching motion and adjusted trim parameters. Stability analysis is conducted using three aerodynamic derivative methods: fixed velocity, forced oscillation, and Floquet. By solving small perturbation equations, eigenvalues and eigenmodes are obtained. All three methods identify two stable modes: a short-period mode with damping coefficient 0.1236–0.1870 and oscillation period 0.1121 s–0.1380 s, and a long-period mode with damping coefficient 0.2456–0.6203 and damping half-life 3.5803 s–4.8890 s, verifying stability under periodic vibration and unsteady aerodynamic coupling. Flow field results show clear distinct dynamic pressure and drag fluctuation characteristics between the downstroke and the upstroke. The unsteady stability framework provides a theoretical reference for analyzing the longitudinal stability of biomimetic flapping-wing aircraft and offers useful insight for future bird-inspired flight dynamics studies. Full article

Vibration-based mechanical fault diagnosis has become a critical research area, mostly driven by the need to improve equipment reliability and reduce unplanned downtime in industrial settings. Time–Frequency Networks (TFNs) have shown strong potential here, combining interpretable time–frequency transformations with deep learning classifiers in a single framework. This work reproduces the original TFN model from the recent literature and evaluates its noise robustness under additive Gaussian noise (10 dB, 0 dB, $-5$ dB SNR) and impulsive noise at the same levels, across five architectures: Backbone CNN, Random CNN, TFN-Chirplet, TFN-Morlet, and a squeeze-and-excitation attention CNN baseline. The evaluation protocol corrects two methodological issues identified during peer review of an earlier version of this work—window-level data leakage between train and test splits, and selection of the best-performing training epoch rather than a fixed final-epoch result—both of which are shown to materially affect reported outcomes. Under the corrected protocol, TFN-Morlet remains the most noise-robust architecture, with only a 19.09% accuracy drop from clean to $-5$ dB AWGN, approximately 15.5 percentage points better than Backbone CNN under the same conditions; an architectural anomaly reported in the earlier version of this study, in which mild noise appeared to improve an unconstrained CNN’s accuracy, was not reproduced under the corrected protocol and is shown to be an artifact of the original methodological issues. Per-class analysis and multi-model confusion matrices further reveal that misclassifications under severe noise are dominated by confusion between the same defect severity at different fault locations, rather than between different severities at the same location as previously reported. These results indicate that time–frequency-aware convolutional kernels improve both classification accuracy and noise resistance under rigorous, leakage-free evaluation, and that this robustness is not replicated by a generic attention mechanism alone. Full article

In this study, we reveal the emergence of a tri-regime gradient in stiffness across substrate-supported poly(3-alkylthiophene) (P3AT) thin films, comprising an adsorbed region, a bulk-like region, and a free surface region. The stiffness distribution is found to be largely independent of the degree of polymerization but is significantly modulated by side chain length and temperature. Specifically, longer side chains (bead count = 4) expand the adsorbed and free surface regions, while elevating temperature above the glass transition leads to an order-of-magnitude reduction in stiffness. Phonon mode analysis demonstrates a clear inverse correlation between vibrational frequency and both the degree of polymerization and temperature, with side chain length exerting minimal influence. A high phonon mode similarity index between the main and side chains indicates coupled vibrational dynamics. Interfacial energy decomposition confirms that van der Waals interactions, particularly through distinct π–π stacking, dominate the substrate adhesion. These findings provide fundamental insights into the nanoscale thermomechanical properties of P3AT thin films on silica substrates, offering valuable guidance for the interface engineering of P3AT-on-silica systems in organic electronics. Full article

By using 1~2 degrees of freedom (DOF) examples, linear/nonlinear and quasi-periodical systems (QPS) are investigated based on the energy-flow theory (EFT), which reveals the following new findings on chaotic motions. (a) A 1-DOF linear non-damped-forced motion is chaotic if there is no least common multiple (LCM) for the periods of natural and force frequencies. (b) For a 1-DOF nonlinear non-damping-forced system with nonlinear stiffness of $\gamma x^2,\left(\gamma >0\right),$ both the free vibration by initial conditions and the composed motion with forced one excited by a force of given frequency are chaotic; while, when damping is added, the free vibration is damped to zero, but the forced vibration consists of different frequency components showing chaotic characteristics. (c) For a 1-DOF system of natural frequency 1 with nonlinear damping $2\epsilon \left(\dot{x}\right)=-2+\dot{x}+{\dot{x}}^2,$ $\epsilon \left(\dot{x}\right)<0$, ${\epsilon \left(\dot{x}\right)}^2<1,$ the free vibration by the initial conditions ($x_0=1,p_0=0$) is along a non-repeating orbit towards a zero energy-flow limit circle of $\dot{E}=0, p=-1$. The tackled 1-DOF systems of no LCMs belong to a type of QPS, which implies that the orbit of a QPS in the phase space is also a non-closed curve, and based on the EFT, its motion is chaotic. The chaotic orbits of above 1-DOF cases in the phase space, as well as a 2-DOF free vibration case, are numerically examined to confirm the theoretical prediction. The findings may suggest that the LCMs could be an important factor in identifying chaotic motions. It is demonstrated that the generalised potential energy (GPE) and the energy-flow characteristic factors (EFCF), respectively, play a Lyapunov function and exponents to tackle the stabilities and chaotic motions of dynamic systems. Full article

Recent years have seen significant advancements in the field of structural health monitoring (SHM) using computer vision, which has steadily developed into a practical and effective technique for measuring the dynamic properties of structures. With the long-range, non-contact, and easy-to-use features of this measurement technology, some difficulties associated with utilizing conventional techniques to detect the natural frequencies of tall structures can be reduced. Because it is label-free, the method also avoids attaching physical targets or sensors to energized, difficult-to-access structures. This paper proposes a label-free computer vision measurement method, which uses a label-free detection system to determine the tracking feature points, and then tracks the feature points of the structure based on the KLT optical flow method. Finally, the natural frequencies of the structure are obtained by frequency domain analysis of the vibration signal of the feature points. Experimental studies on indoor transmission tower models and outdoor high street lamps were conducted to confirm the viability of the above technology. The test findings were compared with the measurements from a 941B accelerometer, and the impact of several aspects, such as frame rate, resolution, and measuring distance, on the accuracy of the results was examined. The test findings show that the label-free system identifies the first several natural frequencies, including the fundamental, with the modal-frequency estimates agreeing with the 941B accelerometer to within roughly 0.02–0.04 Hz across the first three modes; the larger percentage error at the fundamental reflects its low frequency rather than reduced accuracy, since the absolute discrepancies are comparable across all modes. The study’s findings may serve as a guide for developing software that will be used in the future to assess structural health monitoring using computer vision. Full article

Rolling-element bearings remain a leading cause of unplanned downtime in industrial machinery, while vibration-based condition monitoring has matured, the post-2018 literature has converged on machine-learning classifiers whose interpretability layer remains restricted to expert analysts. This paper presents an operator-centred visualization framework supported by two time-frequency methods: the Zhao–Atlas–Marks Distribution (ZAMD), a Cohen’s-class representation with a cross-term-suppressing cone kernel, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), evaluated through its Hilbert spectral analysis output. Both methods produce two-dimensional time-frequency artefacts with a similar visual structure—impact-related energy bursts that recur at the characteristic fault frequencies—and are presented in side-by-side form for each fault class. A four-stage framework wraps either method with the characteristic fault frequencies (supplied as a comparison reference) and colour-coded, healthy baseline-referenced scaling. The framework is demonstrated on a laboratory bearing rig (KOYO 6302, 600 RPM) across inner-race, outer-race, and ball-spin fault classes. A preliminary readability assessment of annotated ZAMD-generated artefacts, with twelve non-specialist participants from a brewing and packaging industrial context, recorded 89.8% aggregate classification accuracy (194 of 216 trials) at a mean response time of 15.4 s. Because no label-free or alternative-format control conditions were included, this result characterises the annotated artefact as a whole and does not isolate the contribution of the time-frequency representation from that of the annotation layer; it is established for the ZAMD engine only. The two methods are compared as visualization engines—qualitatively, through the structure of their side-by-side time-frequency artefacts, and quantitatively, through computational cost—whereas the non-specialist readability assessment characterises the ZAMD-based framework specifically. CEEMDAN is positioned as a candidate alternative engine whose time-frequency output is shown to be structurally similar but whose operator readability has not been tested with human participants and is identified as future work. Full article

Functionally graded multi-material architected structures fabricated by fused deposition modeling (FDM) were investigated to evaluate their multifunctional mechanical and dynamic performance. Sixteen honeycomb configurations incorporating poly(lactic acid) (PLA), thermoplastic polyurethane (TPU), and wood-filled PLA (WWF-PLA) were designed by systematically varying material distribution, cellular geometry, and structural density as integrated architected configurations. Compression, flexural, dynamic mechanical, free-vibration, density reduction, and water absorption tests were conducted, and the experimental responses were objectively evaluated using the CRITIC–WASPAS multi-criteria decision-making framework. Among the investigated configurations, A16 exhibited the highest overall performance, achieving 41.8 MPa compressive strength, 56.4 MPa flexural strength, 1425 MPa storage modulus, 0.162 loss factor (tan δ), 3.7% damping ratio, and 39% density reduction. Compared with the baseline configuration (A1), A16 demonstrated improvements of 14.5%, 17.0%, 20.8%, 44.6%, 76.2%, and 77.3% in the respective performance metrics. The proposed framework provides an objective approach for ranking integrated architected designs for lightweight multifunctional engineering applications. Full article

Structural health monitoring (SHM) systems based on Internet of Things (IoT) technologies have become an effective approach for continuous monitoring of bridge infrastructures. However, many wireless monitoring systems relying on LoRaWAN or contention-based communication suffer from packet collisions, unpredictable latency, and limited scalability when multiple sensing nodes operate simultaneously. To address these limitations, this study proposes a soft real-time LoRa-based IoT architecture for bridge SHM using a time division multiple access (TDMA) communication framework implemented on an embedded real-time platform. The proposed system integrates distributed vibration sensing nodes, a TDMA-enabled LoRa communication layer, an ESP32-based gateway, and a web-based monitoring database for remote visualization and analysis. The architecture leverages FreeRTOS (v10.4.3) for system-level task scheduling, enabling concurrent execution of sensing, communication, and networking processes across the dual-core ESP32-WROOM-32D platform. Experimental results obtained using a laboratory-scale cable-stayed bridge model demonstrate stable multi-node communication with a packet delivery ratio exceeding 95% and predictable TDMA-scheduled transmission cycles with TDMA slots of 100–200 ms under the evaluated operating conditions. The experiments validate end-to-end operation using a representative three-node deployment, while broader scalability is evaluated analytically through the TDMA capacity model and identified as future work for larger physical deployments. Full article

This paper delves into the free vibration analysis of a rotating non-uniform functionally graded carbon nanotube-reinforced composite (FG-CNTRC) beam with symmetric material distribution, taking into account boundary relaxation. Three common carbon nanotube (CNT) distributions, namely FG-X, UD, and FG-O, are considered. The governing equations of a rotating FG-CNTRC beam with variable cross-section and boundary relaxation are formulated via Hamilton’s principle. Some factors, including the centrifugal force induced by rotation, boundary relaxation, cross-section gradient, and others, substantially complicate the boundary conditions, making it challenging to directly obtain an analytical solution with variable coefficients. To address this, a novel power series solution based on the differential transformation method (DTM) is introduced to discretize the vibration equation and obtain the natural frequency of the rotating FG-CNTRC beam, which forms the core novelty of this study. Comprehensive numerical calculations are carried out, and the reliability of the DTM results is fully verified via comparisons with finite element (FEM) outputs and published reference data. Full article

This paper proposes a high-sensitivity and sensitivity-tunable vibration sensing system based on a switchable loop length optoelectronic oscillator (OEO). Carrier-sideband separation is realized by using an acousto-optic modulator (AOM), and the resonant cavity length is designed to be independent of the sensing fiber arm. Compared with a conventional 10 GHz OEO under the same total loop delay condition, the proposed architecture provides a theoretical sensitivity enhancement of approximately $1.93\times {10}^4$, without requiring a high RF oscillation frequency. Meanwhile, the system oscillates at only 80 MHz, which greatly reduces the implementation difficulty of the frequency detection circuit. The proposed scheme further introduces a mechanical optical switch (MOS) to select intra-loop fibers of different lengths, thereby reconfiguring the equivalent loop delay and the free spectral range of the OEO. Experimental results show that stable single-mode oscillation is achieved at 80.42 MHz with a side-mode suppression ratio of 51 dB. By selecting loop fiber lengths of 1200 m, 500 m and 0 m, frequency-to-displacement sensitivities of 0.892 GHz/cm, 1.93 GHz/cm and 9.27 GHz/cm are obtained respectively, with excellent linearity. A 600 Hz vibration signal is successfully demodulated with a signal-to-noise ratio of 72.1 dB. The proposed method provides a simple and reconfigurable solution for high-precision vibration measurement under different operating conditions. Full article

The long-term durability of concrete structures is significantly affected by moisture. Excessive moisture may cause drying shrinkage, crack formation, and accelerated corrosion of embedded reinforcement; therefore, reliable and non-destructive moisture assessment is essential for structural durability evaluation. In this study, a controlled acoustic measurement method and a machine learning-based classification framework are presented for the non-destructive identification of moisture levels in concrete specimens. A magnet-assisted free-fall steel ball mechanism was used to generate standardized impacts instead of conventional manual hammer excitation. To reduce environmental vibration noise and capture internal material responses, acoustic signals were recorded using a piezoelectric contact microphone. Experiments were conducted on concrete specimens prepared at nine moisture levels under both large-sample (BIG) and small-sample (SMALL) conditions. Power Spectral Density (PSD) and Mel-Frequency Cepstral Coefficients (MFCC) were extracted from the recorded impact signals and used as input features. Individual machine learning classifiers were compared with a hybrid stacking ensemble model to evaluate discriminative performance and probabilistic reliability. The results showed that MFCC features provided higher classification performance than PSD features under both dataset conditions. For the BIG specimens, the MFCC-based model achieved an accuracy of 0.9872, whereas the PSD-based model achieved 0.9811. For the SMALL specimens, MFCC reached an accuracy of 0.9822, while PSD achieved 0.9750. The AUC-ROC values of the proposed model ranged from 0.9980 to 0.9996 in the multi-class classification of nine moisture levels. These findings demonstrate that controlled impact acoustics combined with MFCC-based representation and stacking-based ensemble learning provides a rapid, low-cost, and reliable NDT approach for concrete moisture classification. Full article