Search Results (3,111)

Sound reproduction is the electro-mechanical re-creation of sound waves using analogue and digital audio equipment. Although sound reproduction implies that repeated acoustical events are close to identical, numerous fixed and variable conditions affect the acoustic result. To arrive at a better understanding of the magnitude of deviations in sound reproduction, amplitude deviation and phase distortion of a sound signal were measured at various reproduction stages and compared under a set of controlled acoustical conditions, one condition being the presence of a human subject in the acoustic test environment. Deviations in electroacoustic reproduction were smaller than ±0.2 dB amplitude and ±3 degrees phase shift when comparing trials recorded on the same day (Δt < 8 h, mean uncertainty $u$ = 1.58%). Deviations increased significantly with greater than two times the amplitude and three times the phase shift when comparing trials recorded on different days (Δt > 16 h, $u$ = 4.63%). Deviations further increased significantly with greater than 15 times the amplitude and the phase shift when a human subject was present in the acoustic environment ($u$ = 24.64%). For the first time, this study shows that the human body does not merely absorb but can also cause amplification of sound energy. The degree of attenuation or amplification per frequency shows complex variance depending on the type of reproduction and the subject, indicating a nonlinear dynamic interaction. The findings of this study may serve as a reference to update acoustical standards and improve accuracy and reliability of sound reproduction and its application in measurements, diagnostics and therapeutic methods. Full article

Thermally modified wood is primarily used in exterior applications due to its enhanced resistance to biotic degradation. However, reduced mechanical performance limits its structural use. This study investigates the structural potential of high-temperature-treated European beech timber (Fagus sylvatica, L.) and evaluates its mechanical properties and grading models for structural design. Timber from 32 beech logs was air-dried and divided into untreated (NoTMW) and thermally modified (TMW) groups. Thermal modification was carried out commercially in an oxidizing atmosphere at 190 °C. All specimens were visually graded according to DIN 4074-5 and assessed using acoustic non-destructive methods before testing in four-point bending following EN 408. Modulus of elasticity (MOE), modulus of rupture (MOR), and density were determined, and characteristic values were calculated according to EN 384. On average, TMW exhibited a 17% reduction in bending strength compared to untreated wood, while both static and dynamic MOE were not significantly affected. The multiple regression model only slightly improved bending strength prediction compared with single linear regression based on global modulus, as the R²-value increased from 17% to 19%. The prediction of stiffness of thermally treated beech timber was greatly improved by combining local and acoustic moduli, explaining 76% of the total variation. Full article

This study aims to elucidate the deterministic correlation between the microscopic fracture mechanisms and the multi-domain characteristics of acoustic emission in reinforced concrete beams under cyclic loading. Cyclic incremental tests were designed and conducted, with synchronized application of digital image correlation and AE techniques to capture the entire damage evolution process and corresponding signal responses throughout. The findings reveal that the damage stage division based on mechanical responses is consistent with that based on AE responses. Damage accumulation and irreversible processes can be clearly characterized by AE activity, and the systematic decrease in the Felicity ratio quantitatively verifies the irreversible accumulation of damage. Under cyclic loading, different microscopic fracture mechanisms generate AE frequency-domain signatures with statistically significant differences. A damage identification model integrating the Felicity ratio and multi-band energy features was developed, achieving an accuracy of 88.89% in identifying macroscopic damage stages. This research quantitatively confirms the effectiveness of AE characteristics as reliable identifiers of microscopic fracture mechanisms, providing a new basis for advancing structural health monitoring technologies grounded in fracture mechanism recognition. Full article

The pressure-gradient Micro-Electro-Mechanical Systems (MEMS) vector hydrophone is a novel type of sensor capable of simultaneously acquiring both scalar and vectorial information within an acoustic field. Conventional azimuth estimation methods struggle to achieve high-resolution localization using a single pressure-gradient MEMS vector hydrophone. In practical marine environments, the multiple signal classification (MUSIC) algorithm is hampered by significant resolution performance loss. Similarly, the complex acoustic intensity (CAI) method is constrained by a high-resolution threshold for multiple targets, often resulting in inaccurate azimuth estimates. Therefore, a cross-spectral model between the acoustic pressure and the particle velocity for the pressure-gradient MEMS vector hydrophone was established. Integrated with an improved particle swarm optimization (IPSO) algorithm, a high-resolution azimuth estimation method utilizing this hydrophone is proposed. Furthermore, the corresponding Cramér-Rao Bound is derived. Simulation results demonstrate that the proposed algorithm accurately resolves two targets separated by only 5° at a low signal-to-noise ratio (SNR) of 5 dB, boasting a root mean square error of approximately 0.35° and a 100% success rate. Compared with the CAI method and the MUSIC algorithm, the proposed method achieves a lower resolution threshold and higher estimation accuracy, alongside low computational complexity that enables efficient real-time processing. Field tests in an actual seawater environment validate the algorithm’s high-resolution performance as predicted by simulations, thus confirming its practical efficacy. The proposed algorithm addresses key limitations in underwater detection by enhancing system robustness and offering high-resolution azimuth estimation. This capability holds promise for extending to multi-target scenarios in complex marine settings. Full article

Objective: We examine whether a finite-range scalar–tensor modification of gravity can be simultaneously compatible with cosmological background data, galaxy rotation curves, and local/astrophysical consistency tests, while satisfying the luminal gravitational-wave propagation constraint ($c_T=1$) implied by GW170817 at low redshifts. Methods: We formulate the model at the level of an explicit covariant action and derive the corresponding field equations; for cosmological inferences, we adopt an effective background closure in which the late-time dark-energy density is modulated by a smooth activation function characterized by a length scale $\lambda$ and amplitude $ϵ$. We constrain this background model using Pantheon+, DESI Gaussian Baryon Acoustic Oscillations (BAOs), and a Planck acoustic-scale prior, including an explicit $\Lambda$CDM comparison. We then propagate the inferred characteristic length by fixing $\lambda$ in the weak-field Yukawa kernel used to model 175 SPARC galaxy rotation curves with standard baryonic components and a controlled spherical approximation for the scalar response. Results: The joint background fit yields ${\Omega }_m=0.293\pm 0.007$, $\lambda =7.{69}_{-1.71}^{+1.85}$$\mathrm{Mpc}$, and $H_0=72.33\pm 0.50$$\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$. With $\lambda$ fixed, the baryons + scalar model describes the SPARC sample with a median reduced chi-square of ${\chi }_{\nu }^2=1.07$; for a 14-galaxy subset, this model is moderately preferred over the standard baryons + NFW halo description in the finite-sample information criteria, with a mean $\Delta$AICc outcome in favor of the baryons + scalar model (≈2.8). A Vainshtein-type screening completion with $\Lambda =1.3\times {10}^{-8}$ eV satisfies Cassini, Lunar Laser Ranging, and binary pulsar bounds while keeping the kpc scales effectively unscreened. For linear growth observables, we adopt a conservative General Relativity-like baseline (${\mu }_0=0$) and show that current $f{\sigma }_8$ data are consistent with ${\mu }_0\simeq 0$ for our best-fit background; the model predicts $S_8=0.791$, consistent with representative cosmic-shear constraints. Conclusions: Within the present scope (action-level weak-field dynamics for galaxy modeling plus an explicitly stated effective closure for background inference), the results support a mutually compatible characteristic length at the Mpc scale; however, a full perturbation-level implementation of the covariant theory remains an issue for future work, and the role of cold dark matter beyond galaxy scales is not ruled out. Full article

The integration of artificial intelligence (AI) with ultrasonic biosensing presents a transformative opportunity for enhancing diagnostic accuracy in agricultural and biomedical applications. This study develops a data-driven deep learning model to address the challenge of acoustic artifacts in B-mode ultrasound imaging, specifically for sow pregnancy diagnosis. We designed a biosensing system centered on a mechanical sector-scanning ultrasound probe (5.0 MHz) as the core biosensor for data acquisition. To overcome the limitations of traditional filtering methods, we introduced a lightweight Deep Neural Network (DNN) based on the YOLOv8 architecture, which was data-driven and trained on a purpose-built dataset of sow pregnancy ultrasound images featuring typical artifacts like reverberation and acoustic shadowing. The AI model functions as an intelligent detection layer that identifies and masks artifact regions while simultaneously detecting and annotating key anatomical features. This combined detection–masking approach enables artifact-aware visualization enhancement, where artifact regions are suppressed and diagnostic structures are highlighted for improved clinical interpretation. Experimental results demonstrate the superiority of our AI-enhanced approach, achieving a mean Intersection over Union (IOU) of 0.89, a Peak Signal-to-Noise Ratio (PSNR) of 34.2 dB, a Structural Similarity Index (SSIM) of 0.92, and clinically tested early gestation accuracy of 98.1%, significantly outperforming traditional methods (IoU: 0.65, PSNR: 28.5 dB, SSIM: 0.72, accuracy: 76.4). Crucially, the system maintains a single-image processing time of 22 ms, fulfilling the requirement for real-time clinical diagnosis. This research not only validates a robust AI-powered ultrasonic biosensing system for improving reproductive management in livestock but also establishes a reproducible, scalable framework for intelligent signal enhancement in broader biosensor applications. Full article

The mitigation of traffic noise is essential for the development of sustainable and livable urban environments, a goal that is directly contingent on addressing tire-pavement interaction noise (TPIN) as the dominant acoustic pollutant at medium to high vehicle speeds. This comprehensive review addresses a critical gap in the literature by systematically analyzing the damping properties of pavement systems through a unified, multi-scale framework—from the molecular-scale viscoelasticity of asphalt binders to the composite performance of asphalt mixtures. The analysis begins by synthesizing state-of-the-art testing and characterization methodologies, which establish a clear connection between macroscopic damping performance and the underlying viscoelastic mechanisms coupled with the microscopic morphology of the binders. Subsequently, the review critically assesses the influence of critical factors, such as polymer modifiers including rubber and Styrene-Butadiene-Styrene (SBS), temperature, and loading frequency. This examination elucidates how these variables govern molecular mobility and relaxation processes to ultimately determine damping efficacy. A central and synthesizing conclusion emphasizes the paramount importance of the asphalt binder’s properties, which serve as the primary determinant of the composite mixture’s overall acoustic performance. By delineating this structure-property-performance relationship across different scales, the review consolidates a foundational scientific framework to guide the rational design and informed material selection for next-generation asphalt pavements. The insights presented not only advance the fundamental understanding of damping mechanisms in pavement materials but also provide actionable strategies for creating quieter and more sustainable transportation infrastructures. Full article

This article presents an advanced processing workflow for a Seismic While Drilling (SWD) dataset acquired using Distributed Acoustic Sensing (DAS) in a cross-well setting at the Otway International Test Centre (OITC) in Victoria, Australia, where no pilot signals were recorded. Recording the drill bit signature enables and simplifies the decoding of passive seismic signals emitted by the drill bit while drilling. In conventional SWD, a measured drill bit signature is used to correlate passive seismic recordings and to determine source trigger times, analogous to vibroseis processing. Without this reference, both source timing and signature must be inferred from the recorded wavefield. This can typically be achieved by backpropagating the recorded seismic data over short time windows, estimating the source location and trigger time based on the peak RMS energy in space and time. However, to simplify the processing of SWD data, a data processing workflow is presented, guided by travel time and seismic modelling, which transforms passive SWD data into active equivalents. The transformed data can then be used to characterize the subsurface by implementing travel time tomography and cross-well imaging. The results demonstrate reliable velocity and structural information can be recovered from DAS-based SWD data without pilot measurements, enabling simplified and scalable deployment of passive seismic while-drilling workflows. Full article

Fibrin clots with strain-hardening characteristics exhibit pronounced material nonlinearity and acoustic dispersion under ultrasound, leading to waveform distortion and shock formation during finite-amplitude wave propagation. However, peak-shock stress is limited by viscoelastic dissipation and dispersion, constraining the efficiency of ultrasound in applications such as thrombus ablation. To overcome this limitation, a shock wave amplification method using designed multi-wave-packet sequences is proposed. Based on a power-law model from quasi-static compression tests, shock generation under a single sinusoidal pulse was first simulated. The dual-wave-packet chasing strategy was then developed, in which the amplitude, frequency, and time delay of the second packet were tuned to achieve effective superposition with the precursor. The waveform superposition factor (WSF) was introduced for quantitative evaluation. Numerical results demonstrate that this strategy can significantly increase the peak-shock-wave stress, with a maximum gain of 22.7%. Parametric analysis further identified amplitude as the dominant factor influencing wavefront steepness and amplification effectiveness. This study provides a novel method and theoretical support for developing efficient and controllable ultrasonic sequences for thrombolysis. Full article

Herbage intake by grazers and browsers is of fundamental importance to agricultural ecosystems worldwide but is also notoriously difficult to quantify. The intake process is mediated by herbage comminution in the mouth. The attendant chew actions generate sound bursts that can be detected acoustically and analyzed to help elucidate the entire process. Goats consuming a single plant species were acoustically monitored in order to (i) determine the sensitivity of the chewing effort to the large variation in bite mass and satiety level and (ii) estimate how well the amount of herbage consumed can be predicted by counting chews. Experiments used hand-constructed patches containing bite-sized carob (Ceratonia siliqua L.) leaflets of a pre-determined mass that were presented to six goats, individually, with acoustic sensors attached to their horns. Experiment 1 determined the chewing effort and the sequence of bites and chews for three bite masses across five levels of total intake. Experiment 2 determined the chewing effort and the chew sequence at three levels of satiety, achieved by control of the feeding regime, using a single bite mass across three levels of total intake. In Experiment 1, the global chewing coefficient was ≈4 chews g⁻¹ fresh mass ingested (≈10 chews g⁻¹ dry matter). For an individual animal, the chewing coefficient was fairly stable, being influenced mildly by bite mass, but the variation between animals was large. In Experiment 2, the chewing coefficient was again fairly stable in an individual animal, although the chewing effort was slightly elevated at low satiety. At the population level, and for the most relevant range of intake levels, inverse regression of the pooled data from both experiments estimated the two-sided 95% confidence interval of the predicted intake of carob leaves to be <10% of the predicted value. If chewing coefficients can be estimated locally, usefully precise intake predictions should be attainable for the tested vegetation. These results are promising for the future potential of acoustic monitoring, although significant challenges remain. Full article

The complex modulus is one of the intrinsic properties of bituminous materials, and, hence, is of importance for their rheological characterization. It was shown by various authors that the complex modulus of asphalt mixtures can be calculated from dynamic modulus measurements using the Resonant Acoustic Spectroscopy (RAS). This paper extends the RAS technique to bitumen. For the purpose of validation, rheological data for the same bitumen are also derived from standard Dynamic Shear Rheometer (DSR) tests, and the master curves resulting from both methods are compared. The laboratory programme comprised a temperature range from −30 °C to 20 °C, and four different bitumens in unaged and aged condition, resulting in 36 different test variants. RAS successfully characterizes the complex modulus of bitumen and reflects temperature and ageing effects, with good agreement to DSR results at low temperatures. At higher temperatures, viscosity and damping introduce deviations, indicating that RAS is effective for modulus evaluation but not sufficient for complete master curve development. Full article

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation. Full article

Passive acoustic monitoring (PAM) using hydrophones enables collecting acoustic data to be collected in large and diverse quantities, necessitating the need for a reliable automated classification system. This paper presents a data-processing pipeline and a set of neural networks designed for a humpback-whale-detection system. A collection of audio segments is compiled using publicly available audio repositories and extensively curated via manual methods, undertaking thorough examination, editing and clipping to produce a dataset minimizing bias or categorization errors. An array of standard data-augmentation techniques are applied to the collected audio, diversifying and expanding the original dataset. Multiple neural networks are designed and trained using TensorFlow 2.20.0 and Keras 3.13.1 frameworks, resulting in a custom curated architecture layout based on research and iterative improvements. The pre-trained model MobileNetV2 is also included for further analysis. Model performance demonstrates a strong dependence on both feature representation and network architecture. Mel spectrogram inputs consistently outperformed MFCC (Mel-Frequency Cepstral Coefficients) features across all model types. The highest performance was achieved by the pretrained MobileNetV2 using mel spectrograms without augmentation, reaching a test accuracy of 99.01% with balanced precision and recall of 99% and a Matthews correlation coefficient of 0.98. The custom CNN with mel spectrograms also achieved strong performance, with 98.92% accuracy and a false negative rate of only 0.75%. In contrast, models trained with MFCC representations exhibited consistently lower robustness and higher false negative rates. These results highlight the comparative strengths of the evaluated feature representations and network architectures for humpback whale detection. Full article

Improving city crossroads safety is a critical problem for modern smart transportation systems (STS). This article presents the results of developing, upgrading, and comprehensively experimentally testing an acoustic monitoring system prototype designed for rapid accident detection. Unlike conventional camera- or lidar-based approaches, the proposed solution uses passive sound source localization to operate effectively with no direct visibility and in adverse weather conditions, addressing a key limitation of camera- or lidar-based systems. Generalized Cross-Correlation with Phase Transform (GCC-PHAT) algorithms were used to develop a hardware–software complex featuring four microphones, a multichannel audio interface, and a computation module. This study focuses on the gradual upgrading of the algorithm to reduce the mean localization error in real-life urban conditions. Laboratory and complex field tests were conducted on an open-air testing ground of a university campus. During these tests, the system demonstrated that it can accurately determine the coordinates of a sound source imitating accidents (sirens, collisions). The analysis confirmed that the system satisfies the V2X infrastructure integration response time requirement (<200 ms). The results suggest that the system can be used as part of smart transportation systems. Full article

Ceramic matrix composites (CMCs) exhibit excellent high-temperature strength, oxidation resistance, and fracture toughness, making them superior to traditional metals and single-phase ceramics in extreme environments such as aerospace, nuclear energy equipment, and high-temperature protection systems. The mechanical properties of CMCs directly influence the reliability and service life of structures; thus, accurately predicting their mechanical response and service behavior has become a core issue in current research. However, the multi-phase heterogeneity of CMCs leads to highly complex stress distribution and deformation behavior in traditional mechanical property testing, resulting in significant uncertainty in the measurement of key mechanical parameters such as strength and modulus. Additionally, the high manufacturing cost and limited experimental data further constrain material design and performance evaluation based on experimental data. Therefore, the development of effective numerical simulation and mechanical modeling methods is crucial. This paper provides an overview of the research hotspots and future directions in the field of CMCs numerical simulation and mechanical modeling through bibliometric analysis using the CiteSpace software. The analysis reveals that China, the United States, and France are the leading research contributors in this field, with 422, 157, and 71 publications and 6170, 3796, and 2268 citations, respectively. At the institutional level, Nanjing University of Aeronautics and Astronautics (166 publications; 1700 citations), Northwestern Polytechnical University (72; 1282), and the Centre National de la Recherche Scientifique (CNRS) (49; 1657) lead in publication volume and/or citation influence. Current research hotspots focus on finite element modeling, continuum damage mechanics, multiscale modeling, and simulations of high-temperature service behavior. In recent years, emerging research frontiers such as interface debonding mechanism modeling, acoustic emission monitoring and damage correlation, multiphysics coupling simulations, and machine learning-driven predictive modeling reflect the shift in CMCs research, from traditional experimental mechanics and analytical methods to intelligent and predictive modeling. Full article