Search Results (271)

This study investigates the sound insulation performance of an anechoic chamber, exploring the influence patterns of different multilayer material combinations on wall sound insulation characteristics. Based on sound transmission theory, a predictive model for multilayer material wall sound insulation was established. The finite element method was employed to simulate the sound propagation characteristics of walls and glass doors with various material combinations. After validating the simulation results through a double-room method experiment, the material combination scheme for the anechoic chamber walls and glass doors was optimized. Based on this, a 1000 mm × 1000 mm × 2300 mm soundproof room prototype was designed and constructed. Its sound insulation performance under reverberant conditions was tested using the insertion loss method and compared with simulation data. Simultaneously, a hybrid calculation method combining low-frequency finite element analysis with high-frequency statistical energy analysis enabled precise and efficient prediction of the overall sound insulation performance of the soundproof room. Research revealed that single-pane glass with thicknesses between 5 and 20 mm conformed to the mass law, with sound insulation increasing by an average of 0.8 dB per additional millimeter. The 10 mm single-pane glass emerged as the optimal choice for the soundproof room’s glass door due to its ideal thickness and excellent low-to-mid-frequency sound insulation. The optimized wall structure featured compact thickness, outstanding low-frequency sound insulation, and balanced mid-to-high-frequency performance. Simulation and experimental results for the core frequency range of 63–1000 Hz showed high consistency, which validates the reliability of the theoretical model and simulation methodology within this frequency band. The deviation of simulation results from experimental data in the frequency range above 1000 Hz is mainly caused by acoustic leakage due to experimental sealing defects, and the high-frequency simulation results are only used for trend analysis rather than conclusion support. This study identifies the optimal multi-layer material combination for soundproof rooms, providing practical material strategies for acoustic design. It also reveals the sound insulation mechanisms of multi-layer composite structures. The findings offer significant reference for optimizing soundproofing materials and structures in architectural acoustics and transportation noise control. Full article

Underwater Acoustic Sensor Networks (UASNs) are pivotal for environmental monitoring, surveillance, and marine data collection. However, their open and largely unattended operational settings, constrained communication capabilities, limited energy resources, and susceptibility to insider attacks make it difficult to achieve safe, secure, and efficient collaborative learning. Federated learning (FL) offers a privacy-preserving method for decentralized model training but is inherently vulnerable to Byzantine threats and malicious participants. This paper proposes trust-aware FL for underwater sensor networks (TAFL-UWSN), a trust-aware FL framework designed to improve security, reliability, and energy efficiency in UASNs by incorporating trust evaluation directly into the FL process. The goal is to mitigate the impact of adversarial nodes while maintaining model performance in low-resource underwater environments. TAFL-UWSN integrates continuous trust scoring based on packet forwarding reliability, sensing consistency, and model deviation. Trust scores are used to weight or filter model updates both at the node level and the edge layer, where Autonomous Underwater Vehicles (AUVs) act as mobile aggregators. A trust-aware federated averaging algorithm is implemented, and extensive simulations are conducted in a custom Python-based environment, comparing TAFL-UWSN to standard FedAvg and Byzantine-resilient FL approaches under various attack conditions. TAFL-UWSN achieved a model accuracy exceeding 92% with up to 30% malicious nodes while maintaining a false positive rate below 5.5%. Communication overhead was reduced by 28%, and energy usage per node dropped by 33% compared to baseline methods. The TAFL-UWSN framework demonstrates that integrating trust into FL enables secure, efficient, and resilient underwater intelligence, validating its potential for broader application in distributed, resource-constrained environments. Full article

We investigate whether late-time modifications of gravity in the teleparallel framework can impact the current tension in the Hubble constant $H_0$, focusing on $f\left(T\right)$ cosmology as a minimal and well-controlled extension of General Relativity. We consider three representative $f\left(T\right)$ parametrisations that recover the teleparallel equivalent of General Relativity at early times and deviate from it only in late epochs. The models are confronted with unanchored Pantheon+ Type Ia supernovae, DESI DR2 baryon acoustic oscillations, compressed Planck cosmic microwave background distance priors, and redshift-space distortion data, allowing us to jointly probe the background expansion and the growth of cosmic structures. Two of the three models partially shift the inferred value of $H_0$ towards local measurements, while the third worsens the discrepancy. This behaviour is directly linked to the effective torsional dynamics, with phantom-like regimes favouring higher $H_0$ values and quintessence-like regimes producing the opposite effect. A global statistical comparison shows that the minimal $f\left(T\right)$ extensions considered here are not favoured over ΛCDM by the combined data. Nevertheless, our results demonstrate that late-time torsional modifications can non-trivially redistribute current cosmological tensions among the background and growth sectors. Full article

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article

Multi-modal sentiment analysis (MSA) aims to accurately identify users’ emotional states by integrating textual, acoustic, and visual modalities. However, existing methods often suffer from insufficient cross-modal interaction, rigid fusion strategies, and limited sensitivity to subtle sentiment-level differences, which severely restrict model generalization and robustness. To address these issues, this paper proposes CrossSent, a multi-modal sentiment analysis framework that combines cross-modal attention with pairwise ranking regularization. Specifically, a Gated Multi-modal Residual Adapter (GMRA) is introduced to dynamically integrate heterogeneous features through gated residual connections, effectively mitigating modality asynchrony and noise interference. Meanwhile, a Monotonic Pairwise Ranking (MPR) regularization enhances discrimination among fine-grained sentiment levels. Furthermore, an Error-Interval Ordinal Inconsistency (EIOI) loss is designed to tolerate small prediction deviations, improving both stability and robustness. Experimental results on CMU-MOSI, CMU-MOSEI, and CH-SIMS demonstrate that CrossSent consistently surpasses state-of-the-art baselines across key metrics. For instance, it achieves 89.78% binary accuracy and 52.1% seven-class accuracy on CMU-MOSI, 87.72% and 54.7% on CMU-MOSEI, and 80.41%, 62.36%, and 43.54% for three- and five-level CH-SIMS tasks, with reduced mean absolute errors of 0.563, 0.513, and 0.408, respectively. We further report ordinal-consistency measures (QWK and level-jump statistics) to complement conventional metrics and quantify level-wise agreement. These results validate the effectiveness and generalization capability of the proposed framework. Full article

We investigate the cosmological implications of torsion–boundary gravity with explicit matter coupling in f(T,B,𝓣) gravity. The purpose is to examine if such couplings offer observationally viable extensions to standard cosmology. Focusing on linear and power-law model realizations, we derive the modified Friedmann equations and analyze the resulting background dynamics. Using a combination of late-time datasets—including Cosmic Chronometers, Type Ia Supernovae, and Baryon Acoustic Oscillations—we perform a joint likelihood analysis to constrain the model parameters. Our results show that both f(T,B,𝓣) models remain compatible with current observations and effectively reduce to the ΛCDM paradigm in their appropriate parameter limits. While the power-law model exhibits mild dynamical deviations at intermediate redshifts, it remains statistically indistinguishable from the standard cosmological model. We conclude that f(T,B,𝓣) gravity represents a viable and robust extension of torsional modified gravity, motivating further study of non-minimal matter–geometry couplings in cosmology. Full article

Formaldehyde (H₂CO) is a hazardous volatile organic compound widely present in indoor and industrial environments, and its real-time, highly sensitive detection is essential for environmental safety. However, existing detection techniques often face challenges in simultaneously achieving high sensitivity and long-term stability, and many conventional photoacoustic spectroscopy (PAS) systems rely strongly on low gas flow rates to suppress flow-induced noise, which limits their applicability for continuous online monitoring. In this work, an ultraviolet photoacoustic spectroscopy (UV-PAS)-based H₂CO detection system operating in a nitrogen (N₂) background is developed. The system integrates a compact differential photoacoustic cell (PAC) with a 320 nm ultraviolet laser source, in which the resonator length and buffer configuration are carefully optimized to enhance acoustic resonance and effectively suppress flow-related disturbances. Notably, a key innovation of this study is that the system maintains a stable photoacoustic response even under relatively high gas flow conditions. Experimental results demonstrate that at a flow rate of 250 sccm, the photoacoustic signal amplitude remains stable, and the noise level is well controlled, significantly reducing the dependence of conventional PAS systems on low-flow operation. The photoacoustic cell exhibits a resonant frequency of 1767 Hz and a quality factor of 46. Calibration using a 47.31 ppm H₂CO:N₂ gas mixture shows a good linear response with a correlation coefficient of R² = 0.98844. The minimum detection limit reaches 2.50 ppm at a 1 s integration time and is further improved to 88.1 ppb at an integration time of 2202 s based on Allan–Werle deviation analysis. These results demonstrate that the proposed UV-PAS system provides a sensitive, stable, and cost-effective solution for real-time trace H₂CO detection while retaining robust performance at elevated gas flow rates, highlighting its strong potential for practical applications. Full article

With the rapid development and widespread application of multibeam echo-sounding systems, large-scale and high-resolution seafloor topography can be efficiently acquired, enabling precise mapping of seabed terrain. However, due to complex oceanographic conditions, instrumental noise, and acoustic interferences, the acquired multibeam data often contain outliers that deviate from the true seafloor surface. These outliers can distort the representation of seafloor topography, adversely affecting subsequent geological analysis and engineering applications. To address this issue, a hybrid outlier detection method combining CUBE filtering with the Isolation Forest (IForest) algorithm, termed CUBE-IForest, is proposed. The method first employs CUBE filtering to remove gross outliers based on local uncertainty estimation, followed by the application of IForest to identify subtle anomalies in the refined data, achieving hierarchical detection of outliers. Experimental results based on in situ multibeam bathymetric data from the northeastern Pacific demonstrate that compared with traditional filtering methods the CUBE-IForest approach significantly improves detection accuracy and reduces both false positive and false negative rates by approximately 30%, confirming its efficiency and reliability in seafloor mapping and analysis. Full article

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

The Jurassic reservoirs in the Fukang Sag of the Junggar Basin exhibit heterogeneous overpressure. As the mechanisms underlying overpressure generation remain poorly constrained, this poses challenges for accurate pre-drilling-pressure prediction and hinders a comprehensive understanding of hydrocarbon accumulation processes. Through integrated analysis of measured pressure, mud weight, and well-logging curves, this study delineates distinct overpressure characteristics in sandstones and identifies the well-logging response to overpressure in mudstones. By coupling the loading-unloading response with the analysis of geological conditions conducive to overpressure, we differentiate the overpressure-generating mechanisms between sandstones and mudstones and assess their implications for deep petroleum exploration. The study reveals significant vertical heterogeneity in pressure regimes, with sandstones exhibiting pressure coefficients ranging from 1.2 to 1.8, locally exceeding 2.1. Strong overpressure preferentially develops in isolated sand bodies linked to deep source kitchens via oil-source faults. The logging response of overpressured mudstones shows high acoustic transit time, high neutron, and low resistivity, deviating from the normal compaction trend, yet demonstrates progressive density increases attributable to chemical compaction processes. Overpressure points with pressure coefficients between 1.2 and 1.4 align with the loading curve dominated by disequilibrium compaction. The overpressure with a pressure coefficient exceeding 1.4 correlates with abrupt unloading responses indicative of fault-transferred overpressure in sandstones. Our results highlight that overpressured fluid migration via faults is a critical process in hydrocarbon migration, with large-magnitude overpressured reservoirs being readily formed near oil-source faults. Multi-overpressure mechanisms create a complex pore-pressure distribution in deep layers, challenging conventional pressure-prediction models. These insights advance predictive models for pore pressure and provide a robust framework for optimizing exploration strategies in the Fukang Sag. 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

This study presents the development and validation of a high-speed optical data acquisition system for detecting and characterizing hydrodynamic cavitation downstream of a triangular nozzle. The system integrates a PIN photodiode, a transimpedance amplifier, and a high-sampling-rate microcontroller. Its performance was first evaluated using controlled sinusoidal signals, and statistical stability was assessed as a function of the number of acquired samples. Experiments were subsequently conducted in a converging–diverging conduit under biphasic flow conditions, where mean irradiance, standard deviation, and frequency spectra were analyzed downstream of the nozzle. The optical signal distributions revealed transitions in flow behavior associated with cavitation development, which were quantified through statistical metrics and spectral features. The Strouhal number was estimated from dominant frequencies extracted from the spectra, exhibiting a non-monotonic dependence on the Reynolds number, consistent with changes in flow structure and turbulence intensity. Spectral analysis further indicated frequency bands associated with energy transfer across turbulent scales and bubble dynamics. Overall, the results demonstrate that the proposed optical system constitutes a viable and non-intrusive methodology for detecting and characterizing cavitation intensity in a way that complements other optical and acoustic methods. Full article

Additive manufacturing (AM) has emerged as a pivotal technology in component fabrication, renowned for its capabilities in freeform fabrication, material efficiency, and integrated design-to-manufacturing processes. As a critical branch of AM, metal additive manufacturing (MAM) has garnered significant attention for producing metal parts. However, process anomalies during MAM can pose safety risks, while internal defects in as-built parts detrimentally affect their service performance. These concerns underscore the necessity for robust in-process monitoring of both the MAM process and the quality of the resulting components. This review first delineates common MAM techniques and popular in-process monitoring methods. It then elaborates on the fundamental principles of acoustic emission (AE), including the configuration of AE systems and methods for extracting characteristic AE parameters. The core of the review synthesizes applications of AE technology in MAM, categorizing them into three key aspects: (1) hardware setup, which involves a comparative analysis of sensor selection, mounting strategies, and noise suppression techniques; (2) parametric characterization, which establishes correlations between AE features and process dynamics (e.g., process parameter deviations, spattering, melting/pool stability) as well as defect formation (e.g., porosity and cracking); and (3) intelligent monitoring, which focuses on the development of classification models and the integration of feedback control systems. By providing a systematic overview, this review aims to highlight the potential of AE as a powerful tool for real-time quality assurance in MAM. Full article

Pronunciation assessment is central to computer-assisted pronunciation training (CAPT) and speaking tests, yet most systems still adopt a native norm, treating deviations from canonical L1 pronunciations as errors. In contrast, rating rubrics and psycholinguistic evidence emphasize intelligibility for a target listener population and show that listeners rapidly adapt their phonetic categories to new accents. We argue that automatic assessment should likewise be referenced to the target learner group. We build a Transformer-based mispronunciation detection (MD) model that computationally mimics listener adaptation: it is first pre-trained on multi-speaker Librispeech, then fine-tuned on the non-native L2-ARCTIC corpus that represents a specific learner population. Fine-tuning, using either synthetic or human MD labels, constrains updates to the phonetic space (i.e., the representation space used to encode phone-level distinctions, the learned phone/phonetic embedding space, and its alignment with acoustic representations), which means that only the phonetic module is updated while the rest of the model stays fixed. Relative to the pre-trained model, L2 adaptation substantially improves MD recall and F1, increasing ROC–AUC from 0.72 to 0.85. The results support a target-population norm and inform the design of perception-aligned, fairer automatic pronunciation assessment systems. Full article

The ecological status of lakes based on ichthyofauna, as defined by the Water Framework Directive, is assessed using intercalibrated methods. However, the methods adopted (in Poland, the Lake Fish Index LFI-EN method, based on results of one-off fishing with multi-mesh gillnets) are labor-intensive and do not allow for frequent repeat testing. Therefore, the concept of a simple model describing changes in the relative number of single traces in the vertical profile (according to the TS target strength distribution) in a lake is presented, as well as an index (the sum of deviations from such a model), enabling quantification of the similarity of TS distributions in lakes with this model. Preliminary analyses were conducted on acoustic data collected in Lake Dejguny. This lake—the condition of which could be estimated based on historical data using the relationships between LFI and the degree of lake eutrophication (expressed by Carlson’s TSI)—was assessed as having a good status in 2006, whereas in 2021, (based on LFI-EN) it had a moderate status. The study tested the TS distribution model, calculated as the arithmetic mean of the relative number of single traces in 2 m-thick layers. It was also shown that the proposed indicator can effectively signal deterioration of ecological status—the sum of the absolute values of the TS distribution deviations in 2021 (moderate status) from the model was more than seven times greater than the sum of the deviations of the distributions from which the model was built (good status). The obtained results confirmed the hypothesis about the possibility of determining a characteristic distribution of single traces in the vertical profile when the lake was classified as being in good condition. Full article