Search Results (1,732)

Wood and wood-based materials represent low-carbon sustainable alternatives to petroleum sound absorbers, yet their baseline sound absorption coefficient varies drastically with wood species, anatomical cutting orientation and pore connectivity due to strong structural anisotropy. This review systematically integrates multiscale structural regulation, porous acoustic theories and data-driven optimization into a unified framework, revealing that broadband high sound absorption relies on the synergistic coordination of impedance matching, thermo-viscous dissipation and low-frequency resonant mechanisms, rather than simply maximizing porosity. We quantitatively compare state-of-the-art wood absorbers: directionally frozen wood aerogels achieve near-perfect absorption (α = 0.95–1.00, NRC = 0.82) across 520–6300 Hz, marking the current performance benchmark, while multifunctional superhydrophobic wood aerogels deliver moderate absorption (α ≈ 0.40) but stand out as all-biomass weather-resistant composites. Rigid-frame JCA/JCAL and poroelastic Biot models are clarified for wood’s distinct stiffness characteristics, and existing data-driven approaches are categorized, highlighting that most neural surrogates rely solely on FEM simulation without physical impedance-tube validation. Critical unresolved challenges including poor moisture/fire durability, insufficient industrial scalability and incomplete material databases are summarized, and targeted research priorities covering gradient manufacturing, hybrid physics–machine learning models and lifecycle environmental evaluation are proposed. Full article

Unmanned surface vehicle (USV) clusters operating in contested maritime environments face communication degradation from jamming, satellite denial, and partial node loss. Existing countermeasures either react after link failure or rely on a single signal quality indicator. A framework is presented that integrates two complementary mechanisms for resilient USV cluster communication: (1) a multi-indicator communication quality metric $Q_{\mathrm{comm}}$ that fuses signal-to-noise ratio, packet loss rate, latency, and temporal stability into a single scalar; and (2) a multi-modal backup communication chain spanning acoustic modem, optical link, and multi-hop RF(Radio Frequency) relay that provides physical-layer redundancy when primary radio frequency links are degraded. Across five representative failure scenarios simulated on a seven-vehicle cluster with 50 independent runs per configuration, the multi-indicator $Q_{\mathrm{comm}}$ metric achieves a mean of 0.521, outperforming single-indicator baselines on the composite $Q_{\mathrm{comm}}$ metric (Cohen’s d = 8.020, p < 0.0001, n = 250 per method); $Q_{\mathrm{comm}}$ is the framework’s own optimization target; this comparison is, therefore, presented as an internal consistency demonstration rather than an independent validation. The framework reduces recovery time from 113.8 s (single-indicator) to 15.3 s—an 86.6% improvement. With the backup communication chain enabled, delivery rates exceed 94% across all methods and scenarios. A direct Monte Carlo ablation (50 seeds × 5 scenarios) reveals that multi-indicator fusion is the primary driver of assessment accuracy and recovery speed, while the multi-modal backup chain is the dominant delivery driver: without it, delivery falls from 94.6% to 24.2%. A sigmoid-blended topology utility function is included as an architectural design component; its independent validation requires extended-duration threat experiments identified as future work. The contributions of this work are the validated multi-indicator fusion metric and the multi-modal backup chain architecture, which together provide a practical foundation for resilient USV communication. Full article

This study evaluates adaptive spatial matrix filtering (AMF) combined with MUSIC for underwater acoustic direction-of-arrival estimation under strong out-of-sector interference and extends the method to coherent sources by incorporating forward spatial smoothing (FSS) into the AMF design. Simulations compared AMF-MUSIC with conventional MUSIC and continuous matrix filter (CMF)-MUSIC. For a 20-sensor array with targets at −2° and 1°, an interferer at 50°, an SNR of −5 dB, and an INR of 20 dB, both AMF-MUSIC and CMF-MUSIC resolved the targets under a common −25 dB stopband bound, but AMF-MUSIC produced a smoother out-of-sector background. Tightening the CMF bound to −40 dB reduced background peaks but degraded target resolution. In coherent-source simulations using a 25-sensor array, AMF-FSS-MUSIC resolved the targets for all tested subarray lengths when the angular separation was at least 4.5°, achieving resolution probabilities of at least 95%. A 900–1100 Hz broadband simulation maintained an approximately −15 dB stopband response and a passband-response error below −12 dB. For the SWellEx-96 narrowband data, AMF-MUSIC reduced the DOA-estimation RMSE from 11.16° to 5.18° and increased the mean spatial-spectrum SIR from −0.41 dB to 13.18 dB, while the broadband results qualitatively demonstrated interference suppression. These results indicate a favorable configuration-dependent suppression–fidelity tradeoff, while robustness to other coherent-source conditions and broader measured-data validation require further investigation. Full article

As urban renewal shifts from expansion-led development toward quality improvement of existing urban areas, the restorative quality of urban village public spaces has become an important concern in high-density human settlements. Existing restorative environment research has focused mainly on parks, green spaces, waterfronts, and other settings with strong natural attributes, while audio–visual studies have often examined environmental perception, restorative appraisal, or physiological response as separate analytical components. Limited evidence therefore exists on how scene-level visual and acoustic characteristics, subjective sensory appraisal, perceived restoration, and short-term physiological responses are related within the same analytical framework in high-density urban village public spaces. This study investigated six selected public spaces in Shenzhen urban villages using a VR-based repeated-measures experiment involving 33 participants and 198 participant–scene observations, together with computer vision indicators, psychoacoustic analysis, subjective evaluation, EDA and HRV monitoring, linear mixed-effects models, and multilevel models of statistical indirect associations. Green view index (B = 0.322, p < 0.001) and color complexity (B = 0.422, p < 0.001) were positively associated with perceived restoration, whereas building enclosure (B = −0.271, p < 0.001) and sound roughness (B = −0.328, p < 0.001) were negatively associated with perceived restoration. Green view index showed a positive indirect association with perceived restoration through visual perception (ab = 0.386, 95% CI [0.304, 0.484]), whereas roughness showed a negative indirect association through soundscape perception (ab = −0.277, 95% CI [−0.385, −0.176]). In the full six-scene models, negative objective interaction estimates were compatible with acoustic constraints on favorable visual associations, but several interaction coefficients were sensitive to scene omission and should be regarded as exploratory; at the subjective level, soundscape perception and visual perception showed a positive interaction (B = 0.095, p = 0.028). Leave-one-scene-out analysis showed that several scene-level main-effect and objective interaction estimates were sensitive to the omission of S5 or S2, whereas the directions of the green-view-index and roughness indirect associations were retained. Perceived restoration was positively correlated with the reversed EDA recovery score (r = 0.416, p < 0.001), whereas HRV showed a negative association with LAeq (B = −0.150, p = 0.008), suggesting different short-term response patterns across subjective and physiological measures. The findings suggest that restorative responses in the examined urban village scenes were associated with both subjective sensory appraisal and audio–visual interactions. The study contributes by integrating scene-level visual and acoustic characteristics, subjective sensory appraisal, perceived restoration, and short-term physiological responses within a high-density urban village context. For renewal practice, the results support the coordinated consideration of adverse sound sources, visible greenness, and visual order; however, the observed relationships should be interpreted as context-specific associations rather than as universal causal mechanisms or validated design thresholds. Full article

Reliable defect detection in motor production lines is important for maintaining manufacturing quality. This study investigates the feasibility of a deep learning-based airborne acoustic screening approach for controlled no-load end-of-line induction motor inspection. Acoustic signals collected under controlled no-load test conditions were used to evaluate Feedforward Neural Networks (FNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and pre-trained models including ResNet50, MobileNetV2, and EfficientNetB0 for detecting rotor unbalance, assembly-induced bearing abnormalities, and their combination. Experimental results show that several models achieved up to 96% classification accuracy, depending on the selected architecture and feature representation. In cross-motor external validation using an unseen motor from the same manufacturer, the CNN with MFCC features achieved the best performance with 96% accuracy and 97% precision, recall, and F1-score, while ResNet50 with spectrogram inputs achieved 93% accuracy and 92% F1-score. These results demonstrate that airborne motor acoustic signals contain discriminative defect-related information under controlled no-load conditions and support the feasibility of low-cost, non-contact airborne acoustic sensing as a complementary screening approach for rapid end-of-line motor inspection. Full article

Architectural heritage interventions often preserve visual form while leaving acoustic memory and everyday sound practices outside the design brief. This article positions soundscape as an architectural material for heritage media architecture and tests it through Sanggar Kungfu Kapasan, a historic Chinese-diaspora kampung in Surabaya, Indonesia. The research combined contextual and soundmark mapping, Pepper’s Ghost prototyping, architectural translation of a semi-transparent virtual layer, and comparative evaluation using 2D visualization and immersive VR. After deduplication, 37 participants evaluated the 2D material, 31 evaluated VR, and 24 completed both modes for within-subject analysis. VR significantly improved perceived physical quality (Δ = +0.46, p = 0.0007, d_z = 0.80), communication quality (Δ = +0.42, p = 0.0218, d_z = 0.50), and overall evaluation (Δ = +0.20, p = 0.0285, d_z = 0.48). Sound-specific VR ratings were positive for place-meaning support (M = 5.87) and scene formation (M = 5.65), and the soundscape design index correlated with communication quality (r = 0.47, p = 0.007). The findings show that soundscape strengthens narrative legibility and architectural fit when deliberately coordinated with visual layering, scene sequencing, and cultural context. The study contributes a soundscape-informed, feng shui-readable workflow for pre-installation evaluation of multisensory heritage interventions. Because the respondent pool was predominantly composed of architecture students, the results are interpreted as a preliminary design-user evaluation rather than community validation. Full article

Underwater acoustic target recognition (UATR) is challenging due to the complex, multi-scale physical characteristics of marine targets and the strict computational limits of edge platforms like unmanned surface vehicles. To navigate the severe interference of underwater environments, existing methods increasingly rely on heavyweight architectures to achieve high recognition accuracy. However, the massive computational overhead of these models is fundamentally at odds with the restricted power and processing capabilities of practical deployment platforms. To resolve this conflict between performance and deployability, we propose LHK-Net, a lightweight Heterogeneous Kernel Network. By integrating a Heterogeneous Kernel Pyramid with Residual Depthwise Separable Convolutions, LHK-Net dynamically captures multi-scale acoustic features, from macroscopic steady-state harmonics to localized transient impulses, while compressing the model size to merely 0.82 M parameters. Additionally, a dual-domain Time–Frequency Attention module and an Adaptive SK-Fusion mechanism are incorporated for robust noise suppression. Experiments on the DeepShip dataset demonstrate that LHK-Net achieves state-of-the-art accuracy, outperforming heavyweight models at real-time speeds. Extensive visual analyses further validate that the network possesses strong physical interpretability, effectively aligning its internal feature representations with the intrinsic acoustic properties of the targets. Full article

Walnuts are commercially important tree nuts whose moisture content (MC) influences quality, shelf life, and post-harvest processing. This study evaluated the potential of low-cost acoustic sensing combined with machine learning for non-destructive MC prediction. Sixty in-shell walnuts were subjected to controlled drying at 40 °C for 26 h, with acoustic recordings and physical measurements collected every two hours. Acoustic signals were processed using Wavelet Soft Threshold Denoising (WSTD), Short-Time Fourier Transform (STFT), and Variational Mode Decomposition (VMD), and features were extracted from the resulting signals. Predictive models included generalized linear models (GLM), random forests (RF), gradient boosting machines (GBM), and Partial Least Squares (PLS) approaches. Following grouped walnut-level validation, the highest MC prediction performance was achieved by the model combining dimensional and acoustic descriptors (RF: R² = 0.836; GBM: R² = 0.826), while the model combining drying time and acoustic descriptors achieved moderate predictive performance (RF: R² = 0.762; GBM: R² = 0.760). Overall, the results provide proof-of-concept evidence that acoustic descriptors may complement physical measurements for non-destructive walnut moisture-content prediction. However, substantially larger independent datasets collected across multiple cultivars, production batches, acquisition conditions, and external validation studies will be required before practical industrial implementation can be considered. Full article

Outdoor comfort is influenced by thermal, visual, and acoustic stimuli, whose relative roles may vary across functional spaces. This study compared multisensory comfort characteristics across five frequently used public outdoor functional spaces in the main built-up area of Dalian, China, during the autumn transition season and examined their associations with overall comfort. Rotational fixed-point monitoring and synchronous questionnaire surveys were conducted in residential, cultural, educational, commercial, and green spaces, yielding 1497 valid responses. The results showed that: (1) Among the five investigated public outdoor spaces, the residential space had the highest proportion of positive overall comfort votes, whereas the educational space showed more thermal discomfort and negative acoustic evaluations. (2) Thermal and acoustic sensations changed linearly with corresponding exposures, while visual comfort first increased and then decreased with illuminance. (3) Thermal comfort had the largest main effect on overall comfort, visual comfort was also significant, and thermal–acoustic interaction was observed. (4) SEM indicated distinct association pathways. The thermal pathway showed a significant serial indirect effect through thermal sensation and thermal comfort, while the visual pathway contained indirect effects in opposite directions and the acoustic pathway was characterized mainly by a direct negative association between LAeq and overall comfort. These findings support function-sensitive multisensory assessment and targeted outdoor environmental optimization. Full article

This study presents a configurable speech signal preprocessing and feature-extraction workflow for identifying candidate acoustic biomarkers in acute heart failure. The workflow was evaluated in 12 patients hospitalized with acute heart failure. Short recordings of repeated vowels /a/, /i/, and /o/ were acquired shortly after admission and again before discharge following treatment and clinical stabilization. The pipeline included active-RMS normalization, automatic segmentation of repeated vowels, optional edge trimming, alternative pitch-estimation variants, and extraction of three feature families: phonatory and temporal measures, spectral-shape descriptors, and MFCC-based cepstral features. Within-patient admission-to-discharge differences were evaluated using two-sided Wilcoxon signed-rank tests, with nominal p-values interpreted as exploratory. Phonatory and temporal measures produced the most consistent exploratory findings. The pause-duration trend for /a/ decreased between admission and discharge and was the most configuration-stable individual candidate. CPP maximum and CPP range for /o/ increased consistently across the evaluated phonatory configurations, indicating systematic changes in cepstral prominence. Shimmer-related measures provided additional exploratory findings. MFCC measures showed complementary changes, particularly in MFCC11 variability for /o/, whereas spectral-shape effects were generally weaker and less consistent. Because the study involved a small, single-center cohort without a control group or external validation, the findings should be regarded as hypothesis-generating candidate acoustic measures rather than clinically validated biomarkers. Full article

The search and discovery of underwater shipwreck sites represent the most arduous and critical phases of underwater archaeology. Wooden shipwrecks, in particular, are characterized by low acoustic impedance contrast and weak magnetic anomalies, coupled with their limited physical dimensions. Consequently, they predominantly exist as shallow-buried, discontinuous small targets scattered within confined areas, making their detection exceptionally challenging. Furthermore, the complexity of the submarine environment—including rugged topography, turbid water columns, and strong currents—poses formidable obstacles to the effective detection of these archaeological remains. Single geophysical methods are often limited by insufficient imaging resolution, interpretation ambiguity, and geological noise, making precise localization and characterization difficult. Focusing on the Nan’ao I Ming Dynasty shipwreck located in waters approximately 24 m deep off the coast of Nan’ao, Guangdong Province, China, this study proposes and validates an “acoustic-magnetic” multi-source data integration detection method. This approach systematically integrates high-resolution multibeam echo sounding (MBES), side-scan sonar (SSS), sub-bottom profiling (SBP), and marine magnetic data to establish a comprehensive framework for identification and integration analysis. The results indicate that the MBES bathymetric data reveal a regular, elongated structure oriented north–south (approximately 34 m × 12 m), closely matching the main hull and deck configuration. The SSS imagery exhibited high backscatter intensity and parallel linear textures, effectively delineating the hard shipwreck structure and the associated rigid protective frame employed for in situ preservation. SBP data confirmed the semi-buried state of the shipwreck (burial depth of approximately 0.6 m). Spatial variations in sediment thickness around the site suggested ongoing modification by strong hydrodynamic processes. Marine magnetic surveys identified localized negative anomalies (−210 nT relative to the ambient magnetic field), contrasting sharply with the positive anomalies of the surrounding natural reefs, thereby indicating an artificial ferromagnetic source. The spatial registration and feature superposition of multi-source data facilitated the characterization of the shipwreck, demonstrating its potential to mitigate environmental interference and enhance detection reliability in this complex environment. Using the Nan’ao I shipwreck site as a case study, this study provides a detailed characterization of the site’s 3D morphology, burial state, and physical properties. The proposed methodology offers a practical and robust technical solution for underwater shipwreck archaeology in complex nearshore environments, providing significant implications for proactive discovery, efficient investigation, and protection of underwater cultural heritage (UCH). Full article

Water leakage in water supply pipeline networks leads to water resource loss, drinking water contamination, and ground subsidence. The existing acoustic leak detection techniques are constrained by low signal-to-noise ratios of leakage signals, difficulties in wave velocity estimation, and rapid signal attenuation, often resulting in localization errors of several meters. This study proposes a “wavenumber–frequency spectrum filtering and phase analysis” method for leak source localization, achieving precise positioning of underground water pipeline leaks through surface-mounted sensor array sampling. Based on the numerical simulation results, this research reveals the wavenumber–frequency spectrum characteristics of leakage noise on the ground surface, analyzes the impact of the sampling strategies on the wavenumber extraction, and ultimately constructs a localization model suitable for near-field array sampling. A mean localization error of 0.0374 m was achieved in a controlled experiment under single-layer sandy soil conditions, demonstrating the feasibility and accuracy of the proposed method under these tested conditions and providing a promising basis for precise leak localization in water supply networks that warrants further field validation. Full article

This study addresses the integration of sustainability-oriented analytics and decision support in CNC milling through a hybrid information system combining structured data management, sustainability KPIs, rule-based expert reasoning, and machine learning models. The proposed framework is evaluated using the publicly available NASA Ames Milling Tool Wear Dataset. Sustainability indicators related to operational energy demand, tool degradation, and vibration/acoustic-emission response are computed from machining parameters and sensor-derived features. Random Forest and Support Vector Machine models are used for tool wear classification. The expert system applies deterministic rules to identify operational risks, which are combined with machine learning predictions through a hierarchical decision-fusion strategy. Under case-wise cross-validation, the Random Forest achieved a mean classification accuracy of 73.8%, while the Support Vector Machine achieved 68.8%. The expert system most frequently identified elevated vibration-index and acoustic-emission conditions, while critical clean-production risks occurred rarely. Overall, the results demonstrate the feasibility of integrating expert knowledge, sustainability KPIs, and data-driven models into a hybrid information system for decision support in CNC milling environments. The proposed framework provides a foundation for future research on hybrid information systems supporting sustainable manufacturing and intelligent decision making. Full article

Architectural atmosphere for spirituality holds potential to support the physical and mental health of post-COVID urban populations, and people now spend more of their lives within buildings even as opportunities for spiritual engagement recede. Yet evidence-based knowledge for designing such environments remains limited. This study aimed to identify the architectural atmospheres for spiritual constructs through a systematic literature review and practice-based interviews conducted following the COVID-19 pandemic. Data from Scopus-indexed publications and architects’ relevant expertise were analyzed using thematic content analysis, while word frequency and bivariate association analyses were employed to identify key architectural characteristics and their associations with spiritual constructs. The findings indicate that a holistic atmospheric approach should integrate spatial, perceptual, and natural features. Tangible atmospheric features such as space, natural materials, and object elements emerged as most prominent, alongside intangible features including illumination, acoustics, and temperature conditions. At the use-effect level, spatial experience and perception were most frequently reported. Positive spiritual constructs were linked to symbolic objects, sensory experiences, and lighting conditions, whereas negative spiritual constructs were primarily associated with temperature conditions. Future studies should extend inquiry across diverse linguistic, cultural, and temporal contexts while empirically validating the proposed framework in real-world environments. Full article

Multimodal sentiment analysis aims to integrate heterogeneous textual, visual, and acoustic information for effective emotion understanding. However, existing methods often suffer from insufficient cross-modal interaction modeling, limited adaptability in multimodal fusion, and inadequate suppression of modality-specific noise under complex conversational scenarios. To address these challenges, this paper proposes a framework for learning adaptive cross-modal interactions for multimodal sentiment analysis. The proposed framework consists of three stages: modality-aware preprocessing, heterogeneous representation learning, and adaptive multimodal fusion. First, a unified preprocessing strategy is designed to improve cross-modal consistency through textual normalization, speaker-aware visual alignment, and utterance-level acoustic representation enhancement. Second, modality-specific encoders are constructed to capture complementary semantic, spatial, and utterance-level acoustic characteristics from textual, visual, and acoustic modalities, respectively. Third, an adaptive fusion framework is introduced to explicitly model cross-modal interactions, dynamically estimate the importance of different modality combinations, and further calibrate discriminative feature channels through channel attention. By jointly performing modality-level interaction learning and channel-wise feature refinement, the proposed framework effectively enhances multimodal representation capability for sentiment classification. Extensive experiments conducted on the CMU-MOSI and MELD benchmark datasets demonstrate that our framework consistently outperforms previous methods. In particular, the proposed model achieves 90.27% accuracy and 90.26% F1-score on CMU-MOSI, together with 66.57% accuracy and 66.21% F1-score on MELD. Additional ablation studies and qualitative analyses further validate the effectiveness of the proposed preprocessing strategy, modality-specific representation learning, and adaptive fusion mechanism. Full article