Search Results (258)

This paper presents a theoretical approach based on the FSDT to study the acoustic vibration performance of rib-stiffened PVC foam sandwich structures with reinforcing and weakening phases when submerged in water. The complex core layer with reinforcing and weakening phases is homogenized to an equivalent orthotropic layer. Building upon this framework, the governing equations of motion for rib-stiffened PVC foam sandwich structures under the boundary conditions of a simply supported type are derived, incorporating the coupling interaction between the reinforcing ribs and the sandwich plates. Considering the influence of the underwater environment, with the Helmholtz equation governing the continuity of the acoustic pressure field and the Euler equation regulating the fluid–structure interaction interface continuity, the Navier method is subsequently employed to solve for the natural frequencies and acoustic vibration responses. For the purpose of verifying the proposed approach, the predicted results are contrasted with both the literature-derived data and numerical simulation results. Finally, parametric research is further conducted to explore the effect of the parameters of the rib and core layers on the underwater acoustic vibration characteristics. The conclusions drawn from this study can provide meaningful guidance for engineering design and optimization of such rib-stiffened sandwich structures, incorporating both reinforcing and weakening phases in underwater engineering applications. Full article

In this investigation, the nonlinear dust-acoustic waves in the lunar terminator region are studied in a three-component complex plasma comprising Boltzmann-distributed electrons and ions and inertial, cold, negatively charged dust grains. The fluid model is reduced, via the reductive perturbation technique, to a planar Korteweg–de Vries (KdV) equation that governs the evolution of small-amplitude dust-acoustic structures in this environment. Hirota’s direct method is then employed to derive exact multiple-soliton solutions, which allow us to examine the parameter dependence of dust-acoustic solitons and to characterize their overtaking collisions. The analysis shows that the soliton polarity and amplitude are controlled by the equilibrium electron–ion density ratio and the electron-to-ion temperature ratio, and that multi-soliton interactions remain elastic, with only finite phase shifts after collision. In the second part of the study, the planar integer KdV model is generalized to a time-fractional KdV (FKdV) equation to incorporate nonlocal temporal memory effects in the dust-acoustic dynamics. This FKdV equation is analyzed using two analytical approximation schemes: the Tantawy technique, recently proposed as a direct and rapidly convergent approach to fractional evolution equations, and the new iterative method, a widely used high-accuracy scheme in the fractional literature. For both methods, higher-order approximations are constructed, and their absolute and global maximum residual errors are quantified. The results demonstrate that the Tantawy technique provides compact approximations with superior accuracy and stability compared with the new iterative method for the present FKdV-soliton problem. The combined integer- and fractional-analytic framework provides a physically transparent framework for understanding how nonlinearity, dispersion, and fractional memory jointly shape dust-acoustic solitary structures in the electrostatically complex lunar terminator plasma, which is of paramount interest for future lunar missions like Luna-25 and Luna-27. Full article

The rapid electrification of road vehicles has fundamentally reshaped the priorities of noise, vibration, and harshness (NVH) engineering. In the absence of combustion-related broadband masking, tonal and order-related phenomena originating from the electric machine, inverter switching, and high-speed reduction gearing have become clearly perceptible and, in many cases, acoustically dominant. Consequently, drivetrain noise in electric vehicles can no longer be assessed at component level alone; it must be understood as a coupled system response shaped by excitation mechanisms, structural dynamics, transfer paths, radiation efficiency, and ultimately human perception. This review adopts a source-to-perception perspective and consolidates the principal physical mechanisms governing vibro-acoustic behavior in integrated electric drive units. Electromagnetic force harmonics and torque ripple are discussed alongside transmission-error-driven gear mesh excitation, while bearing and shaft nonlinearities are examined in the context of high-speed operation. In addition, ancillary thermoacoustic and aerodynamic contributions are considered, reflecting the increasingly integrated packaging of modern e-axle architectures. On this mechanism-oriented basis, dominant excitation types are linked to frequency-appropriate modeling strategies, spanning electromagnetic force extraction, multibody drivetrain simulation, structural finite element analysis, transfer path analysis, and acoustic radiation prediction. Particular attention is given to workflow integration across domains. Finally, the paper identifies research challenges that predominantly arise at system level, including multi-source interaction effects, installation-dependent transfer-path variability, emergent resonances in assembled structures, manufacturing-induced tonal artifacts, and the still limited correlation between predicted vibration fields and perceived sound quality. Full article

In the field of study of fluid–structure interaction (FSI), sloshing dynamics play a crucial role in various engineering applications, from aerospace to civil infrastructure. Finite Volume (FV)-based Computational Fluid Dynamics (CFD) methods for modeling free surface flows like sloshing are computationally expensive, particularly because high-resolution dynamic transient simulations are required. Moreover, FSI effects are usually considered by coupling different solvers for the fluid and the structural domain, respectively, thus adding to the computational burden due to the various steps of data transfer, interpolation, and mesh adaptation needed to obtain accurate results. On the other hand, reduced-order models of sloshing effects are usually obtained by tuning equivalent mechanical models, which often neglect more complex geometries and imperfections. To address this challenge, the use of acoustic finite elements, as an alternative approach for modeling free surface flows interacting with flexible structures, has been proposed previously. Such elements are defined with the sole dynamic pressure as the nodal degree of freedom; therefore, such methods can significantly accelerate simulations to predict sloshing-induced forces and pressure distribution, taking into account the actual geometry of the structure. Due to the reduced computational time, FSI analysis with acoustic elements can serve as a viable tool for control systems and design optimization. Potential applications of this approach include structural analysis of anti-sloshing devices in rocket propellant tanks, control systems for enhanced launch stability, and seismic safety assessment of liquid storage tanks, as well as slosh-induced wall load evaluation in the fuel and water reservoir, transportation, and energy systems. Validation of FSI effects is conducted against results from partitioned two-way coupled fluid–structural simulations. The simplified frequency-prediction model was reliable for practical flexibility ranges. Overall, this work deepens our understanding of how baffle characteristics influence slosh mitigation, offering valuable guidance for anti-sloshing device engineering. Full article

Background/Objectives: Multimodal depression detection research has traditionally relied on early or hybrid fusion strategies without systematically analyzing how dynamic fusion mechanisms interact with modality-specific pretraining. Although gated and attention-based architectures are increasingly adopted, their behavior is rarely examined within a structured fusion taxonomy framework. Methods: In this study, we conduct a controlled taxonomy-level evaluation of multimodal fusion strategies in a Japanese PHQ-9-annotated depression dataset. We compare four fusion paradigms (concatenation, summation, gated fusion, and cross-attention) across three integration stages, crossed with modality-specific affective pretraining configurations for visual (CMU-MOSI/MOSEI), acoustic (JTES), and textual (WRIME) encoders, yielding 512 experimental conditions. Results: The results reveal strong position-dependent effects of fusion strategy. Cross-attention fusion at the audio integration stage achieved the highest mean AUC (0.774) and PR-AUC (0.606), with statistically significant superiority over gated and concatenation-based fusion (Kruskal–Wallis $H=86.28$, $p<0.001$). In contrast, fusion effects at the text stage were non-significant in AUC but significant in PR-AUC, highlighting metric-sensitive behavior under class imbalance. Pretraining effects were modality-specific: SigLIP initialization produced significant positive transfer $(\mathrm{\Delta }=+0.018, p<0.001)$, whereas audio pretraining on JTES resulted in negative transfer $(\mathrm{\Delta }=-0.014, p=0.004)$, suggesting domain mismatch effects. Gate analysis further revealed condition-dependent modality dominance, including cases of semantic–geometric reversal under joint auxiliary augmentation. Conclusions: Our findings suggest that multimodal depression detection systems should not be interpreted through static fusion categories alone. Instead, modality contribution appears to be associated with structured interaction effects between fusion strategy, integration position, and affective pretraining. This work provides a controlled empirical bridge between fusion taxonomy and dynamic modality weighting in clinical multimodal modeling. Full article

Urban wetlands are acoustic hotspots where vegetation structure, hydrological dynamics, and anthropogenic noise interact, yet multi-season assessments of how vegetation influences avian soundscapes are limited. This study explored bird soundscape dynamics across forest, open forest grassland, and meadow habitats in Nanjing Xinjizhou National Wetland Park, eastern China, using passive acoustic monitoring during spring and autumn 2023. Twelve sampling points (four per vegetation type) were established, and six acoustic indices were calculated, including the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bioacoustic Index (BIO), Normalized Difference Soundscape Index (NDSI), and Acoustic Entropy Index (H). were calculated from 48-h recordings each season. Random forest models and redundancy analysis assessed the relationships between acoustic indices, fine-scale vegetation parameters (e.g., crown width, tree height, species richness), and anthropogenic factors (e.g., distance to roads/trails, surface hardness). Vegetation structure, particularly crown width, was the primary driver of avian acoustic diversity, with broad-crowned forests consistently exhibiting the highest acoustic complexity. In spring, anthropogenic factors such as trail and road proximity dominated soundscape variation, suppressing biological sounds. In autumn, with reduced human presence, vegetation structure emerged as the dominant factor, while bioacoustic activity remained elevated despite reduced peaks in acoustic complexity. Proximity to roads increased low-frequency (1–2 kHz) noise and suppressed mid-frequency (4–8 kHz) bird vocalizations, but trees with crown widths ≥4 m maintained higher acoustic diversity even near disturbance sources. This study demonstrates that vegetation structure mediates both resource availability and sound propagation, buffering the effects of anthropogenic disturbance in frequency-specific ways. Multi-season sampling is crucial for understanding the dynamic interplay between vegetation phenology and human activity that shapes urban wetland soundscapes. Full article

The integration of Artificial Intelligence (AI) into the design, manufacturing, and lifecycle management of Composite Overwrapped Pressure Vessels (COPVs) is transforming the pathway toward autonomous and adaptive composite systems. This paper presents a comprehensive review and roadmap for AI-enabled COPVs development, bridging materials design, process optimisation, and predictive maintenance. The study synthesises over a decade of research on data-driven composite manufacturing, combining technology intelligence, PESTEL-SWOT environmental assessment, and cross-sectoral analysis of industrial and academic advances. A unified workflow is proposed to illustrate AI integration across the COPVs lifecycle, highlighting data feedback loops for continuous optimisation through digital twins and intelligent process control. Structural Health Monitoring (SHM) plays a central role in this ecosystem by providing real-time high-fidelity data on damage evolution and environmental interactions in COPVs. Through embedded sensing technologies such as fibre optic sensors and acoustic emission systems, SHM enhances digital twin fidelity, supports AI-based anomaly detection, and strengthens model validation in safety-critical hydrogen storage applications. Critical challenges are identified, including limited hydrogen-exposure datasets, lack of real-time adaptability, explainability in safety-critical design, and sustainability of AI-intensive workflows. These challenges highlight the need for tighter SHM-AI integration to enable reliable condition assessment and prognostics under multi-physics loading conditions. Based on these findings, the paper outlines actionable research directions to enable reliable, transparent, and sustainable AI adoption in composite manufacturing under the Industry 4.0 and hydrogen-economy paradigms. Full article

This paper proposes a novel method for measuring music similarity. Existing music similarity measurements have often been used for music appreciation, but this paper proposes a method for measuring the similarity between music samples which are used for music production. Conventional music recommendation approaches often rely on either metadata-based similarity or audio-based feature similarity in isolation, which limits their effectiveness in sample-based recommendation scenarios where both compositional context and acoustic characteristics are important. To address this limitation, the proposed framework combines a hypergraph-based information similarity module with a feature-based similarity module learned using Siamese networks and triplet loss. In the information-based module, metadata attributes such as beats per minute (BPM), genre, chord, key, and instrument are modeled as vertices in a hypergraph, and Random Walk–Word2Vec embeddings are learned to capture structural relationships between music samples and their attributes. In parallel, the feature-based module employs vertex-specific Siamese networks trained on instrument and key classification tasks to learn perceptual similarity directly from audio signals. The two modules are trained independently and jointly utilized at the recommendation stage to provide attribute-specific similarity results for a given query sample. Results show that the proposed system achieves high Precision@k across multiple attributes and forms stable similarity structures in the embedding space, even without relying on user interaction data. These results reflect embedding consistency evaluated over the entire dataset where training and retrieval are performed on the same sample pool, rather than generalization to unseen samples. These results demonstrate that the proposed hybrid framework effectively captures both structural and perceptual similarity among music samples and is well suited for sample-based music recommendation in music production environments. Full article

Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aircraft skins due to their advantages of high strength and lightweight properties. However, their laminate structure and energy-absorbing characteristics result in low-energy impact damage, such as delamination, that is often invisible but can lead to catastrophic failure. Consequently, early detection of delamination in CFRP laminates is necessary. Nonlinear ultrasonic guided waves exhibit high sensitivity to delamination, and second harmonics are widely employed. Compared to second harmonics, one-way mixing of ultrasonic guided waves can excite and receive signals simultaneously at the same location, thereby precisely localizing delamination. This capability has the potential for inspecting buried structures. However, existing literature has not yet fully addressed the generation mechanism of one-way mixing in CFRP laminates nor its interaction with delamination. Based on finite element simulation, this study investigates one-way mixing of A₀ modes and S₀ modes in CFRP laminates. Utilizing pulse-inversion techniques and two-dimensional fast Fourier transforms, the modes and propagation directions of difference-frequency components and sum-frequency components are determined. Furthermore, by utilizing the normalized acoustic nonlinearity parameter χ’ and adjusting the position of the mixing zone through different time delays, delamination in the CFRP laminate is successfully localized. Full article

An inhomogeneous thin internal stratum sometimes exists between two dissimilar materials, which is usually caused by non-uniform thermal distribution, interaction of different media, diffusion impurity or material degeneration and damage. In this paper, it is considered as a functional graded (FG) piezoelectric material in surface acoustic wave devices, and we investigate its effect on Love wave propagation within the framework of the linear piezoelectric theory. Correspondingly, the power series technique is presented and applied to solve the dynamic governing equations, i.e., two-dimensional partial differential equations with variable coefficients, with the convergence and correctness being proved. In this method, the material coefficients can change in random functions along the thickness direction, which reveals the generality of this method to some extent. As the numerical case, the elastic coefficient, piezoelectric coefficient, dielectric permittivity, and mass density change in the linear form but with different graded parameters, and the influence of material inhomogeneity on the Love wave propagation is systematically investigated, including the phase velocity, electromechanical coupling factor, and displacement distribution. In addition, the FG piezoelectric material caused by piezoelectric damage and material bonding is discussed. Numerical results demonstrated that both piezoelectric damaged and material bonding can make the higher modes appear earlier for the electrically open case, decrease the initial phase velocity, and limit the existing region of the fundamental Love mode for the electrically shorted case. The qualitative conclusions and quantitative results can provide a theoretical guide for the structural design of surface wave devices and sensors. Full article

Emotions and affective responses are core intervention targets in music therapy. Through acoustic elements, music can evoke emotional responses at physiological and neurological levels, influencing cognition and behavior while providing an important dimension for evaluating therapeutic efficacy. However, emotions are inherently abstract and difficult to represent directly. Artificial intelligence models therefore provide a promising tool for modeling and quantifying such abstract affective states from physiological signals. In this paper, we propose a structured and explicitly factorized multi-modal representation learning framework for joint affective state and preference inference. Instead of entangling heterogeneous dynamics within monolithic encoders, the framework decomposes representation learning into cross-channel interaction modeling and intra-channel temporal–spectral organization modeling. The framework integrates electroencephalography (EEG), peripheral physiological signals (GSR, BVP, EMG, respiration, and temperature), and eye-movement data (EOG) within a unified temporal modeling paradigm. At its core, a Dynamic Token Feature Extractor (DTFE) transforms raw time series into compact token representations and explicitly factorizes representation learning into (i) explicit channel-wise cross-series interaction modeling and (ii) temporal–spectral refinement via learnable frequency-domain gating. These complementary structural modules are implemented through Cross-Series Intersection (CSI) and Intra-Series Intersection (ISI), which perform low-rank channel dependency learning and adaptive spectral modulation, respectively. A hierarchical cross-modal fusion strategy integrates modality-level tokens in a representation-consistent and interaction-aware manner, enabling coordinated modeling of neural, autonomic, and attentional responses. The entire framework is optimized under a unified multi-task objective for valence, arousal, and liking prediction. Experiments on the DEAP dataset demonstrate consistent improvements over state-of-the-art methods. The model achieves 98.32% and 98.45% accuracy for valence and arousal prediction, 97.96% for quadrant classification in single-task evaluation, and 92.8%, 91.8%, and 93.6% accuracy for valence, arousal, and liking in joint multi-task settings. Overall, this work establishes a structure-aware and factorized multi-modal representation learning framework for robust affective decoding and intelligent music therapy systems. Full article

This study examines the perceived impact of sensory and interactive architectural design in inclusive learning environments on the sensory–emotional responses and behavioral–academic outcomes of children with neurodevelopmental disorders—namely Autism Spectrum Disorder, Down Syndrome, and Attention-Deficit/Hyperactivity Disorder—during early childhood within the Egyptian educational context. Adopting a perception-based, non-causal analytical perspective, a descriptive–analytical, survey-based design was implemented using a validated questionnaire developed from an architectural–educational conceptual framework grounded in relevant literature. The study involved (N = 202) parents, teachers, therapists, and caregivers who evaluated the perceived influence of environmental design elements on children’s sensory responses, behavior, social interaction, and academic performance, based on observational and experiential assessments rather than objective environmental performance measurements. The results indicated high perceived impacts on sensory–emotional responses (84.8%) and behavioral–academic outcomes (82.0%). Movement–spatial attributes showed the strongest influence, followed by balanced natural lighting, calming colors, natural materials, and low-noise acoustic conditions, while natural elements and sensory gardens played a regulatory role in supporting emotional stability and social interaction. The study concludes that sensory- and emotionally responsive architectural design, when understood as a supportive component of the educational experience rather than an independent causal factor, and integrated with appropriate pedagogical practices, contributes to inclusive learning environments accommodating neurodevelopmental diversity, while informing the development of an applied, evidence-informed architectural design framework that translates perceptual–correlational findings into structured and operational design guidelines adaptable to the Egyptian educational context. Full article

The interaction between humans and birds plays an important role in shaping the sustainability of tropical rainforest ecosystems, particularly through bird vocalizations that function as bioacoustic indicators of ecological conditions while simultaneously embedding socio-cultural meanings within local communities. This study aims to (1) classify types and categories of bird sounds as perceived by rural communities, and (2) assess the role of bird vocalizations as cultural symbols supporting community-based conservation practices. The study was conducted across six islands and eight villages in North Maluku, Eastern Indonesia, using a qualitative approach based on semi-structured interviews and community workshops. A total of 435 respondents, all of whom were farmers residing along forest margins, participated in the study. The results documented 51 bird species belonging to 26 families, whose vocalizations were interpreted and classified by local communities into three acoustic categories: 21 species with loud calls (41.18%), 12 species with melodious calls (23.53%), and 18 species with sad calls (35.29%). Melodious vocalizations were commonly associated with values of beauty, calmness, and social harmony, whereas loud calls were predominantly interpreted as warnings, signals of alertness, or indicators of environmental change. These findings demonstrate that bird sounds serve not merely as ecological cues, but as culturally embedded symbols that guide daily activities, moral values, and conservation ethics within rural communities. By documenting the cultural significance of bird vocalizations across a clearly defined geographic context, this study provides an empirical basis for culturally informed conservation strategies aimed at protecting bird species subject to high levels of cultural use and ecological pressure. Full article

Vibrations have long been a critical subject of investigation across engineering disciplines. With the expansion of major manufacturing sectors such as shipbuilding, automotive engineering, aerospace, and railway transport, the challenges associated with noise, environmental impact, and geotechnical stability have become increasingly complex. Mechanical systems inherently dissipate energy through vibration, and this dissipation can significantly influence structural performance, durability, and operational efficiency. Since the early foundational studies on vibration control in the 1980s, substantial progress has been made in developing innovative mitigation techniques. Among these, the acoustic black hole (ABH) concept has emerged as a promising passive method for reducing vibrational energy without adding significant mass. Over the years, researchers have further enhanced ABH structures by incorporating damping layers, which improve their ability to dissipate energy and control structural vibrations. More recently, scientific interest has shifted toward understanding the role of embedded or dispersed particles in vibration attenuation. Particle-based approaches have shown potential for improving energy dissipation mechanisms through micro-scale interactions, yet the underlying physical processes and their influence on vibration behavior remain active topics of research. In this study, we examine the influence of particles on vibration reduction through combined experimental and numerical investigations. The system is subjected to repeated excitation forces of 1 V, 2 V, and 3 V across frequency ranges of 10–1000 Hz and 10–2000 Hz. Two structural models, ABH-ABH and ABH, were considered, with particles embedded at the mid-plane of each configuration. Additionally, sinusoidal translational motion was analyzed at frequencies between 550 and 625 Hz, with a displacement velocity of 0.5 m/s, to determine the loss factor damping. The numerical results show consistent trends with experimental measurements, reinforcing the effectiveness of particle-enhanced ABH structures in vibration control. Full article

This work addresses the numerical solution of fourth-order initial value problems of the form $y^{\left(4\right)}=f(x,y,y^{\prime })$, extending the capabilities of standard Runge–Kutta–Nyström (RKN) methods which are typically limited to $y^{\left(4\right)}=f(x,y)$. Problems of this type arise naturally in structural and vibroacoustic dynamics, where velocity-dependent damping and coupling effects are essential for realistic modeling. Despite their practical importance, efficient explicit schemes that preserve the fourth-order structure while allowing derivative dependence remain limited. We generally present an explicit s-stage method that incorporates the first derivative into the internal stage approximations, necessitating the introduction of a new matrix parameter D in the order conditions. We successfully derive the algebraic order conditions for this extended method up to the seventh algebraic order. A particular pair of orders 6(4) is constructed at an effective cost of only four stages per step in contrast to eight function evaluations required in conventional RK pairs. This reduction in effective stage cost, together with the direct treatment of derivative-dependent terms, constitutes a structural and computational distinction from existing Runge–Kutta and RKN approaches. To demonstrate the physical relevance of the proposed solvers, we examine coupled fourth-order models arising in structural and vibroacoustic dynamics, including viscoelastic beam systems with aerodynamic (velocity-proportional) damping and structure–acoustic interaction in a thin-walled duct. These examples illustrate the capability of the method to handle coupled dynamics with derivative-dependent damping and source terms that are central to realistic modeling of such systems. On these representative problems, the proposed pair clearly and decisively outperforms existing Runge–Kutta pairs from the current literature, achieving substantially higher accuracy for the same computational effort. The results indicate that explicit fourth-order Nyström-type schemes with derivative-aware internal stages provide both a theoretical extension of classical RKN theory and measurable efficiency gains, offering a competitive alternative to reduction-based first-order formulations for velocity-dependent fourth-order systems. Full article