Porous materials are widely used for sound absorption, but predicting their acoustic response remains challenging because of the complex interaction between pore morphology and material parameters such as porosity, airflow resistivity, tortuosity, characteristic lengths, density and thickness. This mini-review critically examines the evolution of predictive approaches from empirical formulations, including the Delany–Bazley (DB) and Miki models, to physically based equivalent-fluid models such as Johnson–Champoux–Allard (JCA) and Johnson–Champoux–Allard–Lafarge (JCAL), as well as inverse, numerical, and emerging machine-learning (ML) approaches. Particular attention is given to their applicability to conventional polymeric foams, natural and biomass-based materials, recycled materials and architected porous structures. The review examines the relationship between measurable macroscopic parameters, pore-scale morphology and predicted acoustic response, with emphasis on the limitations associated with material heterogeneity, model assumptions and parameter uncertainty. Inverse acoustic characterization is discussed in terms of parameter non-uniqueness, sensitivity to initial conditions and bounds and the distinction between fitting accuracy and physical identifiability. Overall, increasing model complexity does not necessarily improve predictive reliability. Reliable prediction requires physically meaningful parameters, appropriate model assumptions, independent validation and a balance between model complexity, parameter identifiability, and computational cost.
Spectral degradation disrupts the rapid, incremental processes that support spoken word recognition, but most studies quantify endpoint accuracy rather than the time course of lexical access, and the extent to which cognitive resources compensate for this disruption remains unclear. This study examined the influence of spectral resolution and cognition on lexical access using a gated word recognition paradigm. Thirty adults with normal hearing identified monosyllabic words presented in progressively longer fragments under full-spectrum and 8-channel sine-wave vocoded conditions. Isolation points (IPs), the shortest segment required for stable identification, were measured. Speech perception was assessed using vocoded AzBio sentences in quiet and noise (+10 dB and +5 dB SNR) and vocoded Consonant-Nucleus-Consonant (CNC) words in quiet. Working memory and executive function were evaluated using the Abbreviated Reading Span Test and Trail Making Test. Full-spectrum speech supported efficient lexical access, whereas vocoded speech required nearly complete word information (62.7% vs. 98.9% of word duration), indicating severe delays in incremental processing. Near-ceiling CNC and AzBio scores in quiet did not correlate with IPs, but IPs were strongly associated with AzBio performance in noise. Cognitive measures were intercorrelated but not significantly associated with IPs or speech perception scores after correction for multiple comparisons, although the sample was powered to detect only large associations. These findings suggest that the efficiency of incremental lexical access under spectral degradation captures variability in speech-in-noise perception that is not evident in endpoint accuracy in quiet.
Temperature-dependent characterization of nanomaterials is commonly performed through electrical measurements, and many temperature-sensing applications similarly rely on electrical readout signals. Although these approaches are simple and widely used, they require direct sample contact and may lose sensitivity when the electrical response is weak, saturated, or only weakly dependent on temperature. In this study, a photoacoustic (PA) measurement system was used to evaluate the temperature-dependent response of AgNW networks. PA measurement system detects ultrasonic waves generated through transient thermoelastic expansion after optical absorption, which enables sensitive probing of temperature-dependent optothermal responses beyond conventional electrical measurements. Silver nanowire (AgNW) films with different surface coverage were fabricated through one to four repeated spin-coating cycles using a 1% AgNW ethanol dispersion containing nanowires with diameters of 20–40 nm and lengths of 10–20 µm. The photoacoustic (PA) signals of the fabricated films were measured at various temperatures using a customized PA measurement system and were compared with sheet resistance values obtained using a four-point probe. Both sheet resistance and PA signals increased with increasing temperature, regardless of AgNW surface coverage. Notably, AgNW films with higher surface coverages showed only weak temperature dependence in sheet resistance but pronounced temperature-dependent PA response. These results indicate that PA analysis is particularly useful for characterizing AgNW networks with higher surface coverage, including those used in stretchable electrodes. All samples showed linear relationships for both sheet resistance and PA signals, with coefficients of determination (R2) exceeding 0.9. Overall, these findings suggest that PA measurement may provide complementary information on temperature-dependent behavior that is not fully reflected in sheet-resistance measurements.
In this paper, a modal decomposition method is employed to analyze the properties of Rayleigh–Lamb waves propagating in an isotropic plate containing an open surface-breaking crack. The mode conversion coefficients (MCCs) are calculated as functions of crack depth and ultrasonic frequency for both an incident Lamb wave. Compared with the incident A0 mode, the MCC of the S0 mode exhibits a less smooth dependence on the phase of the crack modulated. The experimental, finite element method (FEM) and analytical results confirm that crack characteristics can be quantitatively evaluated from the Lamb wave mode conversion indued by the crack. This finding is beneficial for interpreting the detected ultrasonic signals in scenarios involving crack measurements between different waves, such as the modulation of the probing wave’s MCC by the material response in a tensile test machine.
Acoustic vehicle classification from roadside microphones supports source-specific traffic noise monitoring, but classifiers trained on one dataset generalize poorly to recordings from different environments. This study compares pre-trained audio transformers (BEATs, AST) against a CNN baseline for cross-dataset vehicle classification into five categories (car, truck, motorcycle, bus, background), training on the IDMT-Traffic dataset (Germany) and testing on the MELAUDIS dataset (Australia). Two domain adaptation methods (DANN, ArcFace) are applied to the best-performing transformer. BEATs achieves a balanced F1 of 0.46, a 77% improvement over the CNN baseline (0.26). AST achieves F1 = 0.43, a 65% improvement. Both pre-trained transformers substantially outperform the baseline, with self-supervised pre-training producing slightly more transferable representations than supervised pre-training. ArcFace metric learning achieves the highest cross-dataset F1 (0.50), modestly outperforming standard fine-tuning. DANN degrades cross-dataset performance but achieves the highest microphone robustness score (F1 = 0.80). Classification of the underrepresented bus class (53 training samples) improves from F1 = 0.38 to 0.54 with ArcFace. Microphone robustness is strong across all models (BEATs F1 = 0.75), confirming that recording environment dominates the domain shift over hardware variation. The choice of domain adaptation strategy should be guided by the expected type of domain shift.
