Recent advances in eye-tracking technologies have fostered growing interest in their integration with acoustic research for investigating auditory perception and human behavioral responses. This study presents a structured literature review of recent developments at the intersection of eye tracking and acoustics, with the aim of analyzing how eye-movement data can support the interpretation of auditory events, spatial listening behaviors, and multimodal human-environment interactions. The reviewed studies were organized into four main research areas focusing on the application of eye-tracking in acoustics: sound source localization and identification, sound event detection and classification, acoustic sensing and multimodal systems, and soundscape and perceptual acoustic studies. The analysis indicates that eye-movement patterns can provide useful indicators of auditory attention and perceptual processes, particularly when combined with complementary physiological, visual, and acoustic sensing modalities. Furthermore, recent methodological advances, including real-time processing, machine learning algorithms, and sensor fusion techniques, have contributed to improving the robustness and accuracy of multimodal data analysis. Nevertheless, the review also highlights several limitations in current research, such as the lack of standardized experimental protocols, inter-individual variability, and susceptibility to environmental noise and external interference. Finally, future research perspectives are discussed, emphasizing the development of standardized and adaptive multimodal frameworks for human behavior modeling and intelligent acoustic monitoring systems.
Evaporation-induced self-assembly transforms dilute nanoparticle suspensions into ordered plasmonic superlattices, yet the microscopic mechanisms remain unclear for anisotropic particles in water. Here, we study the drying kinetics of silver nanorod (AgNR) dispersions in cetyltrimethylammonium chloride (CTAC) using time-resolved levitated small-angle X-ray scattering (SAXS), complemented by microbeam SAXS and focused ion beam-scanning electron microscopy on dried samples. The key parameter governing superlattice formation is the initial surfactant concentration, outweighing nanoparticle concentration and shape effects. AgNR ordering is synchronized with surfactant organization: CTAC micelles induce depletion attractions that drive nucleation and growth, followed by structural arrest upon CTAC gelation. These findings are directly relevant for improving the design of plasmonic metamaterial and nanoparticle (NP) self-assemblies in a broad sense. Moreover, the dual role played by CTAC micelles (promotion of NPs ordering followed by structural arrest) likely represents a mechanism applicable to other systems where depletants undergo gelation during drying.
Anxiety involves heightened vigilance and sustained anticipatory responding that may reflect impaired sensory habituation. Disrupted sensory filtering may bias monitoring toward threat, thereby promoting persistent autonomic and defensive responses. We examined associations between state and trait anxiety and acoustic startle habituation across somatic (electromyogram [EMG]), cortical (electroencephalogram [EEG]), and autonomic (electrodermal response [EDR]) measures and tested whether modulation of the right anterior insula (AI) or anterior midcingulate cortex (aMCC) with low-intensity focused ultrasound (LIFU) alters these dynamics. Forty healthy participants (median State-Trait Anxiety Inventory-Trait score = 39.5, range = 25-68) completed 3 sessions (AI, aMCC, sham) during which EMG, EEG, and EDR were recorded during 12 acoustic startle trials before and after LIFU. Habituation slopes were computed separately for early (trials 2-6) and late (trials 7-12) phases and analyzed with linear mixed-effects models controlling for baseline magnitude and anxiety covariates. Before LIFU, higher trait anxiety predicted weaker EMG habituation (ρ ≈ 0.3), whereas state anxiety was unrelated to habituation in any modality. Early slopes were steeper than late slopes across measures, indicating reduced adaptation over repeated trials. LIFU to the AI or aMCC did not alter EMG or EEG habituation but enhanced early-phase EDR habituation relative to sham, indicating transient autonomic facilitation. EMG and EEG habituation were correlated (ρ ≈ 0.4-0.5, p < .01), independent of anxiety, whereas EDR habituation varied independently. Coordinated cortical-somatic habituation may represent an anxiety-relevant biomarker, while AI and aMCC neuromodulation selectively facilitates autonomic adaptation. Anxiety is often linked to heightened vigilance and difficulty adapting to repeated or harmless sensory events. One way to study this process is through the acoustic startle reflex—a rapid defensive response to sudden sounds—that normally becomes smaller with repetition, a process known as habituation. Impaired habituation may reflect difficulty filtering sensory information and could contribute to persistent anxiety and autonomic arousal. In this study, healthy adults completed repeated startle testing while researchers recorded muscle activity, brain signals, and autonomic responses such as sweating. Participants also received brief noninvasive brain stimulation using low-intensity focused ultrasound (LIFU), a technology that can temporarily influence deep brain regions without surgery. Ultrasound targeted either the anterior insula or anterior midcingulate cortex, two areas involved in threat processing and bodily regulation. Individuals with higher trait anxiety showed slower adaptation of muscle startle responses, suggesting reduced sensory inhibition. Ultrasound stimulation did not change brain or muscle habituation but enhanced early autonomic adaptation compared with sham stimulation. These findings suggest that anxiety affects how the body adapts to repeated sensory events and that noninvasive neuromodulation may selectively influence autonomic regulation relevant to anxiety.
Exposure to blast waves without kinetic, penetrating, thermal, or toxic components causes a distinct form of traumatic brain injury, termed primary blast-induced TBI (pbTBI). Clinical manifestations of pbTBI span a wide spectrum, ranging from life-threatening intracranial hemorrhage, hyperemia, and delayed cerebral edema to mild and transient neurological symptoms without detectable structural abnormalities on routine imaging. At the mild end of the spectrum, symptoms after a single exposure may resolve quickly, yet repeated exposures-even at very low levels, termed "subconcussive"-can develop into post-concussive syndrome (PCS) or persistent post-concussive symptoms (PPCS) in a subset of individuals. Despite extensive studies, the molecular pathobiology linking primary blast exposure to delayed and sometimes chronic neurobehavioral deficits remains incompletely understood. A mechanistic framework connecting blast-wave physics to biomechanics to biological vulnerability may therefore help define exposure hazards, interpret clinical symptomatology, and guide diagnostic and therapeutic development. This review summarizes the physics of primary blast waves, the resulting biomechanical responses, and candidate biological substrates, emphasizing structures and interfaces with distinct acoustic impedances across anatomical, tissue, cellular, and molecular scales. We synthesize evidence supporting the hypothesis that the cerebral vasculature and endothelial cells represent critically vulnerable substrates of primary blast-wave injury, in part because the vascular tree constitutes the brain's largest and most widely distributed interface between compartments with different acoustic impedances. Across experimental and human studies, endothelial stress, vascular injury, and downstream neuroinflammation emerge as convergent molecular responses to primary blast exposure. Temporal dynamics are central to understanding pbTBI because many blast-induced processes unfold in sequential phases. These observations support conceptualizing pbTBI as a condition characterized by prominent cerebrovascular injury of varying severity with secondary consequences for neuronal signaling, network function, and behavior. Within this framework, cerebrovascular and neurovascular unit (NVU) dysfunction provides a parsimonious bridge between primary blast-wave exposure and chronic symptom trajectories, where vascular pathology may offer more accessible therapeutic targets than neuronal injury. Key knowledge gaps include identifying which physical component(s) of the blast are most injurious, establishing biologically meaningful dose-response relationships at molecular and physiological levels, and defining windows of vulnerability during recovery that are relevant to repeated exposures. Addressing these gaps is essential for refining safety protocols, improving diagnostic specificity through mechanism-informed biomarkers, and developing evidence-based molecular and vascular therapeutic targets for pbTBI-associated conditions. Progress will require integrating waveform-aware dosimetry with longitudinal physiological and molecular monitoring across both preclinical and human cohorts. Such integration offers a practical path toward translating blast physics into actionable medical guidance for prevention, triage, and recovery management.
Anxiety disorders are highly prevalent in youth. Exposure-focused cognitive behavioral therapy (EF-CBT) is the first-line treatment, yet a substantial proportion of youth do not achieve full response. Clinicians currently lack scalable, objective markers to monitor treatment response as it unfolds during therapy, limiting the ability to make timely, data-informed treatment adjustments. This study examined whether youth speech features (acoustic and linguistic) from EF-CBT session audio recordings could predict anxiety improvement and model trajectories of response across treatment. The sample included 603 recorded sessions from 60 youth aged 7 to 17 years participating in a randomized clinical trial of 12-session EF-CBT. Improvement was defined as a ≥ 30% reduction in Pediatric Anxiety Rating Scale (PARS) scores between sessions, assessed by independent evaluators. Machine learning models were developed using data from pairs of sessions within individuals to predict whether improvement occurred between those sessions and were evaluated using internal cross-validation. Models using later-session features performed better than those using early-session features (AUC = 0.86 vs 0.82), indicating good discrimination between improvement and non-improvement. Acoustic features reflecting vocal variability and expressiveness (e.g., variation in pitch, loudness, and voice activity) were the most consistent predictors of improvement. A hybrid Empirical Bayes approach integrating speech-based model predictions with PARS-based priors produced the most accurate and clinically plausible trajectories compared to other approaches tested. These findings suggest that speech-derived markers may provide a scalable, session-level measure to support continuous monitoring of treatment response during EF-CBT for pediatric anxiety.
To address the limited spatial localization accuracy of partial discharge (PD) in high-voltage cable terminations and the difficulty in accurately determining the trigger time in traditional ultrasonic detection, this paper proposes an electro-acoustic synergistic localization technology based on a high-frequency current transformer (HFCT) and a Sagnac optical fiber interferometer. A high-sensitivity Sagnac acoustic sensor based on a 3D-printed photosensitive resin mandrel was developed. Through structural design and 0-50 kHz amplitude-frequency testing, the sensor exhibits a dominant resonant response at 33.2 kHz. This narrow-band, high-sensitivity characteristic effectively enhances the perception capability for weak PD ultrasonic signals. An electro-acoustic synergistic detection system was constructed, in which the high-frequency PD current signal captured by the HFCT was used as the electrical time reference, and a dual-channel Sagnac sensor array was used to extract the arrival times of ultrasonic waves. In a 12 kV laboratory cable-termination PD experiment, the proposed system identified the representative built-in air-gap PD source with an absolute localization error of 5 mm under the tested laboratory configuration. This value should be interpreted as the localization result for the tested representative defect, rather than as a generally validated accuracy specification of the system. This study provides a proof-of-concept laboratory demonstration of an electro-acoustic localization strategy that combines the fast electrical response of HFCT detection with the electromagnetic-interference immunity and acoustic sensitivity of Sagnac fiber-optic sensing.
Ultra-high-performance concrete (UHPC) has attracted extensive attention because of its superior mechanical performance and durability. However, many existing tensile constitutive models are still obtained mainly by fitting macroscopic stress-strain curves, and the coupling among tensile damage development, steel-fiber parameters, and structural-scale response has not been sufficiently clarified. In this work, an acoustic-emission-informed tensile damage model was established for UHPC. Direct tensile tests were carried out on UHPC specimens containing steel fibers with aspect ratios of 43, 65, and 100 and volume fractions ranging from 0.5% to 3.0%, while acoustic emission signals were collected during loading. The normalized cumulative AE count was adopted as a damage indicator, and its evolution with tensile strain was described using a Weibull-type function. A fiber factor combining fiber volume fraction and aspect ratio was further incorporated into the damage constitutive equation. The proposed relationship was checked against 14 independent tensile datasets reported in the literature. After correction, the mean relative error of the predicted model parameter was reduced to 2.6%, with a standard deviation of 4.1%, and the fitted stress-strain curves all achieved R2 values above 0.85. The constitutive model was then implemented in ABAQUS for the simulation of reinforced UHPC beams. By introducing a member-level reduction coefficient of μ = 0.84, the numerical load-deflection curve showed improved agreement with the experimental beam response. The coefficient is empirical and is applicable only to the beam configuration investigated here unless further validation is performed. Overall, the proposed model provides a damage-based link among AE monitoring, steel-fiber reinforcement parameters, and member-scale numerical analysis.
Quantitative ultrasound parameters offer effective tools to evaluate microstructural changes in liver tissue and noninvasively assess tissue health. We examined the relationship between steatotic liver disease severity, measured by the Total Severity Score (TSS), and the structure function (SF), a frequency-dependent ultrasound backscatter metric. TSS was calculated on a 0-14 scale from histological evaluation by a pathologist of fibrosis, inflammation, and steatosis in hematoxylin and eosin-stained liver slides. A total of 274 regions of interest (ROIs) were examined from histopathology liver specimens acquired from 49 human subjects. SF was calculated for every ROI over a spatial frequency range of 3-80 MHz using histology-determined cell nuclei positions. Results revealed a strong relationship between SF and TSS across all analyzed ROIs, and distinct frequency-dependent patterns emerged across TSS groups. Statistical analysis showed significant differences between TSS 0 and TSS 1-3 for the frequency range of 4.5 to 9.0 MHz, and between TSS 1-3 and TSS 4-6 for the frequency ranges of 3.0 to 9.0 MHz and 32.5 to 80.0 MHz. TSS groups 4-6 and 7-14 did not differ significantly, indicating a possible plateau in ultrasound response at later disease stages. These findings help lay the foundation for establishing SF as a potential ultrasonic biomarker for identifying and distinguishing steatotic liver disease stages in vivo.
The inherent performance conflicts among the acoustic, mechanical, and hydraulic properties of pervious concrete represent a core obstacle to its application as a low-noise pavement material. To address this challenge, this paper proposes a multi-objective synergistic optimization method based on Stacking ensemble learning and the NSGA-II algorithm to proactively optimize mix proportions, thereby achieving a balance and enhancement of multiple performance metrics. A comprehensive database, comprising both proprietary experimental data and data from the literature, was first established to systematically train and construct a high-precision predictive model for the sound absorption performance of pervious concrete. Subsequently, this model was combined with previously established models for compressive strength and permeability to serve as the fitness functions for the NSGA-II genetic algorithm, which performed a multi-objective search for optima. The accuracy and reliability of the optimization results were then confirmed through experimental validation. Results indicate that aggregate gradation has a significant impact on the sound absorption of pervious concrete, with a relative performance improvement of 95.7% between the optimal and poorest gradations. The constructed Stacking ensemble learning model achieved a coefficient of determination (R2) of 0.97, outperforming all individual models with minimal fluctuation. The proposed multi-objective optimization framework successfully resolved the intrinsic conflict between permeability, compressive strength, and sound absorption. The optimized mix proportion solution (O3) not only satisfied the standards for permeability and strength but also achieved superior sound absorption performance that surpassed all single-sized aggregate groups, with an error of only 8.9% between the model's prediction and the experimental value.
This study explores non-destructive techniques for characterizing the mechanical properties of materials pertinent to cultural heritage, emphasizing the preservation of sample integrity. A modified acoustic resonance method (MARM), utilizing a two-microphone configuration, is introduced for the simultaneous, fully non-contact determination of dynamic elastic modulus and damping (loss factor). The method is validated through comparison with the impulse excitation technique (IET) and ultrasonic pulse velocity testing (UT). The approach is applied to two material categories exhibiting contrasting porosities: dense natural stone and highly porous unfired clay. Results demonstrate strong concordance among all methods for stone, confirming the reliability of non-destructive techniques for homogeneous materials. Conversely, unfired clay displays greater variability attributable to its heterogeneous and porous nature, alongside increased damping. This investigation reveals that conventional modulus-strength correlations are not directly applicable to unfired clay. To address this, a simplified strength estimation model incorporating the estimated elastic modulus and porosity is proposed. The model achieves improved alignment with experimental data and delineates applicability boundaries for porous materials. The presented framework facilitates consistent, non-destructive evaluation of mechanical properties, with notable implications for the assessment and preservation of cultural heritage materials.
Schizophrenia is a complex neuropsychiatric disorder characterized by impairments in cognition, perception, and social behavior. Current antipsychotic medications primarily target dopaminergic signaling but often exhibit limited efficacy in treating negative and cognitive symptoms. Moreover, long-term antipsychotic therapy is frequently associated with clinically relevant adverse effects, including metabolic syndrome and extrapyramidal symptoms (EPS). Phosphodiesterase 10A (PDE10A) has emerged as a promising non-dopaminergic therapeutic target involved in regulating cyclic nucleotide signaling in the striatum. However, several synthetic PDE10A inhibitors have shown safety and tolerability limitations. This study therefore investigated the antipsychotic-like potential of the ethanol extract of Dracocephalum moldavica (EEDM) and its major flavone glycoside, tilianin, in an MK-801-induced schizophrenia-like mouse model and explored the underlying molecular mechanisms. Tilianin content in EEDM was quantified using HPLC, and phytochemical constituents were comprehensively characterized by UHPLC-Q-TOF LC-MS/MS. The inhibitory effect of EEDM and tilianin on PDE10A activity was evaluated using in vitro enzyme assays, supported by molecular docking. Behavioral tests, including the open field test (OFT), acoustic startle response (ASR), novel object recognition, and social interaction test (SIT), were performed in MK-801-induced schizophrenia-like mice. Mechanistic insights were explored through network pharmacology analysis and further validated by analyzing the cAMP/PKA/CREB signaling in the prefrontal cortex using Western blot analysis. HPLC analysis identified tilianin as the major constituent of EEDM, while UHPLC-Q-TOF LC-MS/MS profiling revealed a diverse phytochemical profile comprising multiple flavonoids and phenolic compounds. Molecular docking predicted stable, high-affinity binding of tilianin to the PDE10A catalytic site (docking score: -11.857 kcal/mol), and this prediction was supported by an enzymatic assay showing PDE10A inhibition by both EEDM (IC50 = 346.6 μg/ml) and tilianin (IC50 = 11.25 μg/ml; 25.20 μM). Tilianin ameliorated MK-801-induced hyperlocomotion, rescued prepulse inhibition (PPI) deficits, and reversed impairments in cognitive and social functions. Network pharmacology identified PDE10A, CREB1, ESR1, and MAPK1 as key hubs modulating synaptic plasticity. Furthermore, tilianin restored the disrupted cAMP/PKA/CREB signaling in the prefrontal cortex. These findings suggest that tilianin may act as a network-informed modulator associated with PDE10A inhibition and downstream neuroplastic signaling, providing a potential mechanistic basis for addressing the limited efficacy of conventional antipsychotic strategies in schizophrenia.
To synthesize the available evidence on medical therapies investigated for the management of NF2-related vestibular schwannomas (VS). Systematic review. Not applicable. NF2-related VS patients. Systemic medical therapies. Tumor control and hearing outcomes. Thirty studies met inclusion criteria: 18 investigated bevacizumab, 5 mTOR inhibitors, 5 tyrosine kinase inhibitors (TKIs), and 2 the vascular endothelial growth factor receptor (VEGFR) vaccine. Bevacizumab demonstrated the most consistent activity, with hearing improvement in up to 61% of patients, stable hearing in up to 84%, and tumor response in up to 60% (Level 4 evidence). Outcomes for mTOR inhibitors were variable, with 1 study stopped for futility, while others reported up to 100% hearing and tumor stability (Level 4 evidence). Five different TKIs showed hearing improvement rates ranging from 17 to 60% and tumor responses of 10 to 23.5% (Level 4 evidence). The VEGFR vaccine appeared safe and immunologically active, with radiologic and hearing improvement in one-third of patients (Level 4 evidence). Bevacizumab remains the most studied and clinically active systemic therapy for NF2-related VS, although evidence is largely nonrandomized. Other systemic therapies have demonstrated variable results in terms of hearing outcomes or tumor control. High-quality prospective and randomized controlled trials are needed to clarify efficacy, durability, and comparative effectiveness.
The targeted extraction of bioactive peptides from structurally robust biological matrices, such as velvet antler protein (VAP), is severely hindered by high steric hindrance and profound conformational stability. This study established a multiscale analytical framework to investigate ultrasound-assisted VAP modification and its association with antioxidant peptide release and Keap1/Nrf2-related cellular responses. High-intensity ultrasound pretreatment (optimized at 450 W, 30 min, 30 mL/g) effectively deconstructed the dense interfacial architecture of VAP, sub-micronizing the particle size to ∼175 nm and promoting an ultrasound-associated conformational transition from α -helices to random coils. Thermodynamic deconstruction revealed that acoustic shear forces significantly attenuated the denaturation enthalpy (ΔH) and elevated surface hydrophobicity (H0), alleviating steric constraints and driving an increase in targeted peptide yield from 44.82% to 68.32%. By integrating peptidomics, machine learning, and density functional theory (DFT), five representative antioxidant lead candidates were prioritized and subsequently synthesized for validation. DFT analyses suggested that these selected sequences may possess favorable electron-donating properties, potentially associated with aromatic residues, adjacent hydrophobic residues, and Pro-related conformational restriction. Furthermore, molecular docking suggested that these peptides may interact with the Keap1 Kelch domain, while in vitro cellular assays showed that the selected peptides restored endogenous antioxidant enzymes (SOD, CAT, GSH-Px) in H2O2-challenged RAW264.7 macrophages. These findings provide physicochemical and cellular evidence supporting ultrasound-assisted VAP modification and antioxidant peptide discovery, offering a laboratory-scale, mechanistically guided strategy for discovering functional peptides from structurally dense biological resources. Nevertheless, further pilot-scale validation is required before industrial translation.
Conventional elastic wave metamaterials are typically constrained by fixed structural configurations, which limits their ability to achieve reconfigurable and application-specific wave manipulation. To address this challenge, this work propose a mechanical-acoustic interaction (MAI) paradigm for mechanically programmable elastic wave control, in which acoustic functionalities are reconfigured through reversible switching between bistable mechanical states. The proposed acoustic dome metamaterial (ADM) consists of modular bistable units, where the peak and valley configurations serve as two mechanically programmable states without relying on electrical, magnetic, or thermal stimuli. By spatially encoding these bistable states, the band structure and transmission characteristics of the metamaterial can be reconfigured, enabling programmable control of elastic wave propagation, attenuation, and energy localization. Numerical and experimental results demonstrate low-frequency vibration suppression, reconfigurable waveguiding, and defect-state-enabled energy localization governed by mechanically encoded state patterns. Moreover, a one-press programming strategy is introduced to improve programming efficiency and reproducibility at the system level. These findings establish MAI as a physically intuitive and scalable mechanism for elastic wave programming, offering new opportunities for reconfigurable acoustic metamaterials and intelligent acoustic devices.
Pedestrian-vehicle collisions produce a rich kinematic record that is entirely lost by the time a forensic investigation begins. Recovering this record constitutes a state-estimation problem. This paper presents a Phase 1 design for a multimodal sensor fusion and signal-processing framework utilising 128-channel LiDAR, 1080p NIR stereo cameras, and a 2 kHz IMU, all fused via Kalman filtering and Savitzky-Golay polynomial differentiation. The framework is evaluated through Monte Carlo uncertainty propagation and sensitivity analysis applied to a constructed simulation scenario; no real clinical or forensic data are used in this Phase 1 report. Under simulated conditions with throw-distance measurement uncertainty of ±0.5 m, velocity reconstruction shows an estimated propagated uncertainty of ±2.03 km/h under expanded simulation conditions with vehicle-coefficient variance activated. Sensitivity analysis indicates that a 10% noise spike in acceleration would theoretically amplify injury metrics by 26.9%, providing quantitative justification for noise-optimal pre-filtering. The bimodal kinematic-acoustic architecture is proposed as a physically interpretable foundation for collision reconstruction; its experimental performance awaits Phase 2-4 validation. A five-phase validation roadmap is presented, progressing from FEA simulation to independent multi-site replication before any forensic deployment is proposed.
Pedestrian spaces along streets are important public activity areas, yet existing studies have rarely examined the influence of building height symmetry on both sides of the street on the acoustic environment. This study selected a typical street and established six scenario models with different degrees of symmetry based on field measurements and validated through odeon simulations. The variation of reverberation time (T30) was then analyzed, and under three scenarios with the most significant differences, speech signals were convolved with traffic noise to conduct subjective evaluations of speech intelligibility. The results indicate the following: (1) The greater the asymmetry between buildings on both sides, the lower the T30; in the completely asymmetric scenario, T30 is only 15.1% of that in the symmetric scenario. (2) The more symmetrical the buildings, the lower the subjective evaluation scores of speech intelligibility, indicating that changes in symmetry affect human communication. (3) The T30 on the side with taller buildings is consistently lower than that on the lower side, by a just noticeable difference value of approximately 7, indicating that differences between the two sides can be clearly perceived. This study provides a reference for the acoustic design of pedestrian spaces.
This study examines the potential of sustainable, biobased paper-based structures as panel/membrane sound absorbers. Although intact paper is naturally impermeable and a poor sound absorber, transforming it into complex three-dimensional origami geometries, specifically the Miura-ori pattern, could produce effective panel/membrane absorbers. Three distinct Miura-ori samples (A, B, and C) were fabricated with increasing geometric complexity, ranging from a simple triangular prism to a complex labyrinthine waveguide. The random incidence sound absorption coefficients of these samples were measured in a validated small-scale reverberation room. The underlying absorption mechanisms were further investigated through modal analysis and non-contact vibration velocity measurements. The results indicate that increased geometric complexity enhances acoustic performance. Sample C, the most complex structure, demonstrated the most consistent broadband absorption. The analysis confirmed a significant positive correlation between acoustic pressure modes, surface vibration velocity, and sound absorption peaks, indicating that acoustic energy dissipation is driven by the vibrational response of the paper membrane coupled with resonant modes in the air gap. This research demonstrates that tunable origami folding techniques using intact paper can be used to design lightweight acoustic treatments for diffuse sound fields in the mid-frequency range.
Blast traumatic brain injury (bTBI) is a significant clinical challenge particularly in military populations, yet the biological mechanisms linking cavitation-induced injuries in brain tissue remain poorly understood and effective treatments are limited. Intracranial cavitation may arise from blast wave exposure or other sources of mechanical and acoustic energy, and has been proposed to contribute to brain injury, but the cellular response to cavitation has been difficult to examine in controlled, physiologically relevant experimental systems. To address this limitation, we present a human brain microphysiological system to investigate cavitation-induced injury in a multicellular neural tissue environment. The platform consists of three-dimensional engineered brain tissue constructs containing human neurons, astrocytes, and microglia, and uses focused ultrasound to generate localized cavitation within the tissue. Cavitation injury produces dose- and time-dependent cytotoxic and excitotoxic cellular responses, including the release of clinically relevant neurotrauma biomarkers consistent with patterns reported in experimental and clinical TBI. Protein and functional analyses further reveal disruption of mechanotransduction-associated signaling, cytoskeletal integrity, and network activity. Together, these results demonstrate a scalable human multicellular brain tissue platform for mechanistic studies and evaluation of candidate therapeutics or protective strategies relevant to bTBI and other conditions where intracranial cavitation is implicated.
This work presents a generalized theoretical model of thermoacoustic wave propagation in saturated poroelastic media that incorporates hydrodynamic interactions and variable thermal conductivity. The model integrates generalized thermoelasticity and Biot's poroelastic theory with a temperature-dependent heat conduction law to describe coupled thermal, mechanical, and acoustic processes in a fluid-saturated porous medium. The governing system includes equations of motion, Darcy-type fluid flow, and a generalized heat conduction equation in which the thermal conductivity varies nonlinearly with temperature. The normal mode analysis technique is applied to obtain analytical expressions for the field variables in one-dimensional form. Numerical simulations illustrate the influence of the thermal conductivity variation parameter, porosity, and hydrodynamic coupling on the thermoacoustic wave behavior. The results reveal that variable conductivity significantly modifies the amplitude, phase velocity, and attenuation of thermal and acoustic waves, particularly under transient heating. The proposed framework offers enhanced theoretical insight into the sensitivity of thermoelastic, acoustic, and pore-pressure waves to temperature-dependent conductivity, providing a foundation for future calibration against experimental observations in porous semiconductors, geophysical rocks, and engineered porous composites.
Dairy milk fouling remains a persistent constraint on thermal processing efficiency, product quality, and food safety. However, industrial fouling control still relies predominantly on conservative cleaning schedules and indirect process indicators that respond only after substantial performance loss. This review synthesizes current understanding of dairy fouling mechanisms, heat-exchanger configurations, and emerging online sensing strategies with explicit reference to Technology Readiness Level (TRL) progression. It first outlines how protein-, mineral-, fat-, and biofilm-derived deposits interact with indirect and direct heating geometries to create system-specific fouling loci and sensor constraints. It then critically evaluates thermal, ultrasonic/acoustic, optical, electrochemical, electrical resistance and impedance, tomography, ohmic, QCM-based, and synchrotron imaging approaches for in situ fouling detection, benchmarking each against hygienic integration, early-stage sensitivity, and compatibility with plate heat exchangers, tubular systems, and direct steam injection plants. The analysis highlights that most modalities remain confined to laboratory or pilot scale (TRL 2-4), with no single sensor alone currently capable of resolving both fouling layer growth and thermal properties under full-scale ultra-high-temperature conditions. In response, the review proposes a multimodal, tiered sensing philosophy that couples complementary modalities to deliver condition-based monitoring architectures tailored to each heat-exchanger type. By mapping critical knowledge gaps and integration barriers, this work provides a structured roadmap for advancing from proof-of-concept sensors toward robust, real-time fouling monitoring in industrial dairy processing.