Search Results (220)

Ovarian follicular fluid (FF) is a metabolically active and biomarker-rich medium that mirrors the oocyte microenvironment. Its analysis is increasingly recognized in infertility diagnostics and assisted reproductive technologies (ART) for assessing oocyte competence, understanding reproductive disorders, and guiding personalized treatment. However, FF’s high viscosity, complex composition, and biochemical variability challenge reproducibility in sample preparation and molecular profiling. Sonication-based extraction has emerged as an effective approach to address these issues. By exploiting acoustic cavitation, sonication improves protein solubilization, metabolite release, and lipid recovery, while reducing solvent use and processing time. This review synthesizes recent advances in sonication-assisted FF analysis across proteomics, metabolomics, and lipidomics, emphasizing parameter optimization, integration with advanced mass spectrometry workflows, and emerging applications in microfluidics, automation, and point-of-care devices. Clinical implications are discussed in the context of enhanced biomarker discovery pipelines, real-time oocyte selection, and ART outcome prediction. Key challenges, such as preventing biomolecule degradation, standardizing protocols, and achieving inter-laboratory reproducibility, are addressed alongside regulatory considerations. Future directions highlight the potential of combining sonication with multi-omics strategies and AI-driven analytics, paving the way for high-throughput, standardized, and clinically actionable FF analysis to advance precision reproductive medicine. Full article

The structural integrity of transmission towers, as the backbone of power grids, is critical to overall grid safety, relying heavily on the reliability of bolted connections. Dynamic loads such as wind-induced vibrations can cause bolt loosening, potentially leading to structural deformation, cascading failures, and large-scale blackouts. Traditional manual inspection methods are inefficient, subjective, and hazardous. Existing automated approaches are often limited by environmental noise sensitivity, high computational complexity, sensor placement dependency, or the need for extensive labeled data. To address these challenges, this paper proposes a portable acoustic detection system based on Variational Mode Decomposition (VMD) and an Adversarial Multilayer Perceptual Network (AT-MLP). The VMD method effectively processes non-stationary and nonlinear acoustic signals to suppress noise and extract robust time–frequency features. The AT-MLP model then performs state identification, incorporating adversarial training to mitigate distribution discrepancies between training and testing data, thereby significantly improving generalization and noise robustness. Comparison results and analysis demonstrate that the proposed VMD and AT-MLP framework effectively mitigates structural variability and environmental interference, providing a reliable solution for bolt loosening detection. The proposed method bridges structural mechanics, acoustic signal processing, and lightweight intelligence, offering a scalable solution for condition assessment and risk-aware maintenance of transmission towers. Full article

Undetected cracks in railroad wheels pose significant safety and economic risks, while current inspection methods are limited by cost, coverage, or contact requirements. This study explores the use of passive, air-coupled ultrasonic acoustic (UA) sensors for detecting wheel damage on stationary or moving wheels. Two controlled datasets of wheelsets, one with clear damage and another with early, service-induced defects, were tested using hammer impacts. An automated system identified high-energy bursts and extracted features in both time and frequency domains, such as decay rate, spectral centroid, and entropy. The results demonstrate the effectiveness of UAE (ultrasonic acoustic emission) techniques through Kernel Density Estimation (KDE) visualization, hypothesis testing with effect sizes, and Receiver Operating Characteristic (ROC) analysis. The decay rate consistently proved to be the most effective discriminator, achieving near-perfect classification of severely damaged wheels and maintaining meaningful separation for early defects. Spectral features provided additional information but were less decisive. The frequency spectrum characteristics were effective across both axial and radial sensor orientations, with ultrasonic frequencies (20–80 kHz) offering higher spectral fidelity than sonic frequencies (1–20 kHz). This work establishes a validated “ground-truth” signature essential for developing a practical wayside detection system. The findings guide a targeted engineering approach to physically isolate this known signature from ambient noise and develop advanced models for reliable in-motion detection. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that significantly affects motor functions, including speech production. Voice analysis offers a less invasive, faster and more cost-effective approach for diagnosing and monitoring PD over time. This research introduces an automated system to distinguish between PD and non-PD individuals based on speech signals using state-of-the-art signal processing and machine learning (ML) methods. A publicly available voice dataset (Dataset 1, 81 samples) containing speech recordings from PD patients and non-PD individuals was used for model training and evaluation. Additionally, a small supplementary dataset (Dataset 2, 15 samples) was created although excluded from experiment, to illustrate potential future extensions of this work. Features such as Mel-frequency cepstral coefficients (MFCCs), spectrograms, Mel spectrograms and waveform representations were extracted to capture key vocal impairments related to PD, including diminished vocal range, weak harmonics, elevated spectral entropy and impaired formant structures. These extracted features were used to train and evaluate several ML models, including support vector machine (SVM), XGBoost and logistic regression, as well as deep learning (DL)architectures such as deep neural networks (DNN), convolutional neural networks (CNN) combined with long short-term memory (LSTM), CNN + gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM). Experimental results show that DL models, particularly BiLSTM, outperform traditional ML models, achieving 97% accuracy and an AUC of 0.95. The comprehensive feature extraction from both datasets enabled robust classification of PD and non-PD speech signals. These findings highlight the potential of integrating acoustic features with DL methods for early diagnosis and monitoring of Parkinson’s Disease. Full article

Rail transit dispatch speech plays a critical role in ensuring the safety of urban rail operations. To enable automated and accurate compliance assessment of dispatch speech, this study proposes an improved deep learning model to address the limitations of conventional approaches in terms of accuracy and robustness. Building upon the baseline Whisper model, two key enhancements are introduced: (1) low-rank adaptation (LoRA) fine-tuning to better adapt the model to the specific acoustic and linguistic characteristics of rail transit dispatch speech, and (2) a novel entity-aware attention mechanism that incorporates named entity recognition (NER) embeddings into the decoder. This mechanism enables attention computation between words belonging to the same entity category across different commands and recitations, which helps highlight keywords critical for compliance assessment and achieve precise inter-sentence element alignment. Experimental results on real-world test sets demonstrate that the proposed model improves recognition accuracy by 30.5% compared to the baseline model. In terms of robustness, we evaluate the relative performance retention under severe noise conditions. While Zero-shot, Full Fine-tuning, and LoRA-only models achieve robustness scores of 72.2%, 72.4%, and 72.1%, respectively, and the NER-only variant reaches 88.1%, our proposed approach further improves to 89.6%. These results validate the model’s significant robustness and its potential to provide efficient and reliable technical support for ensuring the normative use of dispatch speech in urban rail transit operations. Full article

Underwater wireless communication (UWC) is an emerging technology crucial for automating marine industries, such as offshore aquaculture and energy production, and military applications. It is a key part of the 6G vision of creating a hyperconnected world for extending connectivity to the underwater environment. Of the three main practicable UWC technologies (acoustic, optical, and radiofrequency), acoustic methods are best for far-reaching links, while optical is best for high-bandwidth communication. Recently, utilizing reconfigurable intelligent surfaces (RISs) has become a hot topic in terrestrial applications, underscoring significant benefits for extending coverage, providing connectivity to blind spots, wireless power transmission, and more. However, the potential for further research works in underwater RIS is vast. Here, for the first time, we conduct an extensive survey of state-of-the-art of RIS and metasurfaces with a focus on underwater applications. Within a holistic perspective, this survey systematically evaluates acoustic, optical, and hybrid RIS, showing that environment-aware channel switching and joint communication architectures could deliver holistic gains over single-domain RIS in the distance–bandwidth trade-off, congestion mitigation, security, and energy efficiency. Additional focus is placed on the current challenges from research and realization perspectives. We discuss recent advances and suggest design considerations for coupling hybrid RIS with optical energy and piezoelectric acoustic energy harvesting, which along with distributed relaying, could realize self-sustainable underwater networks that are highly reliable, long-range, and high throughput. The most impactful future directions seem to be in applying RIS for enhancing underwater links in inhomogeneous environments and overcoming time-varying effects, realizing RIS hardware suitable for the underwater conditions, and achieving simultaneous transmission and reflection (STAR-RIS), and, particularly, in optical links—integrating the latest developments in metasurfaces. Full article

The structural integrity of poultry eggs is essential for food safety, economic value, and hatchability. External eggshell quality—measured by thickness, strength, cracks, color, and cleanliness—is a key criterion for grading and sorting. Traditional assessment methods, although simple, suffer from subjectivity, low efficiency, and destructive nature. In contrast, recent developments in nondestructive testing (NDT) technologies have enabled precise, automated, and real-time evaluation of eggshell characteristics. This review systematically summarizes state-of-the-art NDT techniques including acoustic resonance, ultrasonic imaging, terahertz spectroscopy, machine vision, and electrical property sensing. Deep learning and sensor fusion methods are highlighted for their superior accuracy in microcrack detection (up to 99.4%) and shell strength prediction. We further discuss emerging challenges such as noise interference, signal variability, and scalability for industrial deployment. The integration of explainable AI, multimodal data acquisition, and edge computing is proposed as a future direction to develop intelligent, scalable, and cost-effective eggshell inspection systems. This comprehensive analysis provides a valuable reference for advancing nondestructive quality control in poultry product supply chains. Full article

Distributed Acoustic Sensing (DAS) is an emerging technology in the maritime domain, enabling the use of existing fiber optic cables to detect acoustic signals in the marine environment. In this study, we present an automated signal detection and classification framework for DAS data that supports near-real-time processing. Using data from the SHEFA-2 cable between the Faroe and Shetland Islands, we develop a method to identify acoustic signals and generate both labeled and unlabeled datasets based on their spectral characteristics. Principal component analysis (PCA) is used to explore separability in the labeled data, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is applied to classify unlabeled data. Experimental validation using clustering metrics shows that with the full dataset, we can achieve a Davies–Bouldin Index of 0.828, a Silhouette Score of 0.124, and a Calinski–Harabasz Index of 189.8. The clustering quality degrades significantly when more than 20% of the labeled data is excluded, highlighting the importance of maintaining sufficient labeled samples for robust classification. Our results demonstrate the potential to distinguish between signal sources such as ships, vehicles, earthquakes, and possible cable damage, offering valuable insights for maritime monitoring and security. Full article

Ship acoustic signal classification is essential for vessel identification, underwater navigation, and maritime security. Traditional methods struggle with the non-stationary nature and noise of ship acoustic signals, reducing classification accuracy. To address these challenges, we propose an automated pipeline that integrates Empirical Mode Decomposition (EMD), adaptive wavelet filtering, feature selection, and a Bayesian-optimized Random Forest classifier. The framework begins with EMD-based decomposition, where the most informative Intrinsic Mode Functions (IMFs) are selected using Signal-to-Noise Ratio (SNR) analysis. Wavelet filtering is applied to reduce noise, with optimal wavelet parameters determined via SNR and Stein’s Unbiased Risk Estimate (SURE) criteria. Features extracted from statistical, frequency domain (FFT), and time–frequency (wavelet) metrics are ranked, and the top 11 most important features are selected for classification. A Bayesian-optimized Random Forest classifier is trained using the extracted features, ensuring optimal hyperparameter selection and reducing computational complexity. The classification results are further enhanced using a majority voting strategy, improving the accuracy of the final object identification. The proposed approach demonstrates high accuracy, improved noise suppression, and robust classification performance. The methodology is scalable, computationally efficient, and suitable for real-time maritime applications. Full article

Alzheimer’s disease (AD) has emerged as a global health threat that demands early detection to seize the optimal intervention opportunity. Central nervous system (CNS)-derived extracellular vesicles (EVs), lipid-bilayer nanoparticles released by CNS cells, carry key biomolecules involved in AD pathology, positioning them as a promising source of biomarkers for early detection. Current breakthroughs in EV-based isolation and detection technologies have opened up the possibility of early, accurate AD diagnosis. This review summarizes their multifaceted roles in AD pathogenesis, including amyloid-β (Aβ) aggregation, tau propagation, neuroinflammation, and synaptic dysfunction, and highlights neuron- and glia-derived EV biomarkers with translational potential. We further outline recent advances in EV isolation techniques—including density-, size-, charge/dielectric-, immunoaffinity-, and acoustics-based approaches—and emerging detection platforms such as fluorescence, surface plasmon resonance (SPR), surface-enhanced Raman spectroscopy (SERS), electrochemical, and nanomechanical sensors for sensitive, multiplex AD diagnostics. Finally, we discuss key challenges, including standardization, sensitivity, and high-throughput adaptation, and explore future directions such as automated microfluidics and single-vesicle analysis. CNS-derived EVs hold significant promise as minimally invasive, next-generation tools for early AD detection and precision medicine. Full article

Accurate stress evaluation of structural components during manufacturing and operation is essential for ensuring the safety and reliability of advanced equipment in aerospace, defense, and other high-performance fields. However, existing electromagnetic ultrasonic stress detection methods are often limited by low signal amplitude and limited adaptability to complex environments, hindering their practical deployment for in situ testing. This study proposes a novel surface wave transducer structure for stress detection based on acoustoelastic theory combined with electromagnetic ultrasonic technology. It innovatively designs a surface wave transducer composed of multiple proportionally scaled dislocation meandering coils. This innovative configuration significantly enhances the Lorentz force distribution and coupling efficiency, which accurately measure the stress of components through acoustic time delays and present an experimental method for applying electromagnetic ultrasonic technology to in situ stress detection. Finite element simulations confirmed the optimized acoustic field characteristics, and experimental validation on 6061 aluminum alloy specimens demonstrated a 111.1% improvement in signal amplitude compared to conventional designs. Through multiple experiments and curve fitting, the average relative error of the measurement results is less than 4.53%, verifying the accuracy of the detection method. Further testing under random stress conditions validated the transducer’s feasibility for in situ testing in production and service environments. Owing to its enhanced signal strength, compact structure, and suitability for integration with automated inspection systems, the proposed transducer shows strong potential for in situ stress monitoring in demanding industrial environments. Full article

Addressing the challenges of bulky, low-efficiency sound-insulation materials at low frequencies, this work proposes an acoustic metamaterial based on curve fractal channels. Each unit cell comprises a concentric circular-ring channel recursively iterated: as the fractal order increases, the channel path length grows exponentially, enabling outstanding sound-insulation performance within a deep-subwavelength thickness. Finite-element and transfer-matrix analyses show that increasing the fractal order from one to three raises the number of bandgaps from three to five and expands total stop-band coverage from 17% to over 40% within a deep-subwavelength thickness. Four-microphone impedance-tube measurements on the third-order sample validate a peak transmission loss of 75 dB at 495 Hz, in excellent agreement with simulations. Compared to conventional zigzag and Hilbert-maze designs, this curve fractal architecture delivers enhanced low-frequency broadband insulation, structural lightweighting, and ease of fabrication, making it a promising solution for noise control in machine rooms, ducting systems, and traffic environments. The method proposed in this paper can be applied to noise reduction of transmission parts for ceramic automation production. Full article

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO^® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software^®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed. Full article

This research presents DuSAFNet, a lightweight deep neural network for fine-grained bird audio classification. DuSAFNet combines dual-path feature fusion, spectral–temporal attention, and a multi-band ArcMarginProduct classifier to enhance inter-class separability and capture both local and global spectro–temporal cues. Unlike single-feature approaches, DuSAFNet captures both local spectral textures and long-range temporal dependencies in Mel-spectrogram inputs and explicitly enhances inter-class separability across low, mid, and high frequency bands. On a curated dataset of 17,653 three-second recordings spanning 18 species, DuSAFNet achieves 96.88% accuracy and a 96.83% F1 score using only 6.77 M parameters and 2.275 GFLOPs. Cross-dataset evaluation on Birdsdata yields 93.74% accuracy, demonstrating robust generalization to new recording conditions. Its lightweight design and high performance make DuSAFNet well-suited for edge-device deployment and real-time alerts for rare or threatened species. This work lays the foundation for scalable, automated acoustic monitoring to inform biodiversity assessments and conservation planning. Full article

Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article