Search Results (825)

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. 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

The escalating environmental noise pollution along urban rail transit corridors, exacerbated by rapid urbanization, necessitates innovative and efficient noise control measures. A comprehensive investigation was conducted that utilized field measurements of train passing-by noise to establish a statistical energy analysis model for evaluating the acoustic performance of both vertical (VB) and fully enclosed (FB) barrier configurations. The study incorporated Maa’s theory of micro-perforated plate (MPP) parameter optimization and developed a neural network surrogate model focused on insertion loss maximization for barrier geometric design. Key findings revealed significant barrier-induced near-track noise amplification, with peak effects observed at the point located 1 m from the barrier and 2 m above the rail. Frequency-dependent analysis demonstrated a characteristic rise-and-fall reflection pattern, showing maximum amplifications of 1.47 dB for VB and 4.13 dB for FB within the 400–2000 Hz range. The implementation of optimized MPPs was found to effectively eliminate the near-field noise amplification effects, achieving sound pressure level reductions of 4–8 dB at acoustically sensitive locations. Furthermore, the high-precision surrogate model (R² = 0.9094, MSE = 0.8711) facilitated optimal geometric design solutions. The synergistic combination of MPP absorption characteristics and geometric optimization resulted in substantially enhanced barrier performance, offering practical solutions for urban rail noise mitigation strategies. Full article

The aperture of the towed horizontal line array is limited and the received signal is unstable in a complex ocean environment, making it difficult to distinguish the location of the sound source. To address this challenge, this paper presents a MoELocNet (Mixture of Experts Localization Network) for deep-sea sound source localization, leveraging interference structures in range-frequency domain signals from a towed horizontal line array. Unlike traditional correlation-based methods constrained by time-varying ocean environments and low signal-to-noise ratios, the model employs multi-expert and multi-task learning to extract interference periods from single-frame data, enabling robust estimation of source range and depth. Simulation results demonstrate its superior performance in the deep-sea shadow zone, achieving a range localization error of 0.029 km and a depth error of 0.072 m. The method exhibits strong noise robustness and delivers satisfactory results across diverse deep-sea zones, with optimal performance in shadow zones and secondary effectiveness in the direct arrival zone. Full article

Accurate localization plays a critical role in enabling underwater vehicle autonomy. In this work, we develop a robust infrastructure-based localization framework that estimates the position and orientation of underwater vehicles using only range measurements from long baseline (LBL) acoustic beacons to multiple on-board receivers. The proposed framework integrates three key components, each formulated as a convex optimization problem. First, we introduce a robust calibration function that unifies multiple sources of measurement error—such as range-dependent degradation, variable sound speed, and latency—by modeling them through a monotonic function. This function bounds the true distance and defines a convex feasible set for each receiver location. Next, we estimate the receiver positions as the center of this feasible region, using two notions of centrality: the Chebyshev center and the maximum volume inscribed ellipsoid (MVE), both formulated as convex programs. Finally, we recover the vehicle’s full 6-DOF pose by enforcing rigid-body constraints on the estimated receiver positions. To do this, we leverage the known geometric configuration of the receivers in the vehicle and solve the Orthogonal Procrustes Problem to compute the rotation matrix that best aligns the estimated and known configurations, thereby correcting the position estimates and determining the vehicle orientation. We evaluate the proposed method through both numerical simulations and field experiments. To further enhance robustness under real-world conditions, we model beacon-location uncertainty—due to mooring slack and water currents—as bounded spherical regions around nominal beacon positions. We then mitigate the uncertainty by integrating the modified range constraints into the MVE position estimation formulation, ensuring reliable localization even under infrastructure drift. Full article

Acaricide usage has led to the growing problem of resistance in ticks. A heavy tick burden and the presence of ticks on animals throughout the year, despite the monthly application of acaricides, in farms in the United Arab Emirates formed the motivation for this study. The objectives of this research were as follows: (a) to assess the acaricide resistance status of the most prevalent tick Hyalomma spp. to widely used acaricides Cypermethrin and Deltamethrin; (b) to identify the association of farm management practices and farm-level risk factors with the failure of tick treatment (acaracide resistance). A total of 1600 ticks were collected from 20 farms located in three different regions of Abu Dhabi Emirate including Al Ain (n = 10), Al Dhafra (n = 5), and Abu Dhabi (n = 5). The ticks were subjected to an in vitro bioassay adult immersion test (AIT) modified with a discriminating dose (AIT-DD) against commercial preparations of Cypermethrin and Deltamethrin. A questionnaire was designed to collect metadata and information on farm management and the farm-level risk factors associated with routine farm practices relating to the treatment and control of tick and blood parasite infections in camels and small ruminant populations. Hyalomma anatolicum and Hyalomma dromedarii were identified among the collected ticks, with H. anatolicum being the most prevalent tick species (70%) in the present study. The test results of the in vitro bioassay revealed varied emerging resistance to both of the acaricides in the majority of the three regions; fully susceptible tick isolates with zero resistance to Deltamethrin were recorded in one farm at Al Ain and two farms in the Abu Dhabi region. A questionnaire analysis showed that the failure of tick treatment in farms varied with the presence or absence of vegetation areas, types of animal breeds, and management practices. This study reports the emergence of resistance in ticks to Cypermethrin and Deltamethrin across the Abu Dhabi Emirate, indicating a strict warning for the cautious use of acaricides. There is also a need to improve awareness about sound tick management and control practices among farm owners through a multidisciplinary approach adopting integrated pest management strategies that engage farmers, veterinarians, and policy makers. Full article

Deep margin elevation (DME) is a widely adopted technique for managing subgingival cervical proximal margins by repositioning them to a supragingival location. This approach enhances access, visibility, and control in these anatomically challenging areas. This narrative review aimed to evaluate current evidence on the indications, materials, clinical protocols, and outcomes of DME. A structured search was conducted in PubMed, the Cochrane Library and Scopus up to February 2025, using keywords such as “deep margin elevation”, “proximal box elevation” and “subgingival margin.” Clinical studies, in vitro investigations, relevant reviews and reports in English were included. A total of 59 articles were selected based on eligibility criteria. The hypothesis was that DME can serve as a reliable alternative to surgical crown lengthening in appropriate cases. A variety of materials have been investigated for use as the intermediate layer, with composite resins of varying viscosities and filler compositions being preferred due to their favorable long-term mechanical properties. DME may reduce the need for surgical intervention while maintaining periodontal health; however further randomized clinical trials are needed to clarify the material selection, establish long-term outcomes, and standardize clinical protocols. Understanding the indications, limitations, and protocol of DME is critical for achieving biologically sound and predictably functional restorations. Full article

The presented article analyzes the impact of internal defects on the modal responses of polyamide parts subjected to bending. Samples with defects of various sizes (0, 3, 5, 7, and 10 mm) located at the neutral bending line were tested. Modal properties were measured via an acoustic and a vibration sensor, using impulse excitation and fast Fourier transform (FFT) analysis. Modal properties include peak frequency, damping and amplitude. Non-defective samples show lower peak frequency and stronger amplitude for both detectors. Moreover, defects larger than 3 mm have minimal impact on peak frequency. The vibration detector is more sensitive to delamination presented at 7 and 10 mm defects. In addition, elevated peak frequency at 3 mm is the result of local hardening at the defect edge. Moreover, a neutral line position reduces damping when the defect size approaches 5 mm. Conversely, acoustic detectors ignore delamination and reveal lower damping and amplitude at 7 and 10 mm defects. Furthermore, internal sound diffusion from 3 and 5 mm defects enhances air losses and damping. Acoustic detectors only evaluate fault size and position, whereas vibrational detectors may detect local reinforcement and delamination more easily. These results highlight the importance of choosing the right detector according to the location, size, and specific modal characteristics of defects. Full article

Aiming for convenience and the low cost of goods transfer between towns, this paper proposes a trade hub location and allocation method based on a novel artificial eagle-inspired optimization algorithm. Firstly, the trade hub location and allocation model is established, taking the total cost consisting of construction and transportation costs as the objective function. Then, to solve the nonlinear model, a novel artificial eagle optimization algorithm (AEOA) is proposed by simulating the collective migration behaviors of artificial eagles when facing a severe living environment. Three main strategies are designed to help the algorithm effectively explore the decision space: the situational awareness and analysis stage, the free exploration stage, and the flight formation integration stage. In the first stage, artificial eagles are endowed with intelligent thinking, thus generating new positions closer to the optimum by perceiving the current situation and updating their positions. In the free exploration stage, artificial eagles update their positions by drawing on the current optimal position, ensuring more suitable habitats can be found. Meanwhile, inspired by the consciousness of teamwork, a formation flying method based on distance information is introduced in the last stage to improve stability and success rate. Test results from the CEC2022 suite indicate that the AEOA can obtain better solutions for 11 functions out of all 12 functions compared with 8 other popular algorithms. Faster convergence speed and stronger stability of the AEOA are also proved by quantitative analysis. Finally, the trade hub location and allocation method is proposed by combining the optimization model and the AEOA. By solving two typical simulated cases, this method can select suitable hubs with lower construction costs and achieve reasonable allocation between hubs and the rest of the towns to reduce transportation costs. Thus, it is used to solve the trade hub location and allocation problem of Henan province in China to help the government make sound decisions. Full article

Concrete is extensively utilized in infrastructure projects. However, issues like construction quality and external loads can lead to the formation of thin-plate-like voids with considerable aspect ratios, posing serious safety risks and highlighting the need for effective boundary detection. This paper addresses the challenges of traditional acoustic detection methods, which often suffer from low efficiency, poor adaptability to environmental conditions, and difficulties in measuring defect sizes. It explores a spatially diverse MIC Array system. Unlike single-point MIC that can only capture multi-directional sound field information from one excitation point, this array improves efficiency through simultaneous multi-channel data acquisition. This study develops a vibration model for a circular thin plate with fixed boundaries, examines the gradient relationships in various directions, and introduces a method that integrates MIC array technology with acoustic vibration techniques. The focus is on identifying concrete defect boundaries, where a single excitation at the same measurement point can yield different first-order vibration modes recorded by various MICs. A gradient-based approach is proposed to determine defect boundaries based on the locations of different MICs in the array. Experiments were carried out using circular thin-plate concrete samples with pre-existing voids. For instance, at boundary measurement point 15, the first-order modal data collected by MIC0 and MIC4 were $7.80\times {10}^{-4}\mathrm{P}\mathrm{a}$ and $5.42\times {10}^{-6}\mathrm{P}\mathrm{a}$, respectively, exhibiting a significant gradient difference, which verified the accuracy and rapidity of identifying concrete void boundaries. Full article

When monitoring seed positions in soil using ultrasonic waves, the main challenge is obtaining acoustic wave characteristics at the seed locations. This study developed a three-dimensional ultrasonic model with the double media of seed–soil using the discrete element method to visualize signal variations and analyze propagation characteristics. The effects of the compression ratio (0/6/12%), excitation frequency (20/40/60 kHz), and amplitude (5/10/15 μm) on signal variation and attenuation were analyzed. The results show consistent trends: time/frequency domain signal intensity increased with a higher compression ratio and amplitude but decreased with frequency. Comparing ultrasonic signals at soil particles before and after the seed along the propagation path shows that the seed significantly absorbs and attenuates ultrasonic waves. Time domain intensity drops 93.99%, and first and residual wave frequency peaks decrease by 88.06% and 96.39%, respectively. Additionally, comparing ultrasonic propagation velocities in the double media of seed–soil and the single soil medium reveals that the velocity in the seed is significantly higher than that in the soil. At compression ratios of 0%, 6%, and 12%, the sound velocity in the seed is 990.47%, 562.72%, and 431.34% of that in the soil, respectively. These findings help distinguish seed presence and provide a basis for ultrasonic seed position monitoring after sowing. Full article

To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the realizable k-ε and Large Eddy Simulation (LES) turbulence models, the Peng–Robinson equation of state, a broadband noise source model, and the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The effects of pipeline operating pressure (2–10 MPa), leakage hole diameter (1–6 mm), soil type (sandy, loam, and clay), and leakage orientation on the flow field, acoustic source behavior, and sound field distribution were systematically investigated. The results indicate that the leakage hole size and soil medium exert significant influence on both flow dynamics and acoustic propagation, while the pipeline pressure mainly affects the strength of the acoustic source. The leakage direction was found to have only a minor impact on the overall results. The leakage noise is primarily composed of dipole sources arising from gas–solid interactions and quadrupole sources generated by turbulent flow, with the frequency spectrum concentrated in the low-frequency range of 0–500 Hz. This research elucidates the acoustic characteristics of pipeline leakage under various conditions and provides a theoretical foundation for optimal sensor deployment and accurate localization in buried pipeline leak detection systems. 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

This study investigates the impact of microphone placement on honey bee audio monitoring for time-of-day classification, a key step toward automated activity monitoring and anomaly detection. Recognizing the time-dependent nature of bee behavior, we aimed to establish a baseline diurnal pattern recognition method. A custom apparatus enabled simultaneous audio acquisition from internal (brood frame, protected from propolization) and external hive locations. Sound signals were preprocessed using Power Spectral Density (PSD). Extra Trees and Convolutional Neural Network (CNN) classifiers were trained to identify diurnal activity patterns. Analysis focused on feature importance, particularly spectral characteristics. Interestingly, Extra Trees performance varied significantly. While achieving near-perfect accuracy (98–99%) with internal recordings, its accuracy was considerably lower (61–72%) with external recordings, even lower than CNNs trained on the same data (76–87%). Further investigation using Extra Trees and feature selection methods using Mean Decrease Impurity (MDI) and Recursive Feature Elimination with Cross-Validation (RFECV) revealed the importance of the 100–600 Hz band, with peaks around 100 Hz and 300 Hz. These findings inform future monitoring setups, suggesting potential for reduced sampling frequencies and underlining the need for monitoring of sound inside the beehive in order to validate methods being tested. Full article

The increasing integration of renewable energy sources and the rising demand for electricity has intensified concerns over voltage stability in radial distribution systems. These networks are particularly susceptible to voltage collapse under heavy loading conditions, posing serious system reliability and efficiency risks. Integrating distributed generation (DG) has emerged as a strategic solution to strengthen voltage profiles and reduce power losses. To address this challenge, this study proposes a novel distribution voltage stability index (NDVSI) for accurately assessing voltage stability and guiding optimal DG placement and sizing. The NDVSI provides a reliable tool to identify weak buses and their neighboring nodes that critically impact stability. By targeting these locations, the method ensures DG units are installed where they offer maximum improvement in voltage support and minimum power losses. The approach is implemented using MATLAB R2019a (MathWorks Inc., Natick, MA, USA) and validated on three benchmark radial distribution systems, including IEEE 12-bus, 33-bus, and 69-bus systems, demonstrating its scalability and effectiveness across different grid complexities. Comparative analysis with existing voltage stability indices confirms the superiority of NDVSI in both diagnostic precision and practical application. The proposed approach offers a technically sound and economically viable tool for enhancing the reliability, stability, and performance of modern distribution networks. Full article