Search Results (1,466)

The alkali–silica reaction (ASR) is an important mechanism of concrete deterioration, whereby reactive silica in aggregate interacts with cement alkalis to form expanding gel, which compromises the structural integrity of the concrete. Although the Republic of Korea has historically been classified as a low-risk region for ASR due to its geological stability, documented examples of concrete damage since the late 1990s have necessitated a rigorous reassessment of local aggregates. This study evaluated the ASR potential of 84 aggregate samples sourced from diverse Korean geological regions using standardized protocols, including ASTM C 1260 for mortar bar expansion and ASTM C 289 for chemical reactivity, supplemented by soundness, acid drainage, and weathering index analyses. The results indicate expansion within the range of 0.1–0.2%, classified as potentially deleterious, for some rock types. In addition to ASR reactivity, isolated high anomalies (e.g., high soundness, acid producing, and weathering) suggest the existence of other durability risks. Consequently, while Korean aggregates predominantly have a low ASR reactivity, the adoption of various validated ASR tests as a routine test and the integration of supplementary cementitious materials are recommended to ensure long-term concrete durability, highlighting the need for sustained monitoring and further investigation into mitigation strategies. 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

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

In recent decades, environmental requirements for reducing the toxic components emitted from vehicle exhausts have decreased drastically. Technologies for after-treatment of diesel vehicle emissions are being improved continuously in order to meet increasingly stringent regulations. Passenger cars are a significant source of air pollution, especially in urban areas. The EU has decided to phase out internal combustion engines. Stricter Real Driving Emissions (RDE) testing procedures have also been introduced, aiming to assess the emissions of nitrogen oxides (NO_x) and particle number (PN). The present work investigates the interaction between performance and smoke emissions of a diesel vehicle on a pre-established route in an urban environment with an everyday (normal) driving style. The results showed that when the vehicle is technically sound and meets its technical specifications, smoke emissions are within normal limits. Full article

Blind source separation (BSS) of underwater acoustic mixed signals aims to improve signal clarity by separating noise components from aliased underwater signal sources. This enhancement directly increases target detection accuracy in underwater acoustic perception systems, particularly in scenarios involving multi-vessel interference or biological sound coexistence. Deep learning-based BSS methods have gained wide attention for their superior nonlinear modeling capabilities. However, existing approaches in underwater acoustic scenarios still face two key challenges: limited feature discrimination and inadequate robustness against non-stationary noise. To overcome these limitations, we propose a novel Robust Cross-Band Network (RCBNet) for the BSS of underwater acoustic mixed signals. To address insufficient feature discrimination, we decompose mixed signals into sub-bands aligned with ship noise harmonics. For intra-band modeling, we apply a parallel gating mechanism that strengthens long-range dependency learning so as to enhance robustness against non-stationary noise. For inter-band modeling, we design a bidirectional-frequency RNN to capture the global dependency relationships of the same signal across sub-bands. Our experiment demonstrates that RCBNet achieves a 0.779 dB improvement in the SDR compared to the advanced model. Additionally, the anti-noise experiment demonstrates that RCBNet exhibits satisfactory robustness across varying noise environments. Full article

Respiratory diseases present significant global health challenges. Recent advances in respiratory sound analysis (RSA) have shown great potential for automated disease diagnosis and patient management. The International Conference on Biomedical and Health Informatics 2017 (ICBHI2017) database stands as one of the most authoritative open-access RSA datasets. This review systematically examines 135 technical publications utilizing the database, and a comprehensive and timely summary of RSA methodologies is offered for researchers and practitioners in this field. Specifically, this review covers signal processing techniques including data resampling, augmentation, normalization, and filtering; feature extraction approaches spanning time-domain, frequency-domain, joint time–frequency analysis, and deep feature representation from pre-trained models; and classification methods for adventitious sound (AS) categorization and pathological state (PS) recognition. Current achievements for AS and PS classification are summarized across studies using official and custom data splits. Despite promising technique advancements, several challenges remain unresolved. These include a severe class imbalance in the dataset, limited exploration of advanced data augmentation techniques and foundation models, a lack of model interpretability, and insufficient generalization studies across clinical settings. Future directions involve multi-modal data fusion, the development of standardized processing workflows, interpretable artificial intelligence, and integration with broader clinical data sources to enhance diagnostic performance and clinical applicability. Full article

Sound event localization and detection (SELD) is a fundamental task in spatial audio processing that involves identifying both the type and location of sound events in acoustic scenes. Current SELD models often struggle with low signal-to-noise ratios (SNRs) and high reverberation. This article addresses SELD by reformulating direction of arrival (DOA) estimation as a multi-class classification task, leveraging deep convolutional recurrent neural networks (CRNNs). We propose and evaluate two modified architectures: M-DOAnet, an optimized version of DOAnet for localization and tracking, and M-SELDnet, a modified version of SELDnet, which has been designed for joint SELD. Both modified models were rigorously evaluated on the STARSS23 dataset, which comprises 13-class, real-world indoor scenes totaling over 7 h of audio, using spectrograms and acoustic intensity maps from first-order Ambisonics (FOA) signals. M-DOAnet achieved exceptional localization (6.00° DOA error, 72.8% F1-score) and perfect tracking (100% MOTA with zero identity switches). It also demonstrated high computational efficiency, training in 4.5 h (164 s/epoch). In contrast, M-SELDnet delivered strong overall SELD performance (0.32 rad DOA error, 0.75 F1-score, 0.38 error rate, 0.20 SELD score), but with significantly higher resource demands, training in 45 h (1620 s/epoch). Our findings underscore a clear trade-off between model specialization and multifunctionality, providing practical insights for designing SELD systems in real-time and computationally constrained environments. Full article

The Flexible Combined Imager (FCI), developed as the next-generation imager for the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellite series, represents a significant advancement over its predecessor, SEVIRI, on the Meteosat Second Generation (MSG) satellites. FCI offers more spectral bands, higher spatial resolution, and faster imaging capabilities, supporting a wide range of applications in weather forecasting, climate monitoring, and environmental analysis. On 13 January 2024, the FCI onboard MTG-I1 (renamed Meteosat-12 in December 2024) experienced a critical anomaly involving the failure of its onboard Calibration and Obturation Mechanism (COM). As a result, the use of the COM was discontinued to preserve operational safety, leaving the instrument dependent on alternative calibration methods. This loss of onboard calibration presents immediate challenges, particularly for the infrared channels, including image artifacts (e.g., striping), reduced radiometric accuracy, and diminished stability. To address these issues, EUMETSAT implemented an external calibration approach leveraging algorithms from the Global Space-based Inter-Calibration System (GSICS). The inter-calibration algorithm transfers stable and accurate calibration from the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral instrument aboard Metop-B and Metop-C satellites to FCI’s infrared channels daily, ensuring continued data quality. Comparisons with Cross-track Infrared Sounder (CrIS) data from NOAA-20 and NOAA-21 satellites using a similar algorithm is then used to validate the radiometric performance of the calibration. This confirms that the external calibration method effectively compensates for the absence of onboard blackbody calibration for the infrared channels. For the visible and near-infrared channels, slower degradation rates and pre-anomaly calibration ensure continued accuracy, with vicarious calibration expected to become the primary source. This adaptive calibration strategy introduces a novel paradigm for in-flight calibration of geostationary instruments and offers valuable insights for satellite missions lacking onboard calibration devices. This paper details the COM anomaly, the external calibration process, and the broader implications for future geostationary satellite missions. Full article

The quality of materials extracted from plant sources, such as oilseeds, is significantly affected by the extraction techniques employed. Thermo-photosensitive bioactive compounds are especially susceptible, often resulting in a loss of functional properties during conventional processing. In this context, studies involving unconventional or “innovative” extraction methods have emerged as a strategic approach to preserve the quality of the extracted material (whether by-product or biomass) by aligning with the core principles of green chemistry and the expansion of sustainable production chains. This approach promotes both raw material integrity and the protection of human and environmental health. These efforts contribute to a virtuous cycle of technological innovation and environmentally sound practices. This review focuses on how ultrasound-assisted extraction affects the quality of plant-derived materials, particularly Brazil nut oil. The article compiles data published over the last five years (2020–2025), following the PRISMA methodology. Recent studies highlight the synergistic potential of ultrasound as a green technology for isolating Brazil nut oil, offering enhanced nutritional and functional properties. This aligns with the growing demand for healthier food products obtained through sustainable industrial processes and presents opportunities for diverse applications across several industry sectors. Full article

Cardiovascular diseases remain one of the leading causes of mortality worldwide, highlighting the importance of effective monitoring and early diagnosis. While electrocardiography (ECG) is the standard technique for evaluating the heart’s electrical activity and detecting rhythm and conduction abnormalities, it alone is insufficient for identifying certain conditions, such as valvular disorders. Phonocardiography (PCG) allows the recording and analysis of heart sounds and improves the diagnostic accuracy when combined with ECG. In this study, ECG and PCG signals were simultaneously acquired from a resting adult subject using a compact system comprising an analog front-end (model AD8232, manufactured by Analog Devices, Wilmington, MA, USA) for ECG acquisition and a digital stethoscope built around a condenser electret microphone (model HM-9250, manufactured by HMYL, Anqing, China). Both the ECG electrodes and the microphone were positioned on the chest to ensure the spatial alignment of the signals. An adaptive segmentation algorithm was developed to segment PCG and ECG signals based on their morphological and temporal features. This algorithm identifies the onset and peaks of S1 and S2 heart sounds in the PCG and the Q, R, and S waves in the ECG, enabling the extraction of the systolic time intervals such as EMAT, PEP, LVET, and LVST parameters proven useful in the diagnosis and monitoring of cardiovascular diseases. Based on the segmented signals, the measured averages (EMAT = 74.35 ms, PEP = 89.00 ms, LVET = 244.39 ms, LVST = 258.60 ms) were consistent with the reference standards, demonstrating the reliability of the developed method. The proposed algorithm was validated on synchronized ECG and PCG signals from multiple subjects in an open-source dataset (BSSLAB Localized ECG Data). The systolic intervals extracted using the proposed method closely matched the literature values, confirming the robustness across different recording conditions; in detail, the mean Q–S1 interval was 40.45 ms (≈45 ms reference value, mean difference: −4.85 ms, LoA: −3.42 ms and −6.09 ms) and the R–S1 interval was 14.09 ms (≈15 ms reference value, mean difference: −1.2 ms, LoA: −0.55 ms and −1.85 ms). In conclusion, the results demonstrate the potential of the joint ECG and PCG analysis to improve the long-term monitoring of cardiovascular diseases. Full article

As research on national cultural parks advances, the significance of conducting multi-dimensional perception evaluations of their cultural ecosystem services (CESs) becomes increasingly apparent. This study examines the eight dimensions of CESs within the Grand Canal National Cultural Park from the perspective of soundscape preference. Using Tongzhou Grand Canal Forest Park as a case study, five categories of soundscapes comprising 19 sound sources were identified through the analysis of online textual data. This study then collected public preferences and perceptions of these five soundscapes via on-site questionnaires and analyzed the data using SPSS26 for correlation and IPA analyses. The results indicate that the overall evaluation of the park’s CESs is positive. There is a significant mutual influence between soundscape preference and CES perception. Specifically, the preference for natural soundscape significantly impacts the evaluation of each CES dimension, while satisfaction with leisure and entertainment is positively correlated with preferences for all types of soundscapes. Additionally, there are notable differences in soundscape preference among different age groups. These findings not only enhance our understanding of soundscape planning in national cultural parks but also provide valuable guidance for their management and design. Full article

The sound speed profile (SSP) is an important feature in the field of ocean acoustics. The accurate estimation of SSP is significant for the development of underwater position, communication, and associated fundamental marine research. The Res-SACNN model is proposed for SSP inversion based on the convolutional neural network (CNN) embedded with the residual network and self-attention mechanism. It combines the spatiotemporal characteristics of sea level anomaly (SLA) and sea surface temperature anomaly (SSTA) data and establishes a nonlinear relationship between satellite remote sensing data and sound speed field by deep learning. The single empirical orthogonal function regression (sEOF-r) method is used in a comparative experiment to confirm the model’s performance in both the time domain and the region. Experimental results demonstrate that the proposed model outperforms sEOF-r regarding both spatiotemporal generalization ability and inversion accuracy. The average root mean square error (RMSE) is decreased by 0.92 m/s in the time-domain experiment in the South China Sea, and the inversion results for each month are more consistent. The optimization ratio hits 71.8% and the average RMSE decreases by 7.39 m/s in the six-region experiment. The Res-SACNN model not only shows more superior inversion ability in the comparison with other deep-learning models, but also achieves strong generalization and real-time performance while maintaining low complexity, providing an improved technical tool for SSP estimation and sound field perception. Full article

Sound source localization represents one of the major challenges for soundscapes due to the dynamicity of a large variety of signals. Many applications are found related to ecosystems to study the migration process of birds and animals other than other terrestrial environments to survey wildlife. Other applications on sound recording are supported by sensors to detect animal movement. This paper deals with the immersive 3D soundscape by using a multi-channel spherical microphone probe, in combination with a 360° camera. The soundscape has been carried out in three Italian squares across the city of Parma. The acoustic maps obtained from the data processing detect the directivity of dynamic sound sources as typical of an urban environment. The analysis of the objective environmental parameters (like loudness, roughness, sharpness, and prominence) was conducted alongside the investigations on the historical importance of Italian squares as places for social inclusivity. A dedicated listening playback is provided by the AGORA project with a portable listening room characterized by modular unit of soundbars. Full article

The measurement error of sound travel time, one of the most critical parameters in acoustic temperature measurement, is significantly affected by the type of sound source signal. In order to select more appropriate sound source signals, a sound source signal preference study of loose coal acoustic temperature measurement was performed and is described herein. The results showed that the absolute error of the swept signal and the pseudo-random signal both increased with increased acoustic wave propagation distance. The relative error of the swept signal showed a relatively stable upward trend; in comparison, the pseudo-random signal showed a general decrease with a large fluctuation in the middle section, and both the relative and absolute errors for the pseudo-random signal were larger than those of the swept signal. Therefore, the swept signal is expected to perform better than the pseudo-random signal in the loose coal medium. Based on the experimental results, the linear sweep signal was selected as the sound source signal for the loose coal temperature inversion experiments: the average error between the inverted temperature value and the actual value was 4.86%, the maximum temperature difference was 2.926 °C, and the average temperature difference was 1.5949 °C. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article