Search Results (3,726)

This work presents the experimental and numerical investigation of a novel acoustic metamaterial based on sustainable and biodegradable components: cork membranes and honeycomb cores made from treated aramid paper. The design exploits the principle of localized resonance induced by tensioned membranes coupled with subwavelength cavities, aiming to achieve high sound absorption at low (250–500 Hz) and mid frequencies (500–1400 Hz) with minimal thickness and environmental impact. Three configurations were analyzed, varying the number of membranes (one, two, and three) while keeping a constant core structure composed of three stacked honeycomb layers. Acoustic performance was measured using an impedance tube (Kundt’s tube), focusing on the normal-incidence sound absorption coefficient in the frequency range of 250–1400 Hz. The results demonstrate that increasing the number of membranes introduces multiple resonances and broadens the effective absorption bandwidth. Numerical simulations were performed to predict pressure field distributions. The numerical model showed good agreement with the experimental data, validating the underlying physical model of coupled mass–spring resonators. The proposed metamaterial offers a low-cost, modular, and fully recyclable solution for indoor sound control, combining acoustic performance and environmental sustainability. These findings offer promising perspectives for the application of bio-based metamaterials in architecture and eco-design. Further developments will address durability, high-frequency absorption, and integration in hybrid soundproofing systems. Full article

The detection of seabed boulders is a critical step in mitigating geological hazards during the planning and construction of offshore wind energy infrastructure, as well as in supporting benthic ecological and palaeoglaciological studies. Traditionally, side-scan sonar (SSS) has been favoured for such detection, but the growing availability of high-resolution multibeam echosounder (MBES) data offers a cost-effective alternative. This study presents a semi-automated, hybrid GIS-AI approach that combines bathymetric position index filtering and a Random Forest classifier to detect boulders and delineate boulder fields from MBES data. The method was tested on a 0.24 km² site in Long Island Sound using 0.5 m resolution data, achieving 83% recall, 73% precision, and an F1-score of 77—slightly outperforming the average of expert manual picks while offering a substantial improvement in time-efficiency. The workflow was validated against a consensus-based master dataset and applied across a 79 km² study area, identifying over 75,000 contacts and delineating 89 contact clusters. The method enables objective, reproducible, and scalable boulder detection using only MBES data. Its ability to reduce reliance on SSS surveys while maintaining high accuracy and offering workflow customization makes it valuable for geohazard assessment, benthic habitat mapping, and offshore infrastructure planning. Full article

Background/Objectives: Immediate dentin sealing (IDS) has been widely investigated in sound dentin; however, its efficacy on demineralized dentin remains insufficiently explored. This in vitro experimental study aimed to evaluate the shear bond strength (SBS) of indirect composite resin restorations bonded to demineralized dentin using IDS, assessed at 24 h and after 6 months of aging. Methods: Twenty-five extracted premolars were randomly divided into five groups: (1) control (no sealing), (2) IDS applied to sound dentin (sound-IDS), (3) IDS applied to demineralized dentin (carious-IDS), (4) delayed dentin sealing (DDS) on sound dentin (sound-DDS), and (5) DDS on demineralized dentin (carious-DDS). SBS values were analyzed using a three-way analysis of variance (ANOVA) with dentin condition (sound vs. demineralized), aging time (24 h vs. 6 months), and sealing strategy (control, IDS, DDS) as independent variables. Statistical analyses were performed using SigmaPlot 12.0, with significance set at p < 0.05. Results: The results showed that IDS led to significantly higher SBS than DDS (p < 0.05). Bond strength was significantly influenced by dentin condition (p < 0.05), and all interactions between variables—particularly between dentin condition and sealing strategy, and between aging time and treatment—were statistically significant (p < 0.001). Overall, bond strength was higher at 24 h than after 6 months. IDS showed optimal performance in sound dentin, while DDS resulted in better long-term outcomes in demineralized dentin. Conclusions: These findings suggest that DDS may be the more effective approach in cases of carious or demineralized dentin. Full article

Based on a comprehensive review of the current research status of ecological compensation both domestically and internationally, combined with field survey data, this study delves into the issue of multi-stakeholder participation in the ecological compensation mechanisms of the Xin’an River Basin. This research reveals that the joint participation of multiple stakeholders is crucial to achieving the goals of ecological compensation in river basins. The government plays a significant role in macro-guidance, financial support, policy guarantees, supervision, and management. It promotes the comprehensive implementation of ecological environmental protection by formulating relevant laws and regulations, guiding the public to participate in ecological conservation, and supervising and punishing pollution behaviors. The public, serving as the main force, forms strong awareness and behavioral habits of ecological protection through active participation in environmental protection, monitoring, and feedback. As participants, enterprises contribute to industrial transformation and green development by improving resource utilization efficiency, reducing pollution emissions, promoting green industries, and participating in ecological restoration projects. Scientific research institutions, as technology enablers, have effectively enhanced governance efficiency through technological research and innovation, ecosystem value accounting to provide decision-making support, and public education. Social organizations, as facilitators, have injected vitality and innovation into watershed governance by extensively mobilizing social forces and building multi-party collaboration platforms. Communities, as supporters, have transformed ecological value into economic benefits by developing characteristic industries such as eco-agriculture and eco-tourism. Based on the above findings, further recommendations are proposed to mobilize the enthusiasm of upstream communities and encourage their participation in ecological compensation, promote the market-oriented operation of ecological compensation mechanisms, strengthen cross-regional cooperation to establish joint mechanisms, enhance supervision and evaluation, and establish a sound benefit-sharing mechanism. These recommendations provide theoretical support and practical references for ecological compensation worldwide. Full article

In the earthquakes that occurred in the Pazarcık (Mw = 7.7) and Elbistan (Mw = 7.6) districts of Kahramanmaraş Province on 6 February 2023, many buildings collapsed in the Hayrullah neighborhood of the Onikişubat district. In this study, we investigated whether there was a soil amplification effect on the damage occurring in the Hayrullah neighborhood of the Onikişubat district of Kahramanmaraş Province. Firstly, borehole, SPT, MASW (multi-channel surface wave analysis), microtremor, electrical resistivity tomography (ERT), and vertical electrical sounding (VES) tests were carried out in the field to determine the engineering properties and behavior of soil. Laboratory tests were also conducted using samples obtained from bore holes and field tests. Then, an idealized soil profile was created using the laboratory and field test results, and site dynamic soil behavior analyses were performed on the extracted profile. According to The Turkish Building Code (TBC 2018), the earthquake level DD-2 design spectra of the project site were determined and the average design spectrum was created. Considering the seismicity of the project site and TBC (2018) criteria (according to site-specific faulting, distance, and average shear wave velocity), 11 earthquake ground motion sets were selected and harmonized with DD-2 spectra in short, medium, and long periods. Using scaled motions, the soil profile was excited with 22 different earthquake scenarios and the results were obtained for the equivalent and non-linear models. The analysis showed that the soft soil conditions in the area amplified ground shaking by up to 2.8 times, especially for longer periods (1.0–2.5 s). This level of amplification was consistent with the damage observed in mid- to high-rise buildings, highlighting the important role of local site effects in the structural losses seen during the Kahramanmaraş earthquakes. Full article

Background/Objectives: This study aimed to investigate the optimal sound presentation level for sound localization testing to assess the effect of hearing interventions in individuals with unilateral conductive hearing loss (UCHL). Methods: Nine participants with normal hearing were tested, and simulated two-stage UCHL was created using earmuffs and earplugs. We created two types of masking conditions: (1) only an earplug inserted, and (2) an earplug inserted with an earmuff worn. A sound localization test was performed for each condition. The sound presentation levels were 40, 45, 50, 55, 60, 65, and 70 dB SPL, and the results were evaluated using root mean square and d-values. Results: Both values showed little difference in masking Condition 2, regardless of the sound presentation level, whereas in masking Condition 1, the values were at their minimum at 55 dB SPL. In addition, comparing the differences between masking Conditions 1 and 2 for each sound presentation level, the greatest difference was observed at 55 dB SPL for both values. Conclusions: The optimal sound presentation level for sound localization testing to assess hearing intervention effects in UCHL was 55 dB. This result may be attributed to the effect of input from the non-masked ear, accounting for interaural attenuation; the effect was considered minimal at 55 dB SPL. Full article

Recent advancements in large language models (LLMs) have significantly enhanced the effectiveness of Retrieval-Augmented Generation (RAG) systems. This study focuses on the development and evaluation of a domain-specific AI chatbot designed to support international student admissions by leveraging LLM-based RAG pipelines. We implement and compare multiple pipeline configurations, combining retrieval methods (e.g., Dense, MMR, Hybrid), chunking strategies (e.g., Semantic, Recursive), and both open-source and commercial LLMs. Dual evaluation datasets of LLM-generated and human-tagged QA sets are used to measure answer relevancy, faithfulness, context precision, and recall, alongside heuristic NLP metrics. Furthermore, latency analysis across different RAG stages is conducted to assess deployment feasibility in real-world educational environments. Results show that well-optimized open-source RAG pipelines can offer comparable performance to GPT-4o while maintaining scalability and cost-efficiency. These findings suggest that the proposed chatbot system can provide a practical and technically sound solution for international student services in resource-constrained academic institutions. Full article

Background/Objectives: This study investigated the association between cochlear implant (CI) users’ assessed perception of musical sound quality and their subjective music perception and music-related quality of life (QoL). The aim was to provide a comprehensive evaluation by integrating a relatively objective Turkish Multiple Stimulus with Hidden Reference and Anchor (TR-MUSHRA) test and a subjective music questionnaire. Methods: Thirty CI users and thirty normal-hearing (NH) adults were assessed. Perception of sound quality was measured using the TR-MUSHRA test. Subjective assessments were conducted with the Music-Related Quality of Life Questionnaire (MuRQoL). Results: TR-MUSHRA results showed that while NH participants rated all filtered stimuli as perceptually different from the original, CI users provided similar ratings for stimuli with adjacent high-pass filter settings, indicating less differentiation in perceived sound quality. On the MuRQoL, groups differed on the Frequency subscale but not the Importance subscale. Critically, no significant correlation was found between the TR-MUSHRA scores and the MuRQoL subscale scores in either group. Conclusions: The findings demonstrate that TR-MUSHRA is an effective tool for assessing perceived sound quality relatively objectively, but there is no relationship between perceiving sound quality differences and measures of self-reported musical engagement and its importance. Subjective music experience may represent different domains beyond the perception of sound quality. Therefore, successful auditory rehabilitation requires personalized strategies that consider the multifaceted nature of music perception beyond simple perceptual judgments. Full article

Objective: This study aimed to develop a performance evaluation index system for a district-level public hospital in Chongqing, China, based on Diagnosis-Related Groups (DRGs), to provide a benchmark for performance assessment in similar hospitals. The system was constructed using a literature analysis, the Delphi method, and the Analytic Hierarchy Process (AHP) to identify and weight relevant indicators. Results: The evaluation system consists of three primary indicators and eighteen secondary indicators. Key secondary indicators include the Case Mix Index (CMI), cost consumption index, low-risk group mortality rate, the proportion of patients with three- or four-level surgeries at discharge, and the proportion of medical service revenue to medical income. In 2020, significant improvements were observed in several indicators, such as a decrease in the low-risk group mortality rate to 0% and increases in the proportion of patients with three- or four-level surgeries and CMI by nearly 10% and 13%, respectively. Conclusions: This study successfully developed a comprehensive and scientifically sound performance evaluation index system for a district-level public hospital in Chongqing. The system has proven effective in objectively assessing inpatient medical care performance and providing valuable guidance for improving healthcare services in similar settings. Full article

In response to the problem of complex and variable loads on wind turbine gearbox bearing in working conditions, as well as the limited amount of sound data making fault identification difficult, this study focuses on sound signals and proposes an intelligent diagnostic method using deep learning. By adding the CBAM module in ResNeXt to enhance the model’s attention to important features and combining it with the Arcloss loss function to make the model learn more discriminative features, the generalization ability of the model is strengthened. We used a fine-tuning transfer learning strategy, transferring pre-trained model parameters to the CBAM-ResNeXt50-ArcLoss model and training with an extracted Mel spectrogram of sound signals to extract and classify audio features of the wind turbine gearbox. Experimental validation of the proposed method on collected sound signals showed its effectiveness and superiority. Compared to CNN, ResNet50, ResNeXt50, and CBAM-ResNet50 methods, the CBAM-ResNeXt50-ArcLoss model achieved improvements of 13.3, 3.6, 2.4, and 1.3, respectively. Through comparison with classical algorithms, we demonstrated that the research method proposed in this study exhibits better diagnostic capability in classifying wind turbine gearbox sound signals. Full article

Monitoring the health of wind turbine foundations is essential for ensuring their operational safety. This paper presents a cost-effective approach to obtain rotational stiffness of wind turbine foundations by using only acceleration and wind speed data that are part of SCADA data, thus lowering the use of moment and tilt sensors that are currently being used for obtaining foundation stiffness. First, a convolutional neural network model is applied to map acceleration and wind speed data within a moving window to corresponding moment and tilt values. Rotational stiffness of the foundation is then estimated by fitting a line in the moment-tilt plane. The results obtained indicate that such a mapping model can provide stiffness values that are within 7% of ground truth stiffness values on average. Second, the developed mapping model is re-trained by using synthetic acceleration and wind speed data that are generated by an autoencoder generative AI network. The results obtained indicate that although the exact amount of stiffness drop cannot be determined, the drops themselves can be detected. This mapping model can be used not only to lower the cost associated with obtaining foundation rotational stiffness but also to sound an alarm when a foundation starts deteriorating. Full article

This study aims to investigate the effects of phase synchronisation on tonal noise reduction in a multi-rotor UAV using an electronic phase-locking system. Experiments at the University of Bristol explored the impact of relative phase angle, propeller spacing, and blade geometry on acoustic performance, including psychoacoustic annoyance. Results show that increasing the phase angle consistently reduces the sound pressure level (SPL) due to destructive interference. For the two-bladed configuration, the highest noise reduction occurred at relative phase angle $\Delta \psi ={90}^{\circ }$, with a 19 dB decrease at the first blade-passing frequency (BPF). Propeller spacing had minimal impact when phase synchronisation was applied. The pitch-to-diameter ($P/D$) ratio also influenced results: for $P/D=0.55$, reductions ranged from 13–18 dB; and for $P/D=1.0$, reductions ranged from 10–20 dB. Maximum psychoacoustic annoyance was observed when propellers were in phase ($\Delta \psi =0^{\circ }$), while annoyance decreased with increasing phase angle, confirming the effectiveness of phase control for noise mitigation. For the five-bladed configuration, the highest reduction of 15 dB occurred at $\Delta \psi ={36}^{\circ }$, with annoyance levels also decreasing with phase offset. 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

Magnetotelluric (MT) sounding is a crucial technique in mineral exploration. However, MT data are highly susceptible to various types of noise. Traditional data processing methods, which rely on the assumption of signal stationarity, often result in severe distortion when suppressing non-stationary noise. In this study, we propose a novel, adaptive, and less parameter-dependent signal decomposition method for MT signal denoising, based on time–frequency domain analysis and the application of modal decomposition. The method uses Variational Mode Decomposition (VMD) to adaptively decompose the MT signal into several intrinsic mode functions (IMFs), obtaining the instantaneous time–frequency energy distribution of the signal. Subsequently, robust statistical methods are introduced to extract the independent components of each IMF, thereby identifying signal and noise components within the decomposition results. Synthetic data experiments show that our method accurately separates high-amplitude non-stationary interference. Furthermore, it maintains stable decomposition results under various parameter settings, exhibiting strong robustness and low parameter dependency. When applied to field MT data, the method effectively filters out non-stationary noise, leading to significant improvements in both apparent resistivity and phase curves, indicating its practical value in mineral exploration. Full article