Search Results (471)

Obtaining the echo of individual fish is an important prerequisite for fisheries acoustic applications, such as in situ measurement of fish target strength and assessment of fish abundance using the counting method. It is also the foundation for evaluating the growth status of farmed fish and managing aquaculture risks. The density of farmed fish populations is typically higher, and such high-density aquaculture further increases the difficulty of obtaining individual fish echoes in practical applications. Building upon previous research and considering the behavioral characteristics of fish in aquaculture settings, this study conducted performance simulations, live fish experiments in simulated aquaculture cages, and comparative evaluations of three individual fish broadband echo detection methods based on a broadband signal system: the amplitude pulse width method (APM) based on echo envelopes, the peak detection and time delay estimation method (PDM), and the peak time delay combined with instantaneous frequency method (PDIM). This study assumed a dorsolateral fish orientation, which limits its research scope and applicability. The results showed that the PDIM achieved a detection accuracy of 78.34% and a false recognition rate of 1.32%. The APM based on echo envelopes was insensitive to individual fish echoes and had lower recognition accuracy. The PDM exhibited better individual fish echo capture capabilities, while the PDIM demonstrated superior overlapping echo rejection capabilities. Full article

Taconis oscillations represent spontaneous excitation of acoustic modes in tubes with large temperature gradients in cryogenic systems. In this study, Taconis oscillations in hydrogen and helium systems are enhanced with a porous material resulting in a standing-wave thermoacoustic engine. A theoretical model is developed using the thermoacoustic software DeltaEC, version v6.4b2.7, to predict system performance, and an experimental apparatus is constructed for engine characterization. The low-amplitude thermoacoustic model predicts the pressure amplitude, frequency, and temperature gradient required for excitation of the standing-wave system. Experimental measurements, including the onset temperature ratio, acoustic pressure amplitudes, and frequencies, are recorded for different stack materials and geometries. The findings indicate that, independent of stack, hydrogen systems excite at smaller temperature differentials than helium (because of different properties such as lower viscosity for hydrogen), and the stack geometry and material affect the onset temperature ratio. However, pressure amplitude in the excited states varies minimally. Initial measurements are also conducted in a cooling setup with an added regenerator. The configuration with stainless-steel mesh screens produces a small cryogenic refrigeration effect with a decrease in temperature of about 1 K. The reported characterization of a Taconis-based thermoacoustic engine can be useful for the development of novel thermal management systems for cryogenic storage vessels, including refrigeration and pressurization. Full article

With the increasing severity of urban noise pollution, its detrimental impact on public health has garnered growing attention. However, accurate identification and classification of noise sources in complex urban acoustic environments remain major technical challenges for achieving refined noise management. To address this issue, this study presents two key contributions. First, we construct a new urban noise classification dataset, namely the urban noise 15-category dataset (UN15), which consists of 1620 audio clips from 15 representative categories, including traffic, construction, crowd activity, and commercial noise, recorded from diverse real-world urban scenes. Second, we propose a novel deep neural network architecture based on a residual network and integrated with a time–frequency attention mechanism, referred to as residual network with temporal–frequency attention (ResNet-TF). Extensive experiments conducted on the UN15 dataset demonstrate that ResNet-TF outperforms several mainstream baseline models in both classification accuracy and robustness. These results not only verify the effectiveness of the proposed attention mechanism but also establish the UN15 dataset as a valuable benchmark for future research in urban noise classification. Full article

The acoustic environment of dental clinics plays a critical role in shaping patient experience, staff performance, and overall clinical effectiveness. This comprehensive review, supported by systematic search procedures, investigates how soundscapes in dental settings influence psychological, physiological, and operational outcomes. A total of 60 peer-reviewed studies were analyzed across dental, healthcare, architectural, and environmental psychology disciplines. Findings indicate that mechanical noise from dental instruments, ambient reverberation, and inadequate acoustic zoning contribute significantly to patient anxiety and professional fatigue. The review identifies emerging strategies for acoustic optimization, including biophilic and sustainable design principles, sound-masking systems, and adaptive sound environments informed by artificial intelligence. Special attention is given to the integration of lean management and circular economy practices for sustainable dental architecture. A design checklist and practical framework are proposed for use by dental professionals, architects, and healthcare planners. Although limited by the predominance of observational studies and geographic bias in the existing literature, this review offers a comprehensive, interdisciplinary synthesis. It highlights the need for future clinical trials, real-time acoustic assessments, and participatory co-design methods to enhance acoustic quality in dental settings. Overall, the study positions sound design as a foundational element in creating patient-centered, ecologically responsible dental environments. Full article

Optimizing hydrodynamic performance and dissolved oxygen (DO) distribution is essential for improving water quality management in industrial recirculating aquaculture systems. This study combines experimental measurements and data analysis to evaluate the effects of the inlet pipe flow rate (Q), deployment distance ratio (d/r), deployment angle (θ), inlet pipe structure on hydrodynamics and the dissolved oxygen distribution across various tank layers. The flow field distribution in the tanks was measured using Acoustic Doppler Velocimetry (ADV), and the hydrodynamic characteristics, including average velocity (v_avg) and the velocity uniformity coefficient (DU₅₀), were quantitatively analyzed. The dissolved oxygen content at different tank layers was recorded using an Aquameter GPS portable multi-parameter water quality analyzer. The findings indicate that average velocity (v_avg) and the velocity uniformity coefficient (DU₅₀) are key determinants of the hydrodynamic characteristic of circular aquaculture tanks. Optimal hydrodynamic performance occurs for the vertical single-pipe porous configuration at Q = 9 L/s, d/r = 1/4, and θ = 45°,the average velocity reached 0.0669 m/s, and the uniformity coefficients attained a maximum value of 40.4282. In a vertical single-pipe porous structure, the tank exhibits higher dissolved oxygen levels compared to a horizontal single-pipe single-hole structure. Under identical water inflow rates and deployment distance ratios, dissolved oxygen levels in the surface layer of the circular aquaculture tank are significantly greater than that in the bottom layer. The results of this study provide valuable insights for optimizing the engineering design of industrial circular aquaculture tanks and addressing the dissolved oxygen distribution across different water layers. Full article

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article

Termites pose significant threats to the structural integrity of embankments due to their nesting and tunneling behavior, which leads to internal voids, water leakage, or even dam failure. This review systematically classifies and evaluates current termite detection techniques in the context of embankment maintenance, focusing on physical sensing technologies and biological characteristic-based methods. Physical sensing methods enable non-invasive localization of subsurface anomalies, including ground-penetrating radar, acoustic detection, and electrical resistivity imaging. Biological characteristic-based methods, such as electronic noses, sniffer dogs, visual inspection, intelligent monitoring, and UAV-based image analysis, are capable of detecting volatile compounds and surface activity signs associated with termites. The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. The findings suggest that future termite detection in embankments will rely on interdisciplinary integration and intelligent monitoring systems to support early warning, rapid response, and long-term structural resilience. This work provides a scientific foundation and practical reference for advancing termite management and embankment safety strategies. Full article

The increasing accumulation of E-waste presents significant environmental challenges, particularly its disposal and resource management. The present study investigates the potential of printed circuit boards (PCBs) as a partial replacement for fine aggregates in cement mortar and concrete. The replacement levels of PCBs ranged from 0 to 35 wt% in cement mortar and from 0 to 30 wt% in concrete, aiming to improve the qualities of both mixes. The specimens were cured for 7 and 28 days, respectively, followed by tests to evaluate the flowability and static mechanical properties. The performance of the developed mortar/concrete was analyzed under aggressive environmental conditions by conducting various durability tests. Properties such as acoustic and thermal conductivity were also evaluated to check the suitability of the developed material for its multifunctionality. Test results revealed that the optimal replacement percentages of fine aggregate by PCBs in mortar and concrete mixes were 25 wt% and 20 wt%, respectively. A decline in mechanical properties was observed after a further increase in replacement level. The results demonstrate the feasibility of E-waste integration in cement and mortar as a sustainable waste management solution. Full article

Eczema, or atopic dermatitis (AD), is a chronic inflammatory skin condition characterized by persistent itching and scratching, significantly impacting patients’ quality of life. Effective monitoring of scratching behaviour is crucial for assessing disease severity, treatment efficacy, and understanding the relationship between itch and sleep disturbances. This review explores current technological approaches for detecting and monitoring scratching and itching in AD patients, categorising them into contact-based and non-contact-based methods. Contact-based methods primarily involve wearable sensors, such as accelerometers, electromyography (EMG), and piezoelectric sensors, which track limb movements and muscle activity associated with scratching. Non-contact methods include video-based motion tracking, thermal imaging, and acoustic analysis, commonly employed in sleep clinics and controlled environments to assess nocturnal scratching. Furthermore, emerging artificial intelligence (AI)-driven approaches leveraging machine learning for automated scratch detection are discussed. The advantages, limitations, and validation challenges of these technologies, including accuracy, user comfort, data privacy, and real-world applicability, are critically analysed. Finally, we outline future research directions, emphasizing the integration of multimodal monitoring, real-time data analysis, and patient-centric wearable solutions to improve disease management. This review serves as a comprehensive resource for clinicians, researchers, and technology developers seeking to advance objective itch and scratch monitoring in AD patients. Full article

This study aimed to compare the efficiency of four extraction methods, hot water (HW), hot alkaline (HA), ultrasound-assisted water (UW), and ultrasound-assisted alkaline (UA), for extracting crude β-glucan from Lentinula edodes, focusing on yield, functionality, and antidiabetic potential. The response surface methodology was used to optimize extraction conditions. Among all methods, UW yielded the highest β-glucan content (34.51 ± 0.82 g/100 g dry extract), indicating enhanced extraction efficiency through acoustic cavitation. However, HW demonstrated the most preserved structural integrity, exhibiting superior and consistent swelling power across all tested pH conditions, which indicated an excellent water-holding capacity. The ability of HA to scavenge antioxidants was significantly higher than that of other methods, likely due to the enhanced release of phenolic residues under alkaline conditions. UA showed the most potent inhibition against α-amylase (IC₅₀ = 1.46 mg/mL) and α-glucosidase (IC₅₀ = 1.21 mg/mL), demonstrating the potential for type 2 diabetes management. These results highlight that while UW is optimal for yield, HW preserves functional integrity, HA enhances antioxidant properties, and UA is promising for enzyme inhibition. The findings provide insights into tailoring extraction strategies for targeted functional or nutraceutical applications. Full article

Mapping marine habitats is fundamental for biodiversity conservation and ecosystem-based management in oceanic regions under increasing anthropogenic and climatic pressures. In the context of global initiatives—such as marine protected area expansion and international agreements—habitat mapping has become mandatory for regional and global conservation policies. It provides spatial data to delineate essential habitats, support connectivity analyses, and assess pressures, enabling ecosystem-based marine spatial planning aligned with EU directives (2008/56/EC; 2014/89/EU). Beyond biodiversity, macrophytes, rhodolith beds, and coral reefs deliver key ecosystem services—carbon sequestration, coastal protection, nursery functions, and fisheries support—essential to local socioeconomies. This systematic review (PRISMA guidelines) examined 69 peer-reviewed studies across Central-Eastern Atlantic archipelagos (Macaronesia: the Azores, Madeira, the Canaries, and Cabo Verde) and the Mid-Atlantic Ridge. We identified knowledge gaps, methodological trends, and key challenges, emphasizing the integration of cartographic, ecological, and technological approaches. Although methodologies diversified over time, the lack of survey standardization, limited ground truthing, and heterogeneous datasets constrained the production of high-resolution bionomic maps. Regional disparities persist in technology access and habitat coverage. The Azores showed the highest species richness (393), dominated by acoustic mapping in corals. Madeira was most advanced in the remote mapping of rhodoliths; the Canaries focused on shallow macrophytes with direct mapping; and Cabo Verde remains underrepresented. Harmonized protocols and regional cooperation are needed to improve data interoperability and predictive modeling. Full article

Monumental trees, defined by their exceptional size, form, and age, are critical components of both cultural heritage and ecological systems. However, their conservation faces increasing threats from habitat fragmentation, climate change, and inadequate public policies. This review synthesized global research on monumental trees by analyzing 204 peer-reviewed articles published between 1989 and 2024 that were sourced from Web of Science and Scopus. Our bibliometric analysis highlighted Olea europaea and Castanea sativa as the most frequently studied species and identified a surge in publications after 2019, particularly from the USA, Italy, and Spain. Key research themes included conservation, biodiversity, and ecosystem services. The methodological approaches varied globally, encompassing ranking systems; GIS mapping; remote sensing; and non-invasive diagnostic tools, such as acoustic tomography and chlorophyll fluorescence. Conservation strategies discussed included vegetative propagation, cryopreservation, and legal risk management. Despite advances in these techniques, significant gaps remain in effectively addressing environmental pressures and integrating multidisciplinary approaches. We concluded that targeted, interdisciplinary strategies are essential to safeguard monumental trees as vital ecological and cultural landmarks. Full article

Diagnosing Parkinson’s disease (PD) through speech analysis is a promising area of research, as speech impairments are often one of the early signs of the disease. This study investigates the efficacy of fine-tuning pre-trained Automatic Speech Recognition (ASR) models, specifically Wav2Vec 2.0 and HuBERT, for PD detection using transfer learning. These models, pre-trained on large unlabeled datasets, can be capable of learning rich speech representations that capture acoustic markers of PD. The study also proposes the integration of a supervised contrastive (SupCon) learning approach to enhance the models’ ability to distinguish PD-specific features. Additionally, the proposed ASR-based features were compared against two common acoustic feature sets: mel-frequency cepstral coefficients (MFCCs) and the extended Geneva minimalistic acoustic parameter set (eGeMAPS) as a baseline. We also employed a gradient-based method, Grad-CAM, to visualize important speech regions contributing to the models’ predictions. The experiments, conducted using the NeuroVoz dataset, demonstrated that features extracted from the pre-trained ASR models exhibited superior performance compared to the baseline features. The results also reveal that the method integrating SupCon consistently outperforms traditional cross-entropy (CE)-based models. Wav2Vec 2.0 and HuBERT with SupCon achieved the highest F1 scores of 90.0% and 88.99%, respectively. Additionally, their AUC scores in the ROC analysis surpassed those of the CE models, which had comparatively lower AUCs, ranging from 0.84 to 0.89. These results highlight the potential of ASR-based models as scalable, non-invasive tools for diagnosing and monitoring PD, offering a promising avenue for the early detection and management of this debilitating condition. Full article

Both the excess and alteration of bed sediments in river systems can cause socioeconomic and environmental damage; thus, the quantification of bedload transport is an important tool to assess the health of rivers and help in decision-making imposed by the agencies responsible for water resource management. This work aims to evaluate the efficiency of pressure-difference samplers (Helley–Smith) qualitatively and quantitatively when used in environments with sandy characteristics. The experiments were carried out in a stream with full transparency and two pressure-difference samplers with nozzle dimensions of 7.20 × 7.20 cm and 8.89 × 7.50 cm. The Particle Image Velocimetry technique was used to analyze the sampler efficiency simultaneously with an Acoustic Doppler Current Profiler. Qualitative results showed that the way the equipment is allocated at the bottom of the river can generate overestimated or underestimated sediment transport measurements. Additionally, evaluating it quantitatively, we see that the collection efficiency of the equipment varied between 15.45% and 534.78% when compared to the results obtained by the Particle Image Velocimetry technique. Full article

The complex life history and stock structure of endangered shortnose sturgeon (Acipenser brevirostrum) may hinder recovery efforts for individually managed river populations in the US. Reliable survival estimates are essential for evaluating population trends and guiding conservation amid ongoing and emergent threats. However, such estimates are scarce in the recent literature and available for only a few wild populations, with their usefulness in practical management limited. In this study, we leverage multi-year acoustic telemetry data from monitoring projects in the Hudson River, New York (2012–2015), and Altamaha River, Georgia (2011–2014), to develop and compare survival estimates for spawning populations at opposite ends of the species’ US geographic range. Bayesian multistate capture–recapture models indicated high and precise apparent monthly adult survival in both the Hudson (0.991; 95% Bayesian credibility interval [CI]: 0.984–0.996) and Altamaha (0.980; 95% CI: 0.969–0.989) rivers, with implied annual survival rates of 0.897 and 0.787, respectively. Overall, this study advances our understanding of clinal variation in key demographic parameters and underscores the need to develop regionally specific goals for recovery. Broadening the estimates through increased telemetry coverage and integration of additional data will strengthen recovery efforts and support the long-term persistence of shortnose sturgeon across their range. Full article