Search Results (97)

This study investigates the generation and suppression of the whistling noise caused by flow through the narrow gap of a vehicle’s side mirror, an aerodynamic phenomenon often reported as a source of discomfort to passengers. The research employs a simultaneous approach, combining wind tunnel experiments to determine the geometries and wind conditions at a flow speed of 22 m/s contributing to whistle generation at between 7 kHz and 8 kHz with numerical simulations utilizing compressible Large Eddy Simulation (LES) techniques for an in-depth investigation of the underlying aerodynamics. The Simplified Side-mirror Model (SSM) is developed, enabling precise wind visualization, and facilitating the identification of fundamental aerodynamic sound sources via vortex sound theory. The analysis reveals that the whistling sound is intricately linked to edge tone phenomena, driven by vortex shedding and flow instabilities at the angled shape in a narrow gap. Building on these insights, the study introduces the Suppressed Whistle Model (SWM), a configuration including shapes resembling a vortex generator that successfully mitigates the whistling by disrupting the identified flow structures causing the whistling sound. The suggested design is validated through wind visualization, comparing the numerical flow structures with the experimental ones. The experimental whistling sound pressure level of SWM decreases by about 20 dB compared to SSM, and a similar trend can be confirmed in the numerical results. Full article

The Strait of Gibraltar is a major biogeographic bottleneck connecting the Atlantic Ocean and the Mediterranean Sea, where migratory cetaceans coexist with an intense maritime traffic. To evaluate the feasibility of broadband passive acoustic monitoring (PAM) for both soundscape characterisation and cetacean detection, a short drifting-buoy experiment was conducted near Barbate, Spain, in May 2025. The system, equipped with a calibrated SoundTrap 400 recorder, continuously sampled the underwater acoustic environment for 2.5 h. Analysis of the recordings revealed vocalisations of Orcinus orca, representing the first preliminary and incomplete description of the Iberian killer whale acoustic repertoire, and numerous transient tonal events with energy peaks between 40 and 50 Hz, consistent with baleen whale sounds previously attributed to foraging fin whales (Balaenoptera physalus). Sperm whale clicks and delphinid whistles were also occasionally detected. The power spectral density analysis further showed a persistent anthropogenic component dominated by vessel noise below 200 Hz and narrow-band echosounder signals at 30 and 50 kHz. These findings confirm the potential of PAM to detect multiple cetacean species and to resolve the complex interplay between biophony and anthropophony in one of the world’s busiest marine corridors. Establishing a permanent PAM observatory in the Strait would enable continuous, non-intrusive monitoring of species presence, behaviour, and habitat use, thereby contributing to conservation efforts for endangered populations such as the Iberian killer whale. Full article

Underwater passive acoustic monitoring (PAM) serves as a core approach pervasively applied to the long-term, non-invasive detection of biological acoustic signals. Dolphin whistles serve as a fundamental aspect of vocal communication, exhibiting intricate frequency-modulated structures. Robust detection of these whistles is essential for dolphin species diversity conservation, yet performance is frequently compromised by underwater background noise, leading to significant degradation in detection reliability. To address this issue, this paper presents an unsupervised enhancement method based on Dually-Regularized Non-Negative Matrix Factorization (DR-NMF). Beyond a standard data fidelity term, the proposed framework integrates two specialized regularizers, including Overlapping Group Shrinkage and Group Lasso. The former promotes time–frequency continuity of whistle ridges, while the latter adaptively eliminates redundant bases, achieving an improved trade-off between structural integrity and noise suppression. The optimization procedure employed a combination of majorization–minimization, iteratively reweighted least squares, and proximal gradient techniques, all of which were implemented within an alternating minimization scheme featuring nested inner–outer iterations. This architecture ensures stable convergence and computational practicality. Extensive experimental evaluations under diverse low signal-to-noise ratio (SNR) conditions reveal that the proposed method achieves a substantial improvement in recall without compromising precision, resulting in consistent enhancements in frame-level $F_1$-scores. When applied to real-world dolphin whistle recordings, our method outperforms existing baseline approaches, demonstrating remarkable robustness in detecting whistle signals when amidst challenging marine environmental noise. Full article

Signature whistles (SW) are a significant element of the vocal repertoire of delphinid species. They encode self-identifying information and allow social cohesion between related and allied individuals through often complex, stereotyped whistle contours. SW recordings allow researchers to explore the presence of individual animals, understand social dynamics, and estimate population size without visual observation. This represents the first study aiming to match distinct SW contours to individual Burrunan dolphins (Tursiops australis) in the Gippsland Lakes, Victoria, Australia. Simultaneous photographic identification and in situ hand-held hydrophone recordings collected in 2021–2024 contained 57 individual dolphins and 236 whistles, resulting in 22 unique SW contours being extracted following modified SIGnature IDentification (SIGID) criteria. Conditional probability analyses revealed that most SW contours were associated with clusters or pairs of dolphins, rather than individuals, reflecting the species’ fission–fusion social structure. Cluster analysis supported these associations, highlighting the difficulty in isolating SWs in a population where individuals are not observed in isolation. Additionally, social structure, philopatric behaviour, and the acoustic environment of transient individuals are suggested to influence signature whistle production and complexity. Although direct signature whistle-to-individual matching remains limited, the study demonstrates that population-level and subgroup-level monitoring by passive acoustic methods is achievable. This approach provides a critical acoustic tool for conservation management of the small, threatened Gippsland Lakes Burrunan dolphin population and offers a foundation for future study of individuals through passive acoustic monitoring. Full article

Cantonese automatic speech recognition (ASR) faces persistent challenges due to its nine lexical tones, extensive phonological variation, and the scarcity of professionally transcribed corpora. To address these issues, we propose a lightweight and data-efficient framework that leverages weak phonetic supervision (WPS) in conjunction with two pho-neme-aware augmentation strategies. (1) Dynamic Boundary-Aligned Phoneme Dropout progressively removes entire IPA segments according to a curriculum schedule, simulating real-world phenomena such as elision, lenition, and tonal drift while ensuring training stability. (2) Phoneme-Aware SpecAugment confines all time- and frequency-masking operations within phoneme boundaries and prioritizes high-attention regions, thereby preserving intra-phonemic contours and formant integrity. Built on the Whistle encoder—which integrates a Conformer backbone, Connectionist Temporal Classification–Conditional Random Field (CTC-CRF) alignment, and a multi-lingual phonetic space—the approach requires only a grapheme-to-phoneme lexicon and Montreal Forced Aligner outputs, without any additional manual labeling. Experiments on the Cantonese subset of Common Voice demonstrate consistent gains: Dynamic Dropout alone reduces phoneme error rate (PER) from 17.8% to 16.7% with 50 h of speech and 16.4% to 15.1% with 100 h, while the combination of the two augmentations further lowers PER to 15.9%/14.4%. These results confirm that structure-aware phoneme-level perturbations provide an effective and low-cost solution for building robust Cantonese ASR systems under low-resource conditions. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

Interior-mounted permanent magnet (IPM) machines have been widely used in recent years due to their high efficiency, high torque/power densities, and so on. These machines can produce reluctance torque whereas their surface-mounted (SPM) counterparts cannot. Hence, IPMs are attractive in industrial applications that require high torque density. ${\mathrm{I}}_{\mathrm{d}}=0$ control is commonly adopted to drive permanent magnet (PM) machines, and the strategy is attractive due to its simplicity. However, although it is suitable for SPMs, adopting it in IPMs sacrifices the reluctance torque that can be obtained from the machine. Hence, it is vital to control IPMs using the maximum torque per ampere (MTPA) strategy. This paper adopts the MTPA strategy for a 4.1 kW prototype IPM machine. Test system configuration is discussed step by step by paying particular attention to potential practical issues and inspirational discussions on their solutions. The issues associated with misaligned rotor positions or whistling problems pertinent to inappropriate power conversion strategies are addressed to overcome such issues in practical IPM drives. Comprehensive discussions and extensive comparisons of well-matched simulation and experimental results of both ${\mathrm{I}}_{\mathrm{d}}=0$- and MTPA-controlled drives at different evaluation metrics will be quite insightful to achieve efficiency-optimized IPM drives. Full article

This study introduces an algorithm for quantifying oceanic wind speed and identifying sound sources in the local underwater soundscape. Utilizing low-complexity metrics like one-minute spectral kurtosis and power spectral density levels, the algorithm categorizes different soundscapes and estimates wind speed. It detects rain, vessels, fin and blue whales, as well as clicks and whistles from dolphins. Positioned as a foundational tool for implementing the Ocean Sound Essential Ocean Variable (EOV), it contributes to understanding long-term trends in climate change for sustainable ocean health and predicting threats through forecasts. The proposed soundscape classification algorithm, validated using extensive acoustic recordings (≥32 kHz) collected at various depths and latitudes, demonstrates high performance, achieving an average precision of $89\%$ and an average recall of $86.59\%$ through optimized parameter tuning via a genetic algorithm. Here, wind speed is determined using a cubic function with power spectral density (PSD) at 6 kHz and the MASLUW method, exhibiting strong agreement with satellite data below 15 m/s. Designed for compatibility with low-power electronics, the algorithm can be applied to both archival datasets and real-time data streams. It provides a straightforward metric for ocean monitoring and sound source identification. Full article

There are few studies on the extent to which waterfowl select plant food compared with what is available in wetland ecosystems. We used a new dataset on the presence of seeds in the alimentary canal or feces to identify flowering plant species whose seeds are ingested by North American ducks or geese. These data are a proxy for dispersal interactions because an important fraction of ingested seeds survives gut passage and is dispersed by endozoochory. We compared the plant traits of species whose seeds were ingested with those of species on the U.S. Department of Agriculture National Wetland Plants List (NWPL). Using a global dataset on plant form and function and chi-squared tests, we compared four categorical traits (moisture requirements, growth form, plant height, and seed mass) between species whose seeds are ingested by North American ducks and geese with the NWPL. Our analyses identified significant differences between the trait distributions of plants whose seeds were ingested by waterfowl guilds and those of the NWPL. Geese and ducks (except whistling ducks) ingested more aquatic and semiaquatic plant species than expected from the NWPL. All guilds except sea ducks ingested more herbaceous graminoids and fewer shrubs or trees than expected. Diving ducks interacted with fewer of the taller plants (>5 m) than expected, but otherwise plant height distributions did not differ from those expected. All waterfowl guilds ingested more species of intermediate seed size (1–10 mg) and fewer species of the smallest (<0.1 mg) or largest (>100 mg) size categories than expected. These results help to explain the role of the long-distance dispersal of seeds by migratory waterfowl in plant biogeography and how plant distributions are likely to respond to global change. Full article

The escalating conflict between cetaceans and fisheries underscores the need for efficient mitigation strategies that balance conservation priorities with economic viability. This study presents a TinyML-driven approach deploying an optimized Convolutional Neural Network (CNN) on a Raspberry Pi Zero 2 W for real-time detection of bottlenose dolphin whistles, leveraging spectrogram analysis to address acoustic monitoring challenges. Specifically, a CNN model previously developed for classifying dolphins’ vocalizations and originally implemented with TensorFlow was converted to TensorFlow Lite (TFLite) with architectural optimizations, reducing the model size by 76%. Both TensorFlow and TFLite models were trained on 22 h of underwater recordings taken in controlled environments and processed into 0.8 s spectrogram segments (300 × 150 pixels). Despite reducing model size, TFLite models maintained the same accuracy as the original TensorFlow model (87.8% vs. 87.0%). Throughput and latency were evaluated by varying the thread allocation (1–8 threads), revealing the best performance at 4 threads (quad-core alignment), achieving an inference latency of 120 ms and sustained throughput of 8 spectrograms/second. The system demonstrated robustness in 120 h of continuous stress tests without failure, underscoring its reliability in marine environments. This work achieved a critical balance between computational efficiency and detection fidelity (F1-score: 86.9%) by leveraging quantized, multithreaded inference. These advancements enable low-cost devices for real-time cetacean presence detection, offering transformative potential for bycatch reduction and adaptive deterrence systems. This study bridges artificial intelligence innovation with ecological stewardship, providing a scalable framework for deploying machine learning in resource-constrained settings while addressing urgent conservation challenges. Full article

Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous data. This study presents a novel approach for classifying dolphin vocalizations from a PAM acoustic recording using a convolutional neural network (CNN). Four types of common bottlenose dolphin (Tursiops truncatus) vocalizations were identified from underwater recordings: whistles, echolocation clicks, burst pulse sounds, and feeding buzzes. To enhance classification performances, edge-detection filters were applied to spectrograms, with the aim of removing unwanted noise components. A dataset of nearly 10,000 spectrograms was used to train and test the CNN through a 10-fold cross-validation procedure. The results showed that the CNN achieved an average accuracy of 95.2% and an F1-score of 87.8%. The class-specific results showed a high accuracy for whistles (97.9%), followed by echolocation clicks (94.5%), feeding buzzes (94.0%), and burst pulse sounds (92.3%). The highest F1-score was obtained for whistles, exceeding 95%, while the other three vocalization typologies maintained an F1-score above 80%. This method provides a promising step toward improving the passive acoustic monitoring of dolphins, contributing to both species conservation and the mitigation of conflicts with fisheries. Full article

Anseriformes represent a basal order in the phylogeny of neognath birds and are of particular interest in cytogenetic research due to their distinctive chromosomal features. However, aspects of their chromosomal evolution, such as the distribution and organization of microsatellite sequences, remain poorly understood. Given the role of these dynamic repetitive sequences in chromosome organization, differentiation, and evolution, we analyzed microsatellite distribution in three Anatidae species, each representing a different subfamily: Amazonetta brasiliensis-Brazilian Teal (Anatinae), Coscoroba coscoroba-Coscoroba Swan (Anserinae), and Dendrocygna viduata-White-faced Whistling Duck (Dendrocygninae). This is the first karyotypic description for White-faced Whistling Duck (2n = 78) and Brazilian Teal (2n = 80), whereas Coscoroba Swan, previously analyzed, exhibits a notably high diploid number (2n = 98). Despite sharing a similar macrochromosome morphology, the three showed differences in diploid numbers and microsatellite distribution. Extensive microsatellite accumulation was found in both autosomal and sex chromosomes (Z and W) of Brazilian Teal and Coscoroba Swan, while White-faced Whistling Duck displays minimal hybridization signals and an absence of microsatellites on the sex chromosomes. The accumulation of specific microsatellites, such as (CAC)₁₀ and (GAG)₁₀, in centromeric and pericentromeric regions suggests an association with transposable elements, potentially driving chromosomal evolution. Notably, the substantial accumulation of these sequences on the Z and W chromosomes of Brazilian Teal and Coscoroba Swan, but not White-faced Whistling Duck, supports the hypothesis that repetitive sequence expansion occurs in a species-specific manner, contributing to sex chromosome differentiation. These findings highlight microsatellite mapping as a valuable tool for understanding chromosomal evolution and genomic differentiation in Anseriformes. Full article

The time–frequency characteristics of dolphin whistle signals under diverse ecological conditions and during environmental changes are key research topics that focus on the adaptive and response mechanisms of dolphins to the marine environment. To enhance the quality and utilization of passive acoustic monitoring (PAM) recorded dolphin whistles, the challenges faced by current wavelet thresholding methods in achieving precise threshold denoising under low signal-to-noise ratio (SNR) are confronted. This paper presents a thresholding denoising method based on stationary wavelet transform (SWT), utilizing suppression impulsive and autocorrelation function (SI-ACF) to select precise thresholds. This method introduces a denoising metric $\rho$, based on the correlation of whistle signals, which facilitates precise threshold estimation under low SNR without requiring prior information. Additionally, it exploits the high amplitude and broadband characteristics of impulsive noise, and utilizes the multi-resolution information of the wavelet domain to remove impulsive noise through a multi-level sliding window approach. The SI-ACF method was validated using both simulated and real whistle datasets. Simulated signals were employed to evaluate the method’s denoising performance under three types of typical underwater noise. Real whistles were used to confirm its applicability in real scenarios. The test results show the SI-ACF method effectively eliminates noise, improves whistle signal spectrogram visualization, and enhances the accuracy of automated whistle detection, highlighting its potential for whistle signal preprocessing under low SNR. Full article

The purpose of the current study was to investigate novice PE teachers’ reflections regarding beliefs and how they played out in teaching practices and identity formation within the settings of their profession. We recruited 31 PE teachers in their early careers to participate in this study. Data were collected through their written accounts of their reflection and in-depth interviews. Analysis of the collected data followed the procedures of thematic analysis through which reflexivity had been emphasized during the analysis process. The results showed three constructed themes. These were teachers’ reflections on their beliefs, PE teaching practices, and PE teachers’ professional identities. We conclude that PE teachers’ reflections on belief systems, as well as the purpose of the subject, have, in one way or another, shaped practices and professional identities. Teachers take up contemporary discourse regarding education, which is not fully aligned with available professional development programs that effectively improve the practices. Teachers form, maintain, and negotiate their professional identities in relation to their beliefs and their actual practices. Full article

Kart racing is one of the hobbies that people get passionate about from a young age. Kart dromes are commonly built in suburban or rural areas, generally surrounded by industrial zones and sporadic residential buildings. The circuits are primarily active during summer, hosting races that often extend into the evening and night hours, where each race has a duration of 20 min. This study examines the noise generated by kart dromes through acoustic measurements conducted at a kart drome located in southern Italy, where a microphone was placed at the side of the circuit for short periods in addition to a survey conducted at the nearest sensitive receptor. Another survey was conducted within the kart drome for a long-term period to record all of the variations in noise levels of a typical summer day when the races are organized during the nighttime; for this type of data, the hourly average values were taken for one week in June, July, and August, highlighting the increasing trend in the noise levels due to the kart races. However, a detailed analysis of noise emissions during different phases of kart operation revealed two significant acoustic events, such as the acceleration of pass-by peaks centered on high frequencies and strong breaking noise at curves that are centered at low-medium frequencies, causing a whistling noise of the wheels while turning the kart. This paper highlights the increasing trend in noise levels during summer nighttime races, compares on-site measurements with laboratory data, and discusses the implications for local communities and noise regulations. Full article