Search Results (92)

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

Background: Salmonella enterica serovar Hvittingfoss, a member of the non-typhoidal Salmonella group, is an important foodborne serovar most frequently identified in regions (Australia, Belgium, and the United States) with active surveillance systems. This serovar has been implicated in outbreaks of foodborne illness. Soft cheese, crab cocktail, beef, and rock melon are commonly involved in these outbreaks. Although the definitive host of this serovar is not yet established, Salmonella Hvittingfoss can be found in wild animals (Feral pigs), bird populations (Bar-tailed godwit, Plumed whistling ducks, White-naped crane, and Sharp-tailed sandpiper), and domestic birds like chickens. Case presentation: We report the first case of Salmonella enterica serovar Hvittingfoss causing acute gastroenteritis in a 52-year-old male labourer and agriculturist from a rural area of Mysuru, South India. This is the first documented case of Salmonella Hvittingfoss causing acute gastroenteritis in India. Conclusions: While non-typhoidal Salmonella infections typically resolve on their own, they can be particularly dangerous for certain demographics, such as children, the elderly, and those with compromised immune systems. Therefore, studying the different serotypes of these infections in both clinical and subclinical cases is crucial for targeting effective surveillance, hygiene practises, and food safety measures that can mitigate their impact on public health. Full article

Bottlenose dolphins often conceal behavioral signs of illness until they reach an advanced stage. Motivated by the efficacy of vocal biomarkers in human health diagnostics, we utilized supervised machine learning methods to assess various model architectures’ effectiveness in classifying dolphin health status from the acoustic features of their whistles. A gradient boosting classifier achieved a 72.3% accuracy in distinguishing between normal and abnormal health states—a significant improvement over chance (permutation test; 1000 iterations, p < 0.001). The model was trained on 30,693 whistles from 15 dolphins and the test set (15%) totaled 3612 ‘normal’ and 1775 ‘abnormal’ whistles. The classifier identified the health status of the dolphin from the whistles features with 72.3% accuracy, 73.2% recall, 56.1% precision, and a 63.5% F1 score. These findings suggest the encoding of internal health information within dolphin whistle features, with indications that the severity of illness correlates with classification accuracy, notably in its success for identifying ‘critical’ cases (94.2%). The successful development of this diagnostic tool holds promise for furnishing a passive, non-invasive, and cost-effective means for early disease detection in bottlenose dolphins. Full article

Hitherto unnoticed similarities between two short stories by Gustav Meyrink and two of the most renowned and widely read ghost stories of M.R. James are detailed through comparative literary analysis. Specifically, one early occult horror tale of Meyrink, The Plants of Dr. Cinderella (1905), shows no less than about 15 congruences beneath the plot level (concerning specific story requisites) with M.R. James’s ‘Oh, Whistle, and I’ll Come to You, My Lad’ (1904), as does, to the same extent, a later, widely known Meyrink tale (The Cardinal Napellus, 1914) vis-à-vis M.R. James’s Mr Humphreys and His Inheritance (1911). Although direct, conclusive evidence is unavailable, a nexus of circumstantial evidence, building on extensive biographical and bibliographical inquiries, convergently attests to these assumed literary influences on Meyrink: for both cases, the chronology is intact and thus possible; Meyrink was expertly fluent in English and well-connected to England and English literature; and, these borrowings are reminiscent of other, already known originality issues surrounding Meyrink’s work. Altogether, these new discoveries shed fresh light on idiosyncrasies of Meyrink’s creative process, imagination, and literary production; on his still under-researched literary inspirational sources; as well as on the early reception of M.R. James’s ghostly fiction beyond the anglophone sphere. Full article

Ring signatures are widely used in e-voting, anonymous whistle-blowing systems, and blockchain transactions. However, due to the anonymity of ring signatures, a signer can sign the same message multiple times, potentially leading to repeated voting or double spending in blockchain transactions. To address these issues in blockchain transactions, this work constructs an identity-based linkable ring signature scheme based on the hardness of the lattice-based Module Small Integer Solution (M-SIS) assumption, which is hard even for quantum attackers. The proposed scheme is proven to be anonymous, unforgeable, linkable, and nonslanderable in the random oracle model. Compared to existing identity-based linkable ring signature (IBLRS) schemes of linear size, our signature size is relatively smaller, and this advantage is more pronounced when the number of ring members is small. We provide approximate signature size data for ring members ranging from 2 to 2048. When the number of ring members is 16 (or 512. resp.), the signature size of our scheme is 11.40 KB (or 24.68 KB, respectively). Finally, a threshold extension is given as an additional scheme with specifications and security analysis. Full article

Recent years have seen a notable rise in dolphin-watching boat activities along the Algarve coast in Portugal, potentially affecting the common dolphin (Delphinus delphis) and bottlenose dolphin (Tursiops truncatus) local populations. This study examines the impact of increasing underwater noise levels from these boats on dolphin vocalizations. Field recordings were conducted from June to September 2022, analyzing dolphin whistles in various boat presence scenarios. The results indicate significant changes in whistle-frequency characteristics with boat presence, including increased start, low, and high frequencies, alongside a decrease in the number of inflection points in modulated whistles. The changes might negatively impact dolphin populations viability, underscoring the need for further research. Additionally, improved mitigation strategies may be necessary to reduce the potential negative effects of dolphin watching on cetacean communication and behavior in the Algarve region. Full article