Search Results (386)

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

Blended and virtual learning have become an integral part in international higher education, especially in the wake of the COVID-19 pandemic and the European Union’s Digital Education Action Plan. These modalities have enabled more inclusive, flexible, and sustainable forms of international collaboration, such as Collaborative Online International Learning (COIL) and Blended Intensive Programs (BIPs), reshaping the landscape of global academic mobility. This paper introduces the INVITE Learning Design Framework (LDF), developed to support higher education instructors in designing high-quality, internationalized blended and virtual learning experiences. The framework addresses the growing need for structured, theory-informed approaches to course design that foster student engagement, intercultural competence, and motivation in non-face-to-face settings. The INVITE LDF was developed through a rigorous scoping review of existing models and frameworks, complemented by needs-identification analysis and desk research. It integrates Self-Determination Theory, Active Learning principles, and the ADDIE instructional design model to provide a comprehensive, adaptable structure for course development. The framework was successfully implemented in a large-scale online training module for over 1000 educators across Europe. Results indicate that the INVITE LDF enhances educators’ ability to create engaging, inclusive, and pedagogically sound international learning environments. Its application supports institutional goals of internationalization by making global learning experiences more accessible and scalable. The findings suggest that the INVITE LDF can serve as a valuable tool for higher education institutions worldwide, offering a replicable model for fostering intercultural collaboration and innovation in digital education. This contributes to the broader transformation of international higher education, promoting equity, sustainability, and global citizenship through digital pedagogies. Full article

Raicilla is a distinctive Mexican beverage produced in two central regions of Jalisco. This study aimed to analyze the physicochemical parameters of 25 raicilla alcoholic drinks originating from the Coast and Sierra regions. Each of the 25 raicilla brands underwent measurements of pH, conductivity, alcohol content, total solids, viscosity, sound velocity, density, and refractive index. Notably, these measurements are cost-effective and their analysis is straightforward. The results were analyzed using principal component analysis (PCA) and cluster analysis. According to the PCA, two main components were identified, explaining 81.77% of the total variability of the physicochemical measurements of the distinct Coast and Sierra regions. Furthermore, applying Fisher’s LSD to the Sierra raicilla cluster allowed for the identification of variations. Specifically, samples from the Sierra zone groups were identified through cluster analysis, demonstrating similarities in physicochemical parameters; both statistical methods indicated no significant differences in the physicochemical parameters between a more acidic pH, higher conductivity, and greater density than those from the Coast zone. After the analysis was carried out, it was possible to find similarities and differences between the raicilla produced in the two regions, so it is possible to assume that using these results could facilitate the authentication of raicilla. 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

This paper presents a robust hardware system designed for future detection of faults in wind turbines by analyzing audible noise signals. Predictive maintenance strategies have increasingly relied on acoustic monitoring as a non-invasive method for identifying anomalies that may indicate component wear, misalignment, or impending mechanical failures. The proposed device captures and processes sound signals in real-time using strategically positioned microphones, ensuring high-fidelity data acquisition without interfering with turbine operation. Signal processing techniques are applied to extract relevant acoustic features, facilitating future identification of abnormal sound patterns that may indicate mechanical issues. The system’s effectiveness was validated through rigorous field tests, demonstrating its capability to enhance the reliability and efficiency of wind turbine maintenance. Experimental results showed an average transmission latency of 131.8 milliseconds, validating the system’s applicability for near real-time audible noise monitoring in wind turbines operating under limited connectivity conditions. Full article

This study investigates L3 Spanish perception patterns among L1 Korean–L2 English bilinguals with varying L3 proficiency levels, aiming to test the applicability of traditional L2 perceptual models in multilingual contexts. We conducted two experiments: a cross-linguistic discrimination task and a cross-language identification task. Results revealed unexpected outcomes unique to multilingual contexts. Participants had difficulty reliably discriminating between cross-linguistic categories and showed little improvement over time. Similarly, they did not demonstrate progress in categorizing sounds specific to each language. The absence of a clear correlation between proficiency levels and the ability to discriminate and categorize sounds suggests that input distribution and language-specific activation may play more critical roles in L3 perception, consistent with the distributional learning approach. We argue that phoneme distributions from all three languages likely occupy a shared perceptual space. When a specific language is activated, the relevant phoneme distributions become dominant, while others are suppressed. This selective activation, while not crucial in traditional L1 and L2 studies, is critical in L3 contexts, like the one examined here, where managing multiple phonemic systems complicates discrimination and categorization. These findings underscore the need for theoretical adjustments in multilingual phonetic acquisition models and highlight the complexities of language processing in multilingual settings. Full article

Background/Objectives: The diagnostic approach to prostate cancer (PCa) has evolved from systematic biopsies to imaging-guided strategies that improve detection of clinically significant PCa (csPCa) while reducing overdiagnosis. Multiparametric magnetic resonance imaging (mpMRI) has emerged as the gold standard for pre-biopsy evaluation, while micro-ultrasound (MicroUS) offers a promising alternative with real-time imaging capabilities. Methods: We examined the principles, image interpretation frameworks (Prostate Imaging Reporting and Data System (PI-RADS) and Prostate Risk Identification using Micro UltraSound (PRI-MUS)), and clinical applications of mpMRI and MicroUS, comparing their diagnostic accuracy in biopsy-naïve patients, repeat biopsy scenarios, active surveillance, and staging. Results: mpMRI improves csPCa detection, reduces unnecessary biopsies, and enhances risk stratification. Landmark studies such as PRECISION (Prostate Evaluation for Clinically Important Disease: Sampling Using Image Guidance or Not?) and PRIME (Prostate Imaging Using MRI±Contrast Enhancement) confirm its superiority over systematic biopsy. However, mpMRI remains resource-intensive, with limitations in accessibility and interpretation variability. Conversely, MicroUS, with its high-resolution real-time imaging, shows non-inferiority to mpMRI and potential advantages in magnetic resonance imaging (MRI)-ineligible patients. It improves lesion visualization and biopsy targeting, with ongoing trials such as OPTIMUM (Optimization of prostate biopsy—Micro-Ultrasound versus MRI) evaluating its standalone efficacy. Conclusions: mpMRI and MicroUS are complementary modalities in PCa diagnosis. While mpMRI remains the preferred imaging standard, MicroUS offers an alternative, particularly in patients with MRI contraindications. Combining these techniques could enhance diagnostic accuracy, reduce unnecessary interventions, and refine active surveillance strategies. Future research should focus on integrating both modalities into standardized diagnostic pathways for a more individualized approach. Full article

Moldy core is a common disease of apples, and non-destructive, rapid and accurate detection of moldy core apples is essential to ensure food safety and reduce post-harvest economic losses. In this study, the acoustic method was used for the first time for the online detection of moldy core apples, and we explore the feasibility of integrating acoustic and visible–near-infrared spectroscopy (Vis–NIRS) technologies for precise, real-time detection of moldy core in apples. The sound and Vis–NIRS signals of apples were collected using a novel acoustic online detection device and a traditional Vis–NIRS online sorter, respectively. Based on this, traditional machine learning and deep learning classification models were developed for the prediction of healthy, mild, moderate, and severe moldy apples. The results show that the acoustic detection method significantly outperforms the Vis–NIRS method in terms of moldy apple identification accuracy, and the fusion of acoustic and Vis–NIRS data can further improve the model prediction performance. The MLP-Transformer shows the best prediction performance, with the overall classification accuracies for the fusion of Vis–NIRS, acoustic, Vis–NIRS and acoustic reached 89.66%, 96.55%, and 98.62%, respectively. This study demonstrates the excellent performance of acoustic online detection for intra-fruit lesion identification and shows the potential of the fusion of acoustics and Vis–NIRS. Full article

Background/Objectives: This study investigates the application of machine learning (ML) techniques in diagnosing speech sound disorders (SSDs) in Saudi Arabic-speaking children, with a specific focus on phonological biomarkers, particularly Infrequent Variance (InfrVar), to improve diagnostic accuracy. SSDs are a significant concern in pediatric speech pathology, affecting an estimated 10–15% of preschool-aged children worldwide. However, accurate diagnosis remains challenging, especially in linguistically diverse populations. Traditional diagnostic tools, such as the Percentage of Consonants Correct (PCC), often fail to capture subtle phonological variations. This study explores the potential of machine learning models to enhance diagnostic accuracy by incorporating culturally relevant phonological biomarkers like InfrVar, aiming to develop a more effective diagnostic approach for SSDs in Saudi Arabic-speaking children. Methods: Data from 235 Saudi Arabic-speaking children aged 2;6 to 5;11 years were analyzed using several machine learning models: Random Forest, Support Vector Machine (SVM), XGBoost, Logistic Regression, K-Nearest Neighbors, and Naïve Bayes. The dataset was used to classify speech patterns into four categories: Atypical, Typical Development (TD), Articulation, and Delay. Phonological features such as Phonological Variance (PhonVar), InfrVar, and Percentage of Consonants Correct (PCC) were used as key variables. SHapley Additive exPlanations (SHAP) analysis was employed to interpret the contributions of individual features to model predictions. Results: The XGBoost and Random Forest models demonstrated the highest performance, with an accuracy of 91.49% and an AUC of 99.14%. SHAP analysis revealed that articulation patterns and phonological patterns were the most influential features for distinguishing between Atypical and TD categories. The K-Means clustering approach identified four distinct subgroups based on speech development patterns: TD (46.61%), Articulation (25.42%), Atypical (18.64%), and Delay (9.32%). Conclusions: Machine learning models, particularly XGBoost and Random Forest, effectively classified speech development categories in Saudi Arabic-speaking children. This study highlights the importance of incorporating culturally specific phonological biomarkers like InfrVar and PhonVar to improve diagnostic precision for SSDs. These findings lay the groundwork for the development of AI-assisted diagnostic tools tailored to diverse linguistic contexts, enhancing early intervention strategies in pediatric speech pathology. Full article

Accurate acoustic identification of marine mammals is vital for monitoring ocean health and human impacts. Existing methods often struggle with limited single-feature representations or suboptimal fusion of multiple features. This paper proposes an Evaluation-Adaptive Weighted Multi-Head Fusion Network that integrates CQT and STFT features via a dual-branch ResNet architecture. The model enhances intra-branch features using channel attention and adaptive weighting of each branch based on its validation accuracy during training. Experiments on the Watkins Marine Mammal Sound Database show that the proposed method achieves superior performance, reaching 96.05% accuracy and outperforming baseline and attention-based fusion models. This approach offers an effective solution for multi-feature acoustic recognition in complex underwater environments. Full article

Background: Sensory processing difficulties can interfere with daily functioning and participation across adulthood. While standardized assessment tools exist, culturally validated instruments for Arabic-speaking adults remain limited. Objectives: This study aimed to validate the Arabic version of the Sensory Processing Measure—Second Edition (SPM-2) Adult Self-Report form in a Saudi population and evaluate its utility for the early detection of sensory processing challenges in at-risk individuals. Methods: A total of 399 Saudi adults (205 females and 194 males), aged 21 to 87 years (M = 44.1; SD = 16.2), completed the Arabic SPM-2 online. The scale consists of eight subscales, six of which form the Sensory Total score—Vision, Hearing, Touch, Taste and Smell, Body Awareness, and Balance and Motion—representing core sensory processing abilities (i.e., Sensory Total (ST)). The remaining two—Planning and Ideas and Social Participation—capture higher-order integrative functions and do not contribute to the ST. Results: The overall scale demonstrated strong internal consistency (α = 0.89), with subscale alphas ranging from 0.43 (Hearing) to 0.70 (Body Awareness). Confirmatory factor analysis (CFA) (χ² [3052] = 4147.4; p < 0.001) showed good absolute fit (RMSEA = 0.030) and moderate incremental fit (CFI = 0.74; TLI = 0.73), values that are typical for large-df models. Descriptive and cluster analyses identified distinct participant subgroups with elevated frequency ratings (scores of 2 or 3) suggestive of sensory risk. Significant age-related differences were observed across multiple sensory domains, while no significant sex-related effects were found. Conclusions: Although Social Participation and Hearing showed lower reliability, the Arabic SPM-2 exhibits sound internal structure and therefore shows promise for future clinical application once criterion validity is established. The findings support its application in culturally responsive screening, early risk identification, and intervention planning in Arabic-speaking contexts. Full article

The exponential growth of the installation of solar photovoltaic systems has been a significant step in the energy transition toward reducing dependence on fossil fuels and mitigating climate change. This growth has raised concerns about land use, particularly in regions where large tracts are allocated to solar farms. Highway infrastructures such as sound barriers occupy large land surfaces which are under-utilized and could therefore contribute to renewable energy generation without increasing the land use. This study proposes the application of the YOLO object detection algorithm to automatically identify and analyse the locations of sound barriers along highways using video or image data, and to estimate the potential energy output from photovoltaic systems installed on these barriers. The model has been trained and tested on sound barriers along Portuguese highways, achieving a mean average precision exceeding 0.84 for YOLOv10 when using training datasets containing more than 600 images. Using the geolocation of the images and the identification of the number of sound barriers from YOLO, it is possible to estimate the potential generation of electricity and inform decision makers on the technical–economic feasibility of using this infrastructure for energy generation. Full article

Monitoring large critical infrastructures is a highly complex and costly task. The use of a network of sensors to aid in the detection and identification of potential anomalies is therefore an important step towards easing maintenance effort while maintaining operational soundness. To address this challenge, a monitoring system was developed and installed in a seaport drawbridge. The structural parameters monitored during operation can be used to assess the bridge’s structural behavior. This provides the ability to identify potential anomalies that could lead to its failure at an early stage, allowing for the better planning of maintenance interventions, saving time and money. In this paper, the monitoring system will be presented and the employed signal identification and analysis methods will be described. Full article

This paper introduces an acoustic-based monitoring system for high-speed CNC drilling, aimed at optimizing processes and enabling real-time machine state detection. High-fidelity acoustic sensors capture sound signals during drilling operations, allowing the identification of critical events such as tool engagement, material breakthrough, and tool withdrawal. Advanced signal processing techniques, including spectrogram analysis and Fast Fourier Transform, extract dominant frequencies and acoustic patterns, while machine learning algorithms like DBSCAN clustering classify operational states such as cutting, breakthrough, and returning. Experimental studies on materials including acrylic, PTFE, and hardwood reveal distinct acoustic profiles influenced by material properties and drilling conditions. Smoother sound patterns and lower dominant frequencies characterize PTFE drilling, whereas hardwood produces higher frequencies and rougher patterns due to its density and resistance. These findings demonstrate the correlation between acoustic emissions and machining dynamics, enabling non-invasive real-time monitoring and predictive maintenance. As AI power increases, it is expected to extract in-situ process information and achieve higher resolution, enhancing precision in data interpretation and decision-making. A key contribution of this project is the creation of an open sound library for drilling processes, fostering collaboration and innovation in intelligent manufacturing. By integrating big data concepts and intelligent algorithms, the system supports continuous monitoring, anomaly detection, and process optimization. This AI-ready hardware enhances the accuracy and efficiency of drilling operations, improving quality, reducing tool wear, and minimizing downtime. The research establishes acoustic monitoring as a transformative approach to advancing CNC drilling processes and intelligent manufacturing systems. Full article

Dental anxiety is an intense and irrational fear of visiting the dentist or of undergoing dental procedures. Background/Objectives: The aim of this study was to investigate the prevalence of dental anxiety in children aged 6–11 years and to identify the importance of communication in reducing anxiety in pediatric patients. Methods: The research was conducted through a questionnaire administered to 101 students (55.4% girls and 44.6% boys), aged 6–11 years, from the North-West Region of Romania. The data collected included the age and gender of the subjects, their previous experiences with the dentist, the identification of factors that trigger anxiety, and the way in which patients perceive future dental visits. Results: This study found that for the majority of participants, a visit to the dentist does not represent a source of fear. Moreover, most children are eager to visit the dentist again. Gender and age did not have a significant effect on the prevalence of anxiety. Elements such as sitting in the dental chair, observing dental instruments, having the teeth examined with a mirror, and hearing the sounds produced by the instruments were identified as factors that may cause anxiety in pediatric patients. Conclusions: It was found that pediatric patients who have good communication with the practitioner display lower anxiety levels compared to those of others. Full article