Search Results (2,089)

This paper studies how intense internal waves (IIWs) affect the holographic reconstruction of the sound field generated by a moving source in a shallow-water environment. It is assumed that the IIWs propagate along the acoustic path between the source and the receiver. The presence of IIWs introduces inhomogeneities into the waveguide and causes significant mode coupling, which perturbs the received sound field. This paper proposes the use of holographic signal processing (HSP) to eliminate perturbations in the received signal caused by mode coupling due to IIWs. Within the HSP framework, we examine the interferogram (the received sound intensity distribution in the frequency–time domain) and the hologram (the two-dimensional Fourier transform of the interferogram) of a moving source in the presence of space–time inhomogeneities caused by IIWs. A key finding is that under the influence of IIWs, the hologram is divided into two regions that correspond to the unperturbed and perturbed components of the sound field. This hologram structure enables the extraction and reconstruction of the interferogram corresponding to the unperturbed field as it would appear in a shallow-water waveguide without IIWs. Numerical simulations of HSP application under the realistic conditions of the SWARM’95 experiment were carried out for stationary and moving sources. The results demonstrate the high efficiency of holographic reconstruction of the unperturbed sound field. Unlike matched field processing (MFP), HSP does not require prior knowledge of the propagation environment. These research results advance signal processing methods in underwater acoustics by introducing efficient HSP methods for environments with spatiotemporal inhomogeneities. Full article

Creating high-fidelity digital twins (DTs) for Industry 4.0 applications, it is fundamentally reliant on the accurate 3D modeling of physical assets, a task complicated by the inherent imperfections of real-world point cloud data. This paper addresses the challenge of reconstructing accurate, watertight, and topologically sound 3D meshes from sparse, noisy, and incomplete point clouds acquired in complex industrial environments. We introduce a robust two-stage completion-to-reconstruction framework, C2R3D-Net, that systematically tackles this problem. The methodology first employs a pretrained, self-supervised point cloud completion network to infer a dense and structurally coherent geometric representation from degraded inputs. Subsequently, a novel adaptive surface reconstruction network generates the final high-fidelity mesh. This network features a hybrid encoder (FKAConv-LSA-DC), which integrates fixed-kernel and deformable convolutions with local self-attention to robustly capture both coarse geometry and fine details, and a boundary-aware multi-head interpolation decoder, which explicitly models sharp edges and thin structures to preserve geometric fidelity. Comprehensive experiments on the large-scale synthetic ShapeNet benchmark demonstrate state-of-the-art performance across all standard metrics. Crucially, we validate the framework’s strong zero-shot generalization capability by deploying the model—trained exclusively on synthetic data—to reconstruct complex assets from a custom-collected industrial dataset without any additional fine-tuning. The results confirm the method’s suitability as a robust and scalable approach for 3D asset modeling, a critical enabling step for creating high-fidelity DTs in demanding, unseen industrial settings. Full article

(1) Background: Suicide is a worldwide leading cause of death among people with mental disorders. Psychoeducation is an integral component of mental health care that may offer patients valuable tools to understand their conditions, develop coping strategies, and engage more effectively in the treatment process. In the present scoping review, we aimed to summarize the evidence on the implementation of psychoeducational interventions in inpatient settings after suicide attempts. (2) Methods: In August 2024, we searched the Web of Knowledge (all databases), PsycINFO, and CINAHL databases following the PRISMA-ScR guidelines. We included original articles evaluating the effects of psychoeducational interventions for patients hospitalized in psychiatric settings after a suicide attempt. We provided a narrative synthesis of the study characteristics and the main findings of the included studies. (3) Results: We included five papers reporting the results of six studies, of which two were randomized controlled trials. Participants were diagnosed with diverse mental disorders, and interventions were generally short in the hospitalization phase, with follow-ups in the short or long term. Outcomes were focused on suicidal ideation, depressive symptoms, and general functioning, along with feasibility and acceptability of the intervention. Psychoeducational interventions were generally well accepted, but more evidence is needed to determine their efficacy. (4) Conclusions: Psychoeducational intervention in an inpatient psychiatric setting may be important for the prevention of future suicide attempts. Nevertheless, research on the topic is still scarce. Methodologically sound randomized controlled trials evaluating the long-term efficacy of psychoeducational interventions on suicide prevention are needed. Future research should also investigate the utility of psychoeducation in non-psychiatric inpatient settings. Full article

Assessing teacher candidates’ self-efficacy in using reading strategies is essential for understanding their academic development. This study developed and validated the Teacher Candidates’ Self-Efficacy Scale for Informational Reading Strategies (TCSES-IRS) using a mixed-methods sequential exploratory design. Initial qualitative data from interviews with 33 candidates and a literature review guided item generation. Lawshe’s method confirmed content validity. The scale was administered to 1176 teacher candidates. Exploratory (n = 496) and confirmatory factor analyses (n = 388) supported a five-factor structure—cognitive, note-taking, exploration and preparation, physical and process-based, and reflective and analytical strategies—explaining 63.71% of total variance, with acceptable fit indices (χ²/df = 2.64, CFI = 0.912, TLI = 0.900, RMSEA = 0.069). Internal consistency was high (α = 0.899 total; subscales α = 0.708–0.906). An additional sample of 294 participants was used for nomological network validation. Convergent validity was demonstrated by significant item-total correlations and strong factor loadings. Discriminant validity was evidenced by moderate inter-factor correlations. Criterion-related validity was confirmed via significant group differences and meaningful correlations with an external self-efficacy measure. The TCSES-IRS emerges as a psychometrically sound tool for assessing informational reading self-efficacy, supporting research and practice in educational psychology. Full article

Aiming for convenience and the low cost of goods transfer between towns, this paper proposes a trade hub location and allocation method based on a novel artificial eagle-inspired optimization algorithm. Firstly, the trade hub location and allocation model is established, taking the total cost consisting of construction and transportation costs as the objective function. Then, to solve the nonlinear model, a novel artificial eagle optimization algorithm (AEOA) is proposed by simulating the collective migration behaviors of artificial eagles when facing a severe living environment. Three main strategies are designed to help the algorithm effectively explore the decision space: the situational awareness and analysis stage, the free exploration stage, and the flight formation integration stage. In the first stage, artificial eagles are endowed with intelligent thinking, thus generating new positions closer to the optimum by perceiving the current situation and updating their positions. In the free exploration stage, artificial eagles update their positions by drawing on the current optimal position, ensuring more suitable habitats can be found. Meanwhile, inspired by the consciousness of teamwork, a formation flying method based on distance information is introduced in the last stage to improve stability and success rate. Test results from the CEC2022 suite indicate that the AEOA can obtain better solutions for 11 functions out of all 12 functions compared with 8 other popular algorithms. Faster convergence speed and stronger stability of the AEOA are also proved by quantitative analysis. Finally, the trade hub location and allocation method is proposed by combining the optimization model and the AEOA. By solving two typical simulated cases, this method can select suitable hubs with lower construction costs and achieve reasonable allocation between hubs and the rest of the towns to reduce transportation costs. Thus, it is used to solve the trade hub location and allocation problem of Henan province in China to help the government make sound decisions. Full article

This study presents a novel approach to the end-of-line (EoL) quality inspection of brushless DC (BLDC) motors by implementing a deep learning model that combines MEL diagrams, convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRUs). The suggested system utilizes raw vibration and sound signals, recorded during the EoL quality inspection process at the end of an industrial manufacturing line. Recorded signals are transformed directly into Mel-frequency spectrograms (MFS) without pre-processing. To remove non-informative frequency bands and increase data relevance, a six-step data reduction procedure was implemented. Furthermore, to improve fault characterization, a reference spectrogram was generated from healthy motors. The neural network was trained on a highly imbalanced dataset, using oversampling and Bayesian hyperparameter optimization. The final classification algorithm achieved classification metrics with high accuracy (99%). Traditional EoL inspection methods often rely on threshold-based criteria and expert analysis, which can be inconsistent, time-consuming, and poorly scalable. These methods struggle to detect complex or subtle patterns associated with early-stage faults. The proposed approach addresses these issues by learning discriminative patterns directly from raw sensor data and automating the classification process. The results confirm that this approach can reduce the need for human expert engagement during commissioning, eliminate redundant inspection steps, and improve fault detection consistency, offering significant production efficiency gains. Full article

The rapid spread of COVID-19 increased the need for speedy diagnostic tools, which led scientists to conduct extensive research on deep learning (DL) applications that use chest imaging, such as chest X-ray (CXR) and computed tomography (CT). This review examines the development and performance of DL architectures, notably convolutional neural networks (CNNs) and emerging vision transformers (ViTs), in identifying COVID-19-related lung abnormalities. Individual ResNet architectures, along with CNN models, demonstrate strong diagnostic performance through the transfer protocol; however, ViTs provide better performance, with improved readability and reduced data requirements. Multimodal diagnostic systems now incorporate alternative methods, in addition to imaging, which use lung ultrasounds, clinical data, and cough sound evaluation. Information fusion techniques, which operate at the data, feature, and decision levels, enhance diagnostic performance. However, progress in COVID-19 detection is hindered by ongoing issues stemming from restricted and non-uniform datasets, as well as domain differences in image standards and complications with both diagnostic overfitting and poor generalization capabilities. Recent developments in COVID-19 diagnosis involve constructing expansive multi-noise information sets while creating clinical process-oriented AI algorithms and implementing distributed learning protocols for securing information security and system stability. While deep learning-based COVID-19 detection systems show strong potential for clinical application, broader validation, regulatory approvals, and continuous adaptation remain essential for their successful deployment and for preparing future pandemic response strategies. Full article

The paper presents an enhanced numerical framework for computing the one-dimensional fast Fractional Fourier Transform (FRFT) by integrating closed-form Composite Newton-Cotes quadrature rules. We show that a FRFT of a $QN$-length weighted sequence can be decomposed analytically into two mathematically commutative compositions: one involving the composition of a FRFT of an N-length sequence and a FRFT of a Q-length weighted sequence, and the other in reverse order. The composite FRFT approach is applied to the inversion of Fourier and Laplace transforms, with a focus on estimating probability densities for distributions with complex-valued characteristic functions. Numerical experiments on the Variance-Gamma (VG) and Generalized Tempered Stable (GTS) models show that the proposed scheme significantly improves accuracy over standard (non-weighted) fast FRFT and classical Newton-Cotes quadrature, while preserving computational efficiency. The findings suggest that the composite FRFT framework offers a robust and mathematically sound tool for transform-based numerical approximations, particularly in applications involving oscillatory integrals and complex-valued characteristic functions. Full article

Text-to-music (TTM) models have recently revolutionized the automatic music generation research field, specifically by being able to generate music that sounds more plausible than all previous state-of-the-art models and by lowering the technical proficiency needed to use them. For these reasons, they have readily started to be adopted for commercial uses and music production practices. This widespread diffusion of TTMs poses several concerns regarding copyright violation and rightful attribution, posing the need of serious consideration of them by the audio forensics community. In this paper, we tackle the problem of detection and attribution of TTM-generated data. We propose a dataset, FakeMusicCaps, that contains several versions of the music-caption pairs dataset MusicCaps regenerated via several state-of-the-art TTM techniques. We evaluate the proposed dataset by performing initial experiments regarding the detection and attribution of TTM-generated audio considering both closed-set and open-set classification. Full article

To address the difficulty of locating small-hole leaks in buried natural gas pipelines, this study conducted a comprehensive theoretical and numerical analysis of the acoustic characteristics associated with such leakage events. A coupled flow–acoustic simulation framework was developed, integrating gas compressibility via the realizable k-ε and Large Eddy Simulation (LES) turbulence models, the Peng–Robinson equation of state, a broadband noise source model, and the Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The effects of pipeline operating pressure (2–10 MPa), leakage hole diameter (1–6 mm), soil type (sandy, loam, and clay), and leakage orientation on the flow field, acoustic source behavior, and sound field distribution were systematically investigated. The results indicate that the leakage hole size and soil medium exert significant influence on both flow dynamics and acoustic propagation, while the pipeline pressure mainly affects the strength of the acoustic source. The leakage direction was found to have only a minor impact on the overall results. The leakage noise is primarily composed of dipole sources arising from gas–solid interactions and quadrupole sources generated by turbulent flow, with the frequency spectrum concentrated in the low-frequency range of 0–500 Hz. This research elucidates the acoustic characteristics of pipeline leakage under various conditions and provides a theoretical foundation for optimal sensor deployment and accurate localization in buried pipeline leak detection systems. Full article

Achieving sustainable economic growth requires a careful balance between public debt accumulation and the macroeconomic stability necessary for long-term development. While public debt can support growth through productive public investment, excessive debt may crowd out private investment, raise borrowing costs, and undermine financial stability, ultimately threatening economic sustainability. In this context, the quality of institutions plays a pivotal moderating role by fostering responsible debt management and ensuring that debt-financed investments contribute to sustainable development. In this context, this study investigates the relationship between public debt and economic growth, with a focus on the moderating role of institutional quality (IQ). Utilizing an unbalanced panel of 115 countries over the period from 1996 to 2021, this study tests the hypothesis that robust institutional frameworks mitigate the negative impact of public debt on economic growth. To address potential endogeneity, this study employs the dynamic system Generalized Method of Moments (GMM) estimation technique. The results reveal that, although the direct effect of public debt on economic growth is negative, the interaction between public debt and IQ yields a positive influence. Furthermore, the results indicate the presence of a threshold beyond which public debt begins to exert a beneficial effect on economic growth, whereas its impact remains adverse below this threshold. These findings underscore the critical importance of sound debt management strategies and institutional development for policymakers, suggesting that effective government governance is essential to harnessing the potential positive effects of public debt on economic growth. Full article

The increasing integration of renewable energy sources and the rising demand for electricity has intensified concerns over voltage stability in radial distribution systems. These networks are particularly susceptible to voltage collapse under heavy loading conditions, posing serious system reliability and efficiency risks. Integrating distributed generation (DG) has emerged as a strategic solution to strengthen voltage profiles and reduce power losses. To address this challenge, this study proposes a novel distribution voltage stability index (NDVSI) for accurately assessing voltage stability and guiding optimal DG placement and sizing. The NDVSI provides a reliable tool to identify weak buses and their neighboring nodes that critically impact stability. By targeting these locations, the method ensures DG units are installed where they offer maximum improvement in voltage support and minimum power losses. The approach is implemented using MATLAB R2019a (MathWorks Inc., Natick, MA, USA) and validated on three benchmark radial distribution systems, including IEEE 12-bus, 33-bus, and 69-bus systems, demonstrating its scalability and effectiveness across different grid complexities. Comparative analysis with existing voltage stability indices confirms the superiority of NDVSI in both diagnostic precision and practical application. The proposed approach offers a technically sound and economically viable tool for enhancing the reliability, stability, and performance of modern distribution networks. Full article

This article highlights the importance of evaluating the morphological characteristics of female dogs during breeding soundness examinations (BSEs) to assess their reproductive potential and ensure the health of future offspring. Key traits considered in this evaluation include body type, body condition score, skull size and shape, mammary gland conformation, and coat quality. Each of these factors plays a significant role in the health and fertility of breeding females. For example, deviations from breed standards in body size can lead to complications during pregnancy and whelping, while an inappropriate body condition score may disrupt hormonal balance and reproductive cycles. This review also addresses concerns related to brachycephalic breeds, whose conformation can contribute to respiratory and reproductive issues. Additionally, assessing vertebral and pelvic conformation is crucial to prevent dystocia and other complications during delivery. By systematically evaluating these morphological traits, veterinarians can promote ethical breeding practices that prioritize the welfare and genetic health of both breeding females and their puppies. Therefore, integrating comprehensive morphological evaluations into BSEs is essential for responsible dog breeding management, ultimately supporting better reproductive outcomes and healthier future generations. Full article

Respiratory diseases present significant global health challenges. Recent advances in respiratory sound analysis (RSA) have shown great potential for automated disease diagnosis and patient management. The International Conference on Biomedical and Health Informatics 2017 (ICBHI2017) database stands as one of the most authoritative open-access RSA datasets. This review systematically examines 135 technical publications utilizing the database, and a comprehensive and timely summary of RSA methodologies is offered for researchers and practitioners in this field. Specifically, this review covers signal processing techniques including data resampling, augmentation, normalization, and filtering; feature extraction approaches spanning time-domain, frequency-domain, joint time–frequency analysis, and deep feature representation from pre-trained models; and classification methods for adventitious sound (AS) categorization and pathological state (PS) recognition. Current achievements for AS and PS classification are summarized across studies using official and custom data splits. Despite promising technique advancements, several challenges remain unresolved. These include a severe class imbalance in the dataset, limited exploration of advanced data augmentation techniques and foundation models, a lack of model interpretability, and insufficient generalization studies across clinical settings. Future directions involve multi-modal data fusion, the development of standardized processing workflows, interpretable artificial intelligence, and integration with broader clinical data sources to enhance diagnostic performance and clinical applicability. Full article

Synthetic jet actuators are becoming increasingly popular for enhancing electronic heat transfer. However, their use is currently limited due to the high noise they generate. This article examines how actuator parameters (orifice diameter, orifice length and cavity height) affect synthetic jet velocity and noise generation. Hot-wire anemometry was used to measure velocity, and noise was measured with a sound meter. The actuator was supplied with constant power at different frequencies ranging from 50 to 500 Hz. Observation of the velocity showed that it decreased with an increasing orifice diameter and increased with a decreasing orifice length. No relationship was observed between cavity height and synthetic jet velocity. This article indicates that increasing the orifice diameter or reducing the orifice length causes an increase in the noise generated by SJAs, provided we remain in the vicinity of the characteristic frequency. It was demonstrated that higher actuator chambers produce higher noise levels, although this was not a consistent trend across the entire tested frequency range. Full article