Search Results (168)

In this study, the effects of pressure anisotropy and viscosity on the propagation of shock waves in spin-polarized degenerate quantum magnetoplasma are studied under the influence of the streaming energy of ion beams. The effects of different suitable plasma parameters on the shock wave’s potential profile are studied using the steady state solution of the Zakharov–Kuznetsov–Burgers (Z–K–B) equation, as well as the numerical simulation of the governing non-linear Z–K–B equation. First-order analysis of the non-linear wave propagation depicted a new beam-induced stable mode whose Mach number may be subsonic or supersonic depending on the anisotropic pressure combination in the presence of different spin density polarization ratios. This is the first observation of this new beam-induced stable mode in ion beam plasma, apart from the other existing modes of ion beam plasma systems, namely, the fast beam mode, the slow beam mode, the inherent ion acoustic mode, and the coupled mode, which also has unique propagation characteristics compared to the other modes. The spin density polarization ratio of spin-up and spin-down electrons have an unprecedented effect on the polarity and the direction of propagation of different shock wave modes in such plasma systems. Apart from the spin effect, anisotropic pressure combinations, as well as the viscosity of ions and ion beams, also play an outstanding role in controlling the nature of propagation of shock waves, especially in the newly detected beam-induced stable mode, and depending on the viscosity parameters of ions and ion beams, both oscillatory and monotonic shock waves can propagate in such plasma. Full article

Acoustic monitoring facilitates the detailed study of herbivore grazing by generating a timeline of sound bursts associated with jaw movements (JMs) that perform bite or chew actions. The unclassified stream of JM events was used here in an observational study to explore the notion of “grazing time”. Working with shepherded goat herds in a wooded landscape, a horn-based acoustic sensor with a vibration-type microphone was deployed on a volunteer animal along each of 12 foraging routes. The software-generated timeline of unclassified JMs contained a total of 334,582 events. After excluding rumination bouts, minutely JM rates showed a broad, non-normal distribution, with an overall mean of 61 JM min⁻¹. The frequency distribution of inter-JM interval values scaled logarithmically, with a peak in the region of 0.43 s representing a baseline interval that generates the unconstrained, more-or-less regular, rhythm of jaw movement (≈140 JM min⁻¹). This rhythm was punctuated by interruptions, for which duration scaled logarithmically, and which were primarily related to the search phase of the intake process. The empirical time accumulation curve shows the contribution of the inter-JM interval to the total foraging time and provides a penetrating profile of how the animal interacted with the foraging environment. The sum total of time along a foraging route spent at a near-potential JM rate was only ≈1 h, whereas sub-potential rates containing intervals as long as ≈30 s accounted for the bulk of the foraging route. The dimensionless behavioral grazing intensity was defined as the product of the number of ingestive JMs performed and the baseline interval, divided by the duration of the foraging route (excluding rumination). Values were mostly <0.5 for the foraging routes examined. This has implications for how animal presence should be translated to grazing pressure and for how long animals need to forage to meet their nutritional requirements. Full article

This study explores the feasibility of using low-intensity focused ultrasound (LIFU) to induce rotational sensations in the human semicircular canal (SCC) through the acoustic streaming effect. Existing vestibular stimulation methods, such as galvanic vestibular stimulation (GVS), caloric vestibular stimulation (CVS), and magnetic vestibular stimulation (MVS), face limitations in spatial and temporal resolution, with unclear mechanisms. This study investigates whether LIFU can overcome these limitations by modulating endolymph motion within SCC. A 3D finite element model was constructed to simulate the effects of LIFU-induced acoustic streaming on SCC (particularly the endolymph), with thermal effects evaluated to ensure safety. Fluid–structure interaction (FSI) was used to analyze the relationship between endolymph flow and cupula deformation. By adjusting the focal point of the ultrasound transducer, we were able to alter fluid flow pattern, which resulted in variations in cupula displacement. The results demonstrated that LIFU successfully induces fluid motion in SCC without exceeding thermal safety limits (<1 °C), suggesting its potential for controlling rotational sensations, with cupula displacement exceeding 1 μm. This novel approach enhances the understanding of LIFU’s thermal and neuromodulatory effects on the vestibular system, and thereby offers promising implications for future therapeutic applications. Full article

Communication with the underwater vehicles during their tasks is one of the most important issues. The need for real-time data transfer raises the necessity of developing communication systems. Conventional underwater communication systems, such as acoustic systems, cannot satisfy applications that need a high transmission data rate. In this study, we investigate the radio frequency communication system in seawater, which is crucial for real-time data transfer with underwater vehicles. The experiments were in a water tank full of seawater and a real environment in the ocean. Three types of antennae were used: loop antenna, wire antenna, and helical antenna. An Autonomous Underwater Vehicle (AUV) is used as a transmitter to measure the transmission rate as a function of distance. The helical antenna showed better performance regarding the coverage area. Furthermore, the AUV could move freely within the helical and capture live video streaming successfully. This investigation underscores the potential of radio frequency communication systems for enhancing underwater vehicle operations, offering promising avenues for future research and practical implementation. Full article

We use high-resolution coupled atmosphere–ocean model simulations over the Gulf Stream extension region during a winter season to examine the upper ocean thermodynamic response to including current feedback to atmospheric wind stress. We demonstrate that a model that includes current feedback leads to significant changes in the structure and transport of heat throughout the upper ocean in comparison to the same model without current feedback. We find that including the current feedback leads to changes in the upper ocean temperature pattern that match the vorticity pattern. Areas with cyclonic ocean vorticity, typically north of the Gulf Stream extension, correspond to areas with warmer temperatures throughout the water column. Areas with anticyclonic ocean vorticity, typically south of the Gulf Stream extension, correspond to areas with cooler temperatures throughout the water column. We also find that including current feedback leads to an overall reduction in the submesoscale vertical heat flux spectra across all spatial scales, with differences in the submesoscale vertical heat flux corresponding to SST minus mixed layer temperature differences. The direct impact of current feedback on the thermodynamic structure within the upper ocean also has indirect impacts on other aspects of the ocean, such as the energy transfer between the ocean and the atmosphere, ocean stratification, and acoustic parameters. Full article

This article studies the general properties of the Stokes drift field. This name is commonly used for the correction added to the mean Eulerian velocity for describing the averaged transport of the material particles by the oscillating fluid flows. Stokes drift is widely known mainly in connection with another feature of oscillating flows known as steady streaming, which has been and remains the focus of a multitude of studies. However, almost nothing is known about Stokes drift in general, e.g., about its energy or helicity (Hopf’s invariant). We address these quantities for acoustic drift driven by simple sound waves with finite discrete Fourier spectra. The results discover that the mean drift energy is partly localized on a certain resonant set, which we have described explicitly. Moreover, the mean drift helicity turns out to be completely localized on the same set. We also present several simple examples to discover the effect of the power spectrum and positioning of the spectral atoms. It is revealed that tuning them can drastically change both resonant and non-resonant energies, zero the helicity, or even increase it unboundedly. Full article

Laser cladding has unique technical advantages, such as precise heat input control, excellent coating properties, and local selective cladding for complex shape parts, which is a vital branch of surface engineering. During the laser cladding process, the parts are subjected to extreme thermal gradients, leading to the formation of micro-defects such as cracks, pores, and segregation. These defects compromise the serviceability of the components. Ultrasonic vibration can produce thermal, mechanical, cavitation, and acoustic flow effects in the melt pool, which can comprehensively affect the formation and evolution for the microstructure of the melt pool and reduce the microscopic defects of the cladding layer. In this paper, the coupling model of temperature and flow field for the laser cladding of 45 steel 316L was established. The transient evolution laws of temperature and flow field under ultrasonic vibration were revealed from a macroscopic point of view. Based on the phase field method, a numerical model of dendrite growth during laser cladding solidification under ultrasonic vibration was established. The mechanism of the effect of ultrasonic vibration on the solidification dendrite growth during laser cladding was revealed on a mesoscopic scale. Based on the microstructure evolution model of the paste region in the scanning direction of the cladding pool, the effects of a static flow field and acoustic flow on dendrite growth were investigated. The results show that the melt flow changes the heat and mass transfer behaviors at the solidification interface, concurrently changing the dendrites’ growth morphology. The acoustic streaming effect increases the flow velocity of the melt pool, which increases the tilt angle of the dendrites to the flow-on side and promotes the growth of secondary dendrite arms on the flow-on side. It improves the solute distribution in the melt pool and reduces elemental segregation. Full article

The small dimensions of microfluidic channels allow for fast diffusive or passive mixing, which is beneficial for time-sensitive applications such as chemical reactions, biological assays, and the transport of to-be-detected species to sensors. In microfluidics, the need for fast mixing within milliseconds arises primarily because these devices are often used in fields where rapid and efficient mixing significantly impacts the performance and outcome of the processes. Active mixing with acoustics in microfluidic devices involves using acoustic waves to enhance the mixing of fluids within microchannels. Using sharp corners and wall patterns in acoustofluidic devices significantly enhances the mixing by acoustic streaming around these features. The streaming patterns around the sharp edges are particularly effective for the mixing because they can produce strong lateral flows that rapidly homogenize liquids. This work presents extensive characterizations of the effect of sharp-edged structures on acoustic mixing in bulk acoustic wave (BAW) mode in a silicon microdevice. The effect of side wall patterns in different angles and shapes, their positions, the type of piezoelectric transducer, and its amplitude and frequency have been studied. Following the patterning of the channel walls, a mixing time of 25 times faster was reached, compared to channels with smooth side walls exhibiting conventional BAW behavior. The average locally determined acoustic streaming velocity inside the channel becomes 14 times faster if sharp corners of 10° are added to the wall. Full article

Nonlinear acoustic effects become prominent when acoustic waves propagate through an orifice, particularly at higher pressure amplitudes, potentially generating vortex rings and transferring acoustic energy into the flow. This study develops and validates a predictive theoretical model for acoustic behaviour both within and outside an orifice under linear conditions. Using transfer matrices, the model predicts the external acoustic field, while finite element numerical simulations are employed to validate the theoretical predictions in the linear regime. The experimental setup includes an impedance tube with a plate and orifice, supported by a custom-built system, where a loudspeaker generates acoustic waves. A single microphone is used to measure acoustic particle velocity and characterize the phenomenon, enabling the identification of the onset of nonlinearity. The experimental data show good agreement with the linear theoretical predictions. This work represents the first observation of nonlinear effects in a free-field environment within a semi-anechoic chamber, eliminating reflections from external surfaces, and demonstrates the efficacy of a purely acoustic-based system (speaker and two microphones) for evaluating speaker velocity and the resulting velocity within the orifice. Full article

Estimating the line spectra of ship-radiated noise is a crucial remote sensing technique for detecting and recognizing underwater acoustic targets. Improving the signal-to-noise ratio (SNR) makes the low-frequency components of the target signal more prominent. This enhancement aids in the detection of underwater acoustic signals using sonar. Based on the characteristics of low-frequency narrow-band line spectra signals in underwater target radiated noise, we propose a dual-stream deep learning network with frequency characteristics transformation (DS_FCTNet) for line spectra estimation. The dual streams predict amplitude and phase masks separately and use an information exchange module to swap learn features between the amplitude and phase spectra, aiding in better phase information reconstruction and signal denoising. Additionally, a frequency characteristics transformation module is employed to extract convolutional features between channels, obtaining global correlations of the amplitude spectrum and enhancing the ability to learn target signal features. Through experimental analysis on ShipsEar, a dataset of underwater acoustic signals by hydrophones deployed in shallow water, the effectiveness and rationality of different modules within DS_FCTNet are verified.Under low SNR conditions and with unknown ship types, the proposed DS_FCTNet model exhibits the best line spectrum enhancement compared to methods such as SEGAN and DPT_FSNet. Specifically, SDR and SSNR are improved by 14.77 dB and 13.58 dB, respectively, enabling the detection of weaker target signals and laying the foundation for target localization and recognition applications. Full article

Pansori, a traditional Korean form of musical storytelling, is characterized by performances involving a vocalist and a drummer. It is well-known for the singer’s expressive narrative (aniri) and delicate gesture with fan in hand. The classical Pansori repertoires mostly tell love, satire, and humor, as well as some social lessons. These performances, which can extend from three to five hours, necessitate that the vocalist adheres to precise rhythmic structures. The distinctive rhythms of Pansori are crucial for conveying both the narrative and musical expression effectively. This paper explores the challenge of open-set recognition, aiming to efficiently identify unknown Pansori rhythm patterns while applying the methodology to diverse acoustic datasets, such as sound events and genres. We propose a lightweight deep learning-based encoder–decoder segmentation model, which employs a 2-D log-Mel spectrogram as input for the encoder and produces a frame-based 1-D decision along the temporal axis. This segmentation approach, processing 2-D inputs to classify frame-wise rhythm patterns, proves effective in detecting unknown patterns within time-varying sound streams encountered in daily life. Throughout the training phase, both center and supervised contrastive losses, along with cross-entropy loss, are minimized. This strategy aimed to create a compact cluster structure within the feature space for known classes, thereby facilitating the recognition of unknown rhythm patterns by allocating ample space for their placement within the embedded feature space. Comprehensive experiments utilizing various datasets—including Pansori rhythm patterns (91.8%), synthetic datasets of instrument sounds (95.1%), music genres (76.9%), and sound datasets from DCASE challenges (73.0%)—demonstrate the efficacy of our proposed method to detect unknown events, as evidenced by the AUROC metrics. Full article

Quality prediction in additive manufacturing (AM) processes is crucial, particularly in high-risk manufacturing sectors like aerospace, biomedicals, and automotive. Acoustic sensors have emerged as valuable tools for detecting variations in print patterns by analyzing signatures and extracting distinctive features. This study focuses on the collection, preprocessing, and analysis of acoustic data streams from a Fused Deposition Modeling (FDM) 3D-printed sample cube (10 mm × 10 mm × 5 mm). Time and frequency-domain features were extracted at 10-s intervals at varying layer thicknesses. The audio samples were preprocessed using the Harmonic–Percussive Source Separation (HPSS) method, and the analysis of time and frequency features was performed using the Librosa module. Feature importance analysis was conducted, and machine learning (ML) prediction was implemented using eight different classifier algorithms (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Trees (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM)) for the classification of print quality based on the labeled datasets. Three-dimensional-printed samples with varying layer thicknesses, representing two print quality levels, were used to generate audio samples. The extracted spectral features from these audio samples served as input variables for the supervised ML algorithms to predict print quality. The investigation revealed that the mean of the spectral flatness, spectral centroid, power spectral density, and RMS energy were the most critical acoustic features. Prediction metrics, including accuracy scores, F-1 scores, recall, precision, and ROC/AUC, were utilized to evaluate the models. The extreme gradient boosting algorithm stood out as the top model, attaining a prediction accuracy of 91.3%, precision of 88.8%, recall of 92.9%, F-1 score of 90.8%, and AUC of 96.3%. This research lays the foundation for acoustic based quality prediction and control of 3D printed parts using Fused Deposition Modeling and can be extended to other additive manufacturing techniques. Full article

(1) Background: This study aimed to evaluate how laser-activated irrigation (LAI) influences the retention of a fiber post when used before an endodontic filling, as well as after post space preparation. (2) Materials and Methods: Sixty freshly extracted human incisors were selected. The teeth were randomly assigned to three groups—CONVENTIONAL (CONV), PIPS or SWEEPS—and treated endodontically. Each group received irrigation with 1 × 5 mL EDTA (17%) and 3 × 5 mL NaOCl (5.25%). In the first group, the irrigants were not activated, while in the second and third group, LAI was adopted using PIPS and SWEEPS protocols (Lightwalker from Fotona, Ljubliana, Slovenia). After post space preparation, each group received the same irrigation protocol initially established. Sticky posts (everStick Post, GC AUSTRIA GmbH Swiss) were individually adapted to the corresponding post spaces and cemented using dual cure resin cement (Gradia Core, GC Austria GmbH Swiss). All specimens were vertically embedded into self-curing acrylate (Duracryl plus, Spofa Dent, Europe), and each was sectioned into three segments of type A and type B samples for debonding through push-out and pull-out tests. The results were statistically analyzed. (3) Results: The pull-out test showed the superiority of the SWEEPS group, with a mean fracture force of 133.0 ± 50.7 N, followed by the PIPS group, with 102 N, with a lower standard deviation of ± 34.5 N. The CONV group registered the lowest fracture force. Concerning the push-out test, the SWEEPS group showed superior shear stress in comparison to the other two groups (13.45 ± 4.29 MPa); the CONV group was inferior, with shear tension values of 8.31 ± 4.67 MPa. (4) Conclusions: It can be stated that the SWEEPS and PIPS protocols resulted in considerably higher fiber post retention than the conventional method, whereas the SWEEPS protocol was superior to the PIPS protocol. Full article

Multimodal sentiment analysis, a significant challenge in artificial intelligence, necessitates the integration of various data modalities for accurate human emotion interpretation. This study introduces the Advanced Multimodal Sentiment Analysis with Enhanced Contextual Fusion and Robustness (AMSA-ECFR) framework, addressing the critical challenge of data sparsity in multimodal sentiment analysis. The main components of the proposed approach include a Transformer-based model employing BERT for deep semantic analysis of textual data, coupled with a Long Short-Term Memory (LSTM) network for encoding temporal acoustic features. Innovations in AMSA-ECFR encompass advanced feature encoding for temporal dynamics and an adaptive attention-based model for efficient cross-modal integration, achieving symmetry in the fusion and alignment of asynchronous multimodal data streams. Additionally, the framework employs generative models for intelligent approximation of missing features. It ensures robust alignment of high-level features with multimodal data context, effectively tackling issues of incomplete or noisy inputs. In simulation studies, the AMSA-ECFR model demonstrated superior performance against existing approaches. The symmetrical approach to feature integration and data alignment contributed significantly to the model’s robustness and precision. In simulations, the AMSA-ECFR model demonstrated a 10% higher accuracy and a 15% lower mean absolute error than the current best multimodal sentiment analysis frameworks. Full article

The diffusion of Multimodal Large Language Models (MLLMs) has opened new research directions in the context of video content understanding and classification. Emotion recognition from videos aims to automatically detect human emotions such as anxiety and fear. It requires deeply elaborating multiple data modalities, including acoustic and visual streams. State-of-the-art approaches leverage transformer-based architectures to combine multimodal sources. However, the impressive performance of MLLMs in content retrieval and generation offers new opportunities to extend the capabilities of existing emotion recognizers. This paper explores the performance of MLLMs in the emotion recognition task in a zero-shot learning setting. Furthermore, it presents a state-of-the-art architecture extension based on MLLM content reformulation. The performance achieved on the Hume-Reaction benchmark shows that MLLMs are still unable to outperform the state-of-the-art average performance but, notably, are more effective than traditional transformers in recognizing emotions with an intensity that deviates from the average of the samples. Full article