Search Results (329)

Brittle geomaterials such as coal and rock are prone to unstable failure under high stress and dynamic disturbances, where rapid release of stored elastic strain energy can trigger dynamic disasters. Polyurea, a high-strength and high-ductility elastomer, can form a continuous flexible coating on the surface of coal/rock to regulate their deformation–fracture behavior. Here, uniaxial compression tests were performed on coal specimens coated with polyurea layers of different thicknesses (0–1.25 mm). Acoustic emission (AE) and digital image correlation (DIC) were jointly employed to characterize macroscopic deformation, microcrack evolution, fracture-mode transition, and energy partitioning. The results show that polyurea provides passive lateral confinement that suppresses lateral expansion and shifts macroscopic failure from brittle splitting to progressive ductile damage. AE-based AF–RA analysis indicates that thicker coatings increase the normal stress and shear resistance along potential fracture planes, promoting a microfracture transition from shear-dominated to tension-dominated cracking. Energy analysis demonstrates that the coating enhances pre-peak energy dissipation via coordinated deformation with the coal, while thicker coatings (≥1.00 mm) exhibit pronounced post-peak elastic tensile deformation to absorb and buffer fracture-released energy, impeding the instantaneous energy release typical of bare coal. Moreover, the elastic energy index shows that polyurea markedly reduces impact tendency, with an appropriate thickness stabilizing specimens from strong to weak/non-impact propensity. These findings clarify the coupled confinement–fracture–energy regulation mechanisms of polyurea coatings and provide quantitative guidance for coating-thickness design to mitigate dynamic failure hazards in brittle materials. Full article

Carotid artery disease has traditionally been assessed according to luminal stenosis, although plaques with similar narrowing may differ substantially in biological activity and clinical risk. Intraplaque neovascularization is a key feature of plaque vulnerability, reflecting microvascular proliferation and its association with inflammation, hemorrhage, and structural destabilization. Dynamic contrast-enhanced ultrasound (DCE-US) offers a real-time, radiation-free method for evaluating intraplaque enhancement kinetics using strictly intravascular microbubble agents. However, its broader use in carotid plaque imaging remains limited by variability in acquisition protocols, contrast administration, signal processing, curve fitting, and parameter interpretation. This technical review clarifies the main analytical approaches used in carotid DCE-US, distinguishing bolus-based wash-in/wash-out analysis from destruction–replenishment modeling. Bolus analysis describes first-pass microbubble transit through the plaque microvasculature and commonly provides parameters such as peak intensity, wash-in slope, area under the curve, and time to peak. Destruction–replenishment analysis evaluates post-destruction refill under stable or quasi-stable contrast conditions and relies on model-based estimation of plateau intensity and the replenishment rate. Because these approaches interrogate different kinetic regimes, their outputs should not be considered interchangeable, even when similar terms are used across studies. Particular emphasis is placed on the operational meaning of quantitative and semi-quantitative parameters, the assumptions underlying curve modeling, and the methodological consequences of ROI placement, motion correction, acoustic settings, and fitting constraints. Rather than proposing a universal acquisition protocol, this article provides practical principles for acquisition, analysis, and reporting, helping radiologists, neuroradiologists, neurologists, and vascular imaging specialists understand the processing steps, algorithmic assumptions, and model-dependent choices underlying software-derived curves and parameters. By making this analytical layer more explicit, the review seeks to support a transparent, reproducible, and biologically coherent approach to quantitative carotid plaque characterization. Full article

The thermal shock resistance (TSR) of refractories is a critical determinant of the service life and operational safety of high-temperature industrial equipment in metallurgy, building materials, and chemical engineering. This paper systematically reviews the state-of-the-art research on the evaluation of TSR for refractories. On the theoretical level, the evolutionary logic from classical thermoelastic theory to energy-based damage theory, brittleness evaluation criteria, and the dimensional analysis-based R_Π theory is delineated, with a comparative analysis of the applicability of various criteria in dense versus porous material systems. Regarding evaluation methodologies, the strengths and limitations of conventional thermal cycling tests, splitting tests (notably Brazilian and wedge splitting), and specialized techniques such as ultrasonic pulsing and nano-indentation are scrutinized. Furthermore, the application of non-destructive monitoring technologies, such as Digital Image Correlation (DIC) and Acoustic Emission (AE), for in-situ damage capture is discussed. Additionally, the potential of machine learning in performance prediction and inverse material design is explored. Finally, it is posited that future research should focus on promoting the development of multiscale, standardized, and intelligent evaluation frameworks to meet the requirements of harsh operating environments in emerging fields such as green metallurgy. Full article

Extreme ultraviolet (EUV) thin-film filters are key optical components in space-based EUV imaging, employed to reject out-of-band visible and ultraviolet radiation. Currently, aluminum (Al) and zirconium (Zr) EUV filters are predominantly used due to their superior mechanical strength and stability. In contrast, indium (In) EUV filters had not been successfully deployed in spaceborne EUV cameras prior to this work. Their inherent fragility makes them highly susceptible to vibration and acoustic loads during the launch phase, ultimately resulting in structural failure. This study presents a comprehensive investigation into the structural protection strategies for indium EUV filters in space applications. Through systematic analysis of the photoenergy requirements and the mechanical characteristics of the indium filters, a robust filter assembly was developed, and vibration isolation as well as acoustic mitigation designs were implemented for the assembly. Finite element simulations and environmental tests confirmed that the indium filters can withstand vibration and acoustic loads. This technology has been successfully implemented in the 83.4 nm channel of the Extreme Ultraviolet Camera (EUVC) onboard the Queqiao-2 relay satellite for the Chang’E-7 mission. Subsequent in-orbit tests validated the structural integrity of the indium filters, providing a valuable technical reference for their successful application in future spaceborne EUV cameras. Full article

Spain’s extensive marine jurisdiction—comprising a continental shelf of approximately 100,000 km² and an Exclusive Economic Zone approaching one million km²—requires robust geospatial frameworks to support ecosystem assessment and marine policy implementation. This study presents GIS-based methodologies developed by the Spanish Oceanographic Institute (IEO-CSIC) within national initiatives such as LIFE IP INTEMARES project and the implementation of Marine Strategy Framework Directive (European Directive 2008/56/EC). The geospatial workflows developed for these initiatives integrates heterogeneous spatial datasets—such as multibeam bathymetry, acoustic backscatter, Remote Operated Vehicle (ROV) and towed-camera transects, sediment samples, oceanographic profiles, and species-habitat occurrence records—into a unified spatial analysis environment. Applied methods include digital terrain modeling, derivation of geomorphometric indices (e.g., slope, rugosity, curvature), image classification, and spatial statistics to quantify habitat extent, condition, and anthropogenic pressures. An integrated spatial analysis framework combining environmental and anthropogenic data is used to support zoning and management decisions within Marine Protected Areas (MPAs). Additionally, the deployment of WebGIS platforms facilitates data dissemination, iterative review, and stakeholder engagement, thereby enhancing transparency and accessibility. The resulting high-resolution maps, harmonized datasets, and computed spatial indicators—aligned with Marine Strategy Framework Directive (MSFD) descriptors such as habitat distribution (D1C4–C5) and seafloor integrity (D6C2–C3)—demonstrate how GIScience methods provide reproducible, decision-ready information to support the monitoring and management of Spain’s diverse marine ecosystems. Full article

Early pregnancy diagnosis is a key component of reproductive management in swine production systems. Accurate identification of pregnant and non-pregnant sows within the first 30 days after insemination allows timely reproductive decisions and reduces non-productive days. The present systematic review evaluates the diagnostic efficiency of ultrasonographic and progesterone-based methods used for early detection of pregnancy in sows. A structured literature search was conducted in accordance with the PRISMA Statement guidelines, using major scientific databases. Studies evaluating pregnancy diagnosis in sows within the first 30 days after insemination were included. Diagnostic approaches were analyzed with respect to methodological design, timing of examination, biological sample matrix, and reported indicators of diagnostic accuracy. Ultrasonographic techniques have evolved from early acoustic detection in A-mode to real-time imaging in B-mode and more recently algorithm-assisted interpretation of ultrasound images. Real-time ultrasonography allows direct visualization of gestational structures; in one study, diagnostic accuracy above 95% was reported after approximately 23–24 days of pregnancy under optimal examination conditions. Progesterone-based analyses evaluate luteal endocrine activity and are particularly useful for early identification of non-pregnant animals after luteolysis. The diagnostic efficiency of hormonal assays depends strongly on the timing of sampling and the biological matrix used for analysis, including plasma, serum, dried blood spots, saliva, or feces. The comparative analysis shows that ultrasonography provides morphological confirmation of pregnancy, whereas progesterone analyses serve mainly as functional indicators of luteal activity. These methods play complementary roles in reproductive management. Ultrasonography remains the most reliable method for confirming pregnancy, while progesterone-based analyses are valuable tools for early reproductive screening and identification of non-pregnant sows. Full article

Understanding the dynamics of gas bubbles in viscoelastic media is crucial for applications involving stable cavitation under ultrasound, such as drug delivery, materials processing, and biomedical imaging. The Rayleigh-Plesset equation formulated in terms of bubble volume variation, incorporating viscoelastic effects via the linear Kelvin–Voigt model, is extended here to a fourth-order approximation. This formulation allows a more accurate description of nonlinear bubble dynamics at finite acoustic amplitudes. The resulting equation is solved numerically under various acoustic conditions, with particular emphasis on driving frequencies near the bubble’s resonance and differences between Newtonian and viscoelastic media. To identify the physical conditions under which higher-order nonlinearities become necessary, a decision-tree classification analysis is performed. The results show that the proximity to resonance and the excitation amplitude are the primary determinants of higher-order nonlinear effects, while rheological properties act as modulators, with viscosity exerting a stronger influence than elasticity within the explored ranges. This work provides a physically interpretable criterion for selecting the appropriate model order, improving the prediction and control of nonlinear bubble oscillations under ultrasound excitation in viscoelastic media. Full article

Ultrasound coupling technology is pivotal to ensuring high-quality diagnostic imaging, yet conventional water-based gels face persistent challenges, including acoustic impedance mismatch, air-bubble formation, dehydration, messiness, and cross-contamination risks. This review presents a comprehensive analysis of the evolution, materials science, and clinical performance of ultrasound gel pads, an advanced alternative engineered for superior acoustic transmission, hygiene, and patient comfort. Historical progression from early coupling agents to modern polymeric and hydrogel-based pads is traced, highlighting breakthroughs such as bilayer hydrogels, nanocomposite reinforcements, metamaterial-inspired designs, and patient-specific 3D-printed pads. Comparative evaluations demonstrate that gel pads, particularly those integrating nanotechnology, rival but often outperform traditional gels in transmission efficiency, near-field resolution, and adaptability to complex anatomical surfaces, while offering reusability and reduced environmental impact. For instance, solid gel pads achieved 92.3% stone disintegration, compared with 45.5% for semi-liquid gel, in ESWL phantom studies (p < 0.001). Materials, including polyacrylamide, silicone, and advanced hydrogels, are analyzed for mechanical properties, biocompatibility, and sustainability, with emphasis on biodegradable and locally sourced alternatives. Manufacturing innovations ranging from continuous casting to additive manufacturing enable customization, functional integration, and scalable production, although cost, supply chain stability, and regulatory compliance remain critical barriers. By uniting advances in materials engineering, nanotechnology, and precision manufacturing, ultrasound gel pads have demonstrated strong potential to advance coupling media for diagnostic, therapeutic, and wearable ultrasound applications, enabling higher diagnostic accuracy, streamlined workflows, and patient-centered care across diverse clinical and resource-limited settings. Full article

This review paper presents a comprehensive and system-oriented analysis of advanced non-destructive testing (NDT) technologies for metal additive manufacturing (AM), including X-ray computed tomography (XCT), ultrasonic testing (UT), infrared thermography, acoustic emission (AE), and electromagnetic techniques. While the existing literature often focuses on the physical principles of individual NDT methods, this work addresses a critical knowledge gap by analyzing NDT as a digitally integrated “quality intelligence layer” rather than a standalone post-process inspection tool. The primary motivation is to bridge the disconnect between raw inspection data and cyber–physical production systems. Particular focus is given to NDT data analytics and digitalization, where machine learning (ML) and digital twin (DT) integration are discussed as fundamental enablers of intelligent manufacturing. The review systematically examines image and signal processing pipelines required for quantitative defect characterization, highlighting challenges related to voxel resolution, signal-to-noise ratio, anisotropic microstructures, and operator dependency. It further analyzes supervised learning, deep learning, and multi-sensor data fusion approaches for automated defect classification and predictive quality assessment. Furthermore, the role of digital twins in coupling in situ monitoring data, ex situ NDT results, and physics-based models is discussed as a transformative pathway toward closed-loop process control and evidence-based certification. By synthesizing NDT science with digital manufacturing architectures, this review contributes a unique framework for transitioning from traditional inspection-centric quality control to a predictive, adaptive, and digital twin-enabled quality assurance paradigm. The work concludes by identifying key research gaps in data standardization and computational scalability, providing a strategic roadmap for the future of smart AM production. Full article

Additive manufacturing (AM) has emerged as a transformative technology across multiple industries, from aerospace to biomedical applications. The integration of artificial intelligence (AI) and machine learning (ML) into AM processes represents a paradigm shift toward intelligent, autonomous manufacturing systems. This comprehensive review synthesizes recent advances in AI/ML applications across the entire AM life cycle—from design optimization and process planning through in situ monitoring, closed-loop control, and post-process qualification. The analysis is organized by ISO/ASTM AM process families, including powder bed fusion (PBF), directed energy deposition (DED), material extrusion (MEX), vat photopolymerization (VP), binder jetting (BJ), material jetting (MJT), and sheet lamination (SL). For each process family, the review examines the specific AI/ML techniques employed, the data modalities utilized (thermal imaging, acoustic signals, in situ cameras, CT/NDE data), and the current state of deployment from research prototypes to industrial implementation. The analysis reveals that while significant progress has been made in single-stage ML applications such as defect detection and parameter optimization, truly integrated life cycle-wide AI-driven AM workflows remain largely aspirational. Key challenges are identified including data scarcity, model generalization across machines and materials, real-time control constraints, and certification requirements. Finally, future research directions are outlined toward autonomous AM systems enabled by physics-informed ML, digital twins, and hierarchical AI architectures. Full article

Background: In recent years, artificial intelligence (AI) methods, including deep convolutional neural networks (CNNs), have gained increasing importance in supporting the automated analysis of echocardiograms. The aim of this study was to evaluate the impact of selected image artifacts—motion blur, acoustic shadowing, and speckle noise—on the performance of automatic classification of standard transthoracic echocardiographic (TTE) views using deep learning models. Methods: The analysis included 217 TTE video clips (2170 frames) covering apical views: two-chamber (A2C), three-chamber (A3C), four-chamber (A4C), and five-chamber (A5C). Two convolutional neural network architectures—ResNet-18 and ResNet-34—were applied, initialized with weights pretrained on the ImageNet dataset (transfer learning). In a limited comparative scope, EfficientNet-B0, a ViT model used as a frozen feature extractor combined with Logistic Regression, and a classical HOG + SVM model, were also included as reference methods. Classification performance was evaluated under conditions of controlled image degradation caused by motion blur, acoustic shadowing, and speckle noise. Results: All analyzed artifacts reduced classification performance, although the magnitude of this effect depended on artifact type. Speckle noise proved to be the most destructive, causing performance collapse across all evaluated methods at high severity. Motion blur and acoustic shadowing produced more differentiated degradation profiles. The ResNet models achieved the highest performance under reference conditions; however, after degradation, the ranking of models was no longer stable. In the comparative analysis, HOG + SVM showed the smallest relative performance loss under motion blur and the highest balanced accuracy under severe acoustic shadowing, whereas severe speckle remained critical for all models. Conclusions: Image quality degradation significantly impairs TTE view classification performance, and evaluation based solely on reference-quality images does not fully reflect model robustness to artifacts. These findings indicate the need to complement standard model evaluation with a structured robustness analysis under degraded imaging conditions and highlight the importance of training and validation settings that better reflect real clinical practice. Full article

Capacitive micromechanical ultrasonic transducers (CMUTs), as microelectromechanical systems (MEMS) devices, have broad application prospects in ultrasonic imaging and sensing. This study investigates the influence of surface acoustic waves (SAWs) using periodically arranged CMUTs as the fundamental unit cells. We first utilize finite element analysis (FEA) to calculate and analyze the band structure and bandgap characteristics of phononic crystals under infinite periodic conditions. Subsequently, for finite periodic structures in practical applications, acoustic transmission spectra were further simulated using FEA to verify the bandgap characteristics of the structure for SAWs. Accordingly, this paper leverages a deep learning framework based on a multilayer perceptron (MLP) architecture to achieve the inverse design and optimization of CMUT geometric parameters, tailored to specific target bandgap requirements. The results demonstrate that this approach can efficiently and accurately determine the optimal structural configurations, offering a robust and novel technical paradigm for the precise control of SAWs using CMUT-based periodic arrays. Full article

In the study of sensory processes, the visual system has received the most research compared to other sensory systems. The primary difference between visual and auditory perception lies in the nature of the stimuli and the reception processes: vision perceives electromagnetic radiation, while auditory perception perceives acoustic signals of mechanical origin. This review aims to analyze modern approaches and controversies to the mechanisms of auditory perception related to psychophysics, psychophysiology, psychopathology, modern research on hearing in human–computer interaction (HCI) systems, and machine learning methods. Modern studies of acoustic patterns include a comprehensive assessment of the physical characteristics of perception, complex nonverbal auditory cues, verbalization, perception and memory, as well as individual differences in auditory perception. An analysis of the scientific literature allowed us to conclude that acoustic signals transformed in the brain into auditory images retain (encode) a number of amplitude-temporal parameters of acoustic signals that facilitate auditory discrimination (filtering), but interfere with auditory detection (recognition). Signal processing often, but not necessarily, involves brain regions involved in other forms of perception. It depends on subvocalization, includes semantically interpreted information and expectations, pictorial (visual) and descriptive components, functions as a mnemonic, and is linked to individual musical ability and experience (although the mechanisms of this connection are unclear). Full article

Posterior acoustic shadowing is a fundamental physical phenomenon associated with calcified stones in ultrasound image, yet it has not been fully exploited in automated ultrasound analysis. This study aimed to develop an explainable, semi-automatic rule-based framework that explicitly incorporates posterior acoustic shadow characteristics for stone detection and localization in a clinically guided manner. A rule-based framework was designed to generate stone candidates using morphological enhancement and to evaluate them through local contrast analysis, posterior shadow region assessment, and shape-based penalties. A composite score integrating these features was used to rank candidates. The method was evaluated on 52 kidney stone and 66 gallbladder stone ultrasound images, stratified into three diagnostic confidence categories. Performance was assessed using an ablation study and centroid distance error measured in pixels relative to expert-defined references. In the 50–60% confidence group, the accuracy increased from 0.29 to 0.64 for kidney stones and from 0.30 to 0.60 for gallbladder stones when posterior shadow information was included. Centroid distance errors in the ≥80% confidence group were 1.26 ± 0.28 mm for kidney stones and 1.44 ± 0.91 mm for gallbladder stones. The proposed framework enhances diagnostic confidence by leveraging physically grounded posterior acoustic shadow analysis and provides a reproducible augmentation to conventional ultrasound-based stone assessment. Full article

Multifrequency operation in micromachined ultrasonic transducers, enabled by targeted excitation of specific vibrational modes, has emerged as an attractive approach for achieving tunable performance and configurability, well-suited for advanced ultrasound imaging and therapeutic applications. This paper presents a dual-electrode rectangular piezoelectric micromachined ultrasonic transducer (PMUT) designed for efficient dual-frequency operation through mode-selective actuation. The proposed architecture employs segmented electrodes that are spatially aligned with the strain distributions of two distinct flexural modes, enabling selective excitation of Mode 1 (fundamental) and Mode 3 (higher order) through appropriate electrode actuation. Finite element simulations and impedance analysis were used to guide the electrode configuration and validate the mode-selective behavior. The dual-mode PMUT was fabricated alongside a conventional single-electrode PMUT using identical membrane dimensions and material stack for direct comparison. Comprehensive electrical and underwater acoustic characterization confirmed that the conventional PMUT is limited to single-frequency operation at the fundamental resonance. In contrast, the proposed design achieved a substantial improvement in higher-order performance, with a threefold increase in acoustic pressure at Mode 3 compared to the conventional device. These results demonstrate that mode-aligned electrode segmentation enables efficient dual-mode operation without added fabrication complexity, making the design highly suitable for multifrequency ultrasonic applications such as biomedical imaging and sensing. Full article