Search Results (399)

Histotripsy is a novel, noninvasive, non-thermal technology invented in 2004 for the precise destruction of biologic tissue. It offers a powerful alternative to more conventional thermal or surgical interventions. Using short-pulse, low-duty cycle ultrasonic waves, histotripsy creates cavitation bubble clouds that selectively and precisely destroy targeted tissue in a predefined volume while sparing critical structures like bile ducts, ureters, and blood vessels. Such precision is of value when treating tumors near vital structures. The FDA has cleared histotripsy for the treatment of all liver tumors. Major medical centers are currently spearheading clinical trials, and some institutions have already integrated the technology into patient care. Histotripsy is now being studied for a host of other cancers, including primary kidney and pancreatic tumors. Preclinical murine and porcine models have already revealed promising outcomes. One of histotripsy’s primary advantages is its non-thermal mechanical actuation. This feature allows it to circumvent the limitations of heat-based techniques, including the heat sink effect and unpredictable treatment margins near sensitive tissues. In addition to its non-invasive ablative capacities, it is being preliminarily explored for its potential to induce immunomodulation and promote abscopal inhibition of distant, untreated tumors through CD8+ T cell responses. Thus, it may provide a multilayered therapeutic effect in the treatment of cancer. Histotripsy has the potential to improve precision and outcomes across a multitude of specialties, from oncology to cardiovascular medicine. Continued trials are crucial to further expand its applications and validate its long-term efficacy. Due to the speed of recent developments, the goal of this review is to provide a comprehensive and updated overview of histotripsy. It will explore its physics-based mechanisms, differentiating it from similar technologies, discuss its clinical applications, and examine its advantages, limitations, and future. Full article

Ultrasonic nondestructive testing has been widely used in various industries due to its simple operation and harmlessness for the object to be detected. However, due to the mechanism of ultrasonic image generation, the generated ultrasonic images often have low resolution, which greatly affects the final detection results. How to improve the resolution of ultrasonic images has become the key to improving the accuracy of defect detection. Therefore, this paper proposes an ultrasonic super-resolution model based on up- and down-sampling layers and multi-layer residual networks combined with Charbonnier loss function. The degradation features of the image are learned through up- and down-sampling layers, and the intrinsic features of the image are learned through multi-layer residual networks, so that all the feature information of the image is fully learned. The Charbonnier loss function accelerates the convergence of the model. Experimental results show that the model proposed in this paper outperforms the common model performance. Full article

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article

The development of materials based on chitosan and polyesters that possess thermoplastic, biocompatible, and biodegradable properties is a perspective for additive technologies in biomedicine. Research on obtaining such compositions is constrained because the polysaccharide content does not exceed 5 wt.%, which cannot ensure effective tissue regeneration. Herein, we propose a method for obtaining thermoplastic block copolymers based on chitosan and poly(ε-caprolactone) by ultrasonic irradiation of a homogeneous solution of a homopolymer mixture in dimethyl sulfoxide as a common solvent, achieving a yield of 99%. The distinctive feature of the method is the interaction between the components at the molecular level and provides obtaining copolymers at any component ratio. SEM images revealed a homogeneous structure without structural defects in both solvent-cast films and extruded filaments. The block copolymers were characterized by high mechanical property tensile strength of up to 60–70 MPa and elasticity of up to 35% for films and 25–40 MPa and elasticity of up to 50% for filaments. Cell adhesion of composition investigated on fibroblast cells (hTERT BJ-5TA) is at the level of chitosan and demonstrated the absence of cytotoxicity. Full article

Current non-destructive testing (NDT) methods, such as those based on wave velocity measurements, lack the sensitivity necessary to detect early-stage damage in concrete structures. Similarly, common signal processing techniques often assume linearity and stationarity among the signal data. By analyzing wave attenuation measurements using advanced signal processing techniques, mainly Hilbert–Huang transform (HHT), this work aims to enhance the early detection of damage in concrete. This study presents a novel energy-based technique that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Hilbert spectrum analysis (HSA), to accurately capture nonlinear and nonstationary signal behaviors. Ultrasonic non-destructive testing was performed in this study on manufactured concrete specimens subjected to micro-damage characterized by internal microcracks smaller than 0.5 mm, induced through controlled freeze–thaw cycles. The recorded signals were decomposed from the time domain using CEEMDAN into frequency-ordered intrinsic mode functions (IMFs). A multi-criteria selection strategy, including damage index evaluation, was employed to identify the most effective IMFs while distinguishing true damage-induced energy loss from spurious nonlinear artifacts or noise. Localized damage was then analyzed in the frequency domain using HSA, achieving an up to 88% reduction in wave energy via Marginal Hilbert Spectrum analysis, compared to 68% using Fourier-based techniques, demonstrating a 20% improvement in sensitivity. The results indicate that the proposed technique enhances early damage detection through wave attenuation analysis and offers a superior ability to handle nonlinear, nonstationary signals. The Hilbert Spectrum provided a higher time-frequency resolution, enabling clearer identification of damage-related features. These findings highlight the potential of CEEMDAN-HSA as a practical, sensitive tool for early-stage microcrack detection in concrete. Full article

The poor interfacial adhesion between ductile gold (Au) electrodes and polydimethylsiloxane (PDMS) substrates affects their application in flexible sensors. Here, a porous Au electrode is designed and combined with a flexible PDMS substrate to form a structure that embeds Au into the PDMS film, thereby enhancing the interfacial adhesion of the Au/PDMS electrode. The resistivity change of the Au/PDMS electrode is only 12.3% after 100 tape peeling trials. The resistance of the Au/PDMS electrode remains stable at the 30% strain level after 2000 tensile cycling tests. This feature is mainly attributed to the deformation buffering effect of the porous Au film. After 100 min of ultrasonic oscillation testing, the resistivity change of the Au/PDMS electrode remains stable. It is also shown that the Au/PDMS electrode has excellent interfacial adhesion properties, which is mainly attributed to the interlocking effect of the Au/PDMS electrode structure. In addition, the temperature coefficient of resistance (TCR) of the temperature sensor based on the Au/PDMS electrode is approximately 0.00320/°C and the sensor’s sensitivity remains almost stable after 200 temperature measurement cycles. Au/PDMS electrodes have great potential for a wide range of applications in flexible electronics due to their excellent interfacial adhesion and electrical stability. Full article

Conventional filters for air filtration typically feature compact nonwoven structures, which not only lead to high pressure drop, significant energy consumption, and a decay in filtration efficacy, but are also uncleanable, resulting in substantial pollution upon disposal. In this study, filters with high-voltage electrostatic loading capability were developed with a dopamine binding layer to facilitate the establishment of an Ag conductive layer on the surface of ultraloose woven structure fabrics (pore size: 73.7 μm). The high-voltage-loaded woven structure filtration (VLWF) system was constructed with a negative-ion zone, a high-voltage filtration zone, and a grounded filter. The morphological, chemical, and electrical properties of the filters and the filtration performance of the VLWF system were evaluated. The single-pass filtration efficiencies for PM_2.5 and E. coli were 67.4% and 97.0%, respectively. Notably, the pressure drop was reduced to 6.2 Pa, and the quality factor reached 0.1810 Pa⁻¹ with no detectable ozone release. After three cycles of ultrasonic cleaning, approximately 58.4% of filtration efficiency was maintained without any increase in air resistance. The removal of PM_2.5 and microorganisms by this system was not solely reliant on blocking and electrostatic attraction but may also involve induced repulsion and biostructure inactivation. By integrating the ultraloose woven structure with high-voltage assistance, this VLWF system effectively balanced the requirements for high filtration efficacy and low air resistance. More importantly, this VLWF system provided a cleanable filter model that reduced the pollution associated with conventional disposable filters and lowered costs for customers. Full article

Algal polysaccharides are a kind of bioactive compound with diverse pharmacological applications, yet their structure–activity relationships and therapeutic potential in chronic disease management remain systematically underexplored. This review comprehensively analyzes the structural characteristics of brown, red, and green algal polysaccharides, revealing how specific structural features—such as glycosidic linkage patterns and sulfate group positioning—dictate their biological activities. We also demonstrated their multifaceted roles in diabetes, cancer, and cardiovascular diseases through distinct mechanisms, including gut microbiota modulation via short-chain fatty acid production, antioxidant enzyme activation, and targeted inhibition of pathological signaling pathways like mTOR and JAK-STAT3. The work further evaluates extraction methodologies, highlighting the advantages of emerging techniques such as enzyme-assisted and ultrasonic extraction for preserving bioactive integrity. By integrating fundamental research with practical applications in functional foods, this synthesis provides critical insights for harnessing algal polysaccharides in precision nutrition and sustainable biomedicine, while identifying key challenges in standardization and environmental safety that warrant future investigation. Full article

The application of rebar reinforced ultra-high-performance concrete (R-UHPC) has been increasingly adopted in engineering structures due to its exceptional mechanical performance and durability characteristics. Nevertheless, when subjected to combined saline and stray current conditions, R-UHPC remains vulnerable to severe corrosion degradation. This investigation examined the corrosion performance and tensile behavior evolution of R-UHPC containing 2.0 vol% copper-coated steel fiber content and HRB400 steel rebar with a reinforcement ratio of 3.1%. The accelerated corrosion process was induced through an impressed current method, followed by direct tensile tests at varying exposure periods. The findings revealed that the embedding of rebar in UHPC led to the formation of fiber-to-rebar (F-R) conductive pathways, generating radial cracks besides laminar cracks. The bonding between rebar and UHPC degraded as corrosion progressed, leading to the loss of characteristic multiple-cracking behavior of R-UHPC in tension. Meanwhile, R-UHPC load-bearing capacity, transitioning from gradual to accelerated deterioration phases with prolonged corrosion, aligns with steel fibers temporally. During the initial 4 days of corrosion, the specimens displayed surface-level corrosion features with negligible steel fiber loss, showing less than 4.0% reduction in ultimate bearing capacity. At 8 days of corrosion, the steel fiber decreased by 22.6%, accompanied by an 18.3% reduction in bearing capacity. By 16 days of corrosion, the steel fiber loss reached 41.5%, with a corresponding bearing capacity reduction of 29.1%. During the corrosion process, corrosion cracks and load-bearing degradation in R-UHPC could be indicated by the ultrasonic damage factor. Full article

Automation of the structural health monitoring process involves the use of successful methods for detecting defects and determining their critical characteristics. An efficient means of crack detection in composite materials is the ultrasonic method, but its application to determine critical crack parameters, such as depth in construction practice, is difficult or leads to large errors. This article focuses on machine learning methods usage to detect cracks in composite materials like brickwork. Ceramic bricks with various mechanical properties and with pre-grown cracks from 2 to 60 mm are considered. To understand the processes occurring during the ultrasonic pulse transmission, modeling was performed in the ANSYS environment. The brick is considered a porous medium weakened by a crack. Numerical modeling allows for the identification of the main features of the signal response and the determination of the amplitude-time range for different porosity and crack depth values. Using machine learning methods made it possible to solve two related problems. The first, binary classification, i.e., the presence or absence of a crack, is solved with 100% accuracy. The second is determining the crack depth. A neural network was built using an ensemble of decision trees. The accuracy of crack depth prediction is R² = 0.983, and the error in predicted values is within 8%. Full article

Ultrasonic applications such as non-destructive testing, biomedical imaging or range measurements are currently based on piezoelectric bulk transducers. Yet, these kinds of transducers with their mm to cm dimensions are rather impractical in fields in which both frequencies in the kHz region and small-feature sizes are required. This fact mainly relates to the inverse relationship between the resonance frequency constant and the transducers’ dimension, yielding a higher frequency and attenuation with a decreasing size. Piezoelectric micromachined ultrasonic transducers (PMUTs), in comparison, incorporate a small-scale µm design while preserving the operating frequency in the desired kHz range. This contribution presents the detailed manufacturing of such a PMUT with a multiple ring electrode‑based structure to additionally adjust the sound pressure fields. The PMUT will be characterized by its deflection in air along with the characterization of the piezoelectric material lead zirconate titanate (PZT) itself. The measurements showed a maximum polarization of 21.8 µC/cm² at 50 kV/cm, the PMUT’s displacement of 30.50 nm/V in air when all electrodes are driven, and an adjustable deflection via different electrode excitations without the need for additional hardware. The ring design also offered the possibility to emit two distinct frequencies simultaneously. These results demonstrate the potential of the designs for small-feature-size applications as they are in high demand for implantable devices, miniaturized ultrasonic-based communication or drug delivery. Full article

Speech recognition in noisy environments has long posed a challenge. Air conduction microphone (ACM), the devices typically used, are susceptible to environmental noise. In this work, a customized bone conduction microphone (BCM) system based on a piezoelectric micromachined ultrasonic transducer is developed to capture speech through real-time bone conduction (BC), while a commercial ACM is integrated for simultaneous capture of speech through air conduction (AC). The system enables simpler and more robust BC speech capture. The BC speech capture achieves a signal-to-noise amplitude ratio over five times greater than that of AC speech capture in an environment with a noise level of 68 dB. Instead of using only AC-captured speech, both BC- and AC-captured speech are input into a speech enhancement module. The noise-insensitive BC-captured speech serves as a speech reference to adapt the SE backbone of AC-captured speech. The two types of speech are fused, and noise suppression is applied to generate enhanced speech. Compared with the original noisy speech, the enhanced speech achieves a character error rate reduction of over 20%, approaching the speech recognition accuracy of clean speech. The results indicate that this speech enhancement method based on the fusion of BC- and AC-captured speech efficiently integrates the features of both types of speech, thereby improving speech recognition accuracy in noisy environments. This work presents an innovative system designed to efficiently capture BC speech and enhance speech recognition in noisy environments. Full article

Soil porosity, as an essential indicator for assessing soil quality, plays a key role in guiding agricultural production, so it is beneficial to detect soil porosity. However, the currently available methods do not apply to high-precision and rapid detection of soil with a black-box nature in the field, so this paper proposes a soil porosity detection method based on ultrasound and multi-scale CNN-LSTM. Firstly, a series of ring cutter soil samples with different porosities were prepared manually to simulate soil collected in the field using a ring cutter, followed by ultrasonic signal acquisition of the soil samples. The acquired signals were subjected to three kinds of data augmentation processes to enrich the dataset: adding Gaussian white noise, time shift transformation, and random perturbation. Since the collected ultrasonic signals belong to long-time series data and there are different frequency and sequence features, this study constructs a multi-scale CNN-LSTM deep neural network model using large convolution kernels based on the idea of multi-scale feature extraction, which uses multiple large convolution kernels of different sizes to downsize the collected ultra-long time series data and extract local features in the sequences, and combining the ability of LSTM to capture global and long-term dependent features enhances the feature expression ability of the model. The multi-head self-attention mechanism is added at the end of the model to infer the before-and-after relationship of the sequence data to improve the degradation of the model performance caused by waveform distortion. Finally, the model was trained, validated, and tested using ultrasonic signal data collected from soil samples to demonstrate the accuracy of the detection method. The model has a coefficient of determination of 0.9990 for detecting soil porosity, with a percentage root mean square error of only 0.66%. It outperforms other advanced comparative models, making it very promising for application. Full article

The structural and functional characteristics of oil body proteins (OBPs) isolated from hemp, plum, and jujube seeds were systematically investigated, along with their potential application in constructing curcumin-loaded artificial oleosomes (AOs). OBPs were extracted through alkaline extraction coupled with ultrasonic disruption, followed by comprehensive physicochemical characterization using SDS-PAGE, FTIR spectroscopy, fluorescence spectroscopy, and evaluation of particle size, zeta potential, surface hydrophobicity, solubility, thermal stability, and emulsification properties. Plum seed-derived OBPs were found to demonstrate superior emulsifying capacity and solubility, which were attributed to distinctive structural features, including the following: an elevated random coil content (13%), enhanced surface hydrophobicity (21,781 A.U.), reduced particle size (103 nm), and higher zeta potential (−46 mV). These structural advantages were correlated with improved interfacial adsorption capacity and colloidal stability. When employed in AO fabrication, plum seed OBPs produced curcumin-loaded systems exhibiting maximum encapsulation efficiency (92%), minimal droplet size (5.99 μm), and optimal bio-accessibility (50%) compared to their hemp- and jujube-based counterparts. Furthermore, AOs utilizing plum seed OBPs displayed enhanced antioxidant activity and significantly improved stability. The collective findings establish plum seed OBPs as exceptional natural emulsifiers with strong potential for bioactive compound delivery applications. Full article

Due to accelerated urbanization, modern urban residents are facing increasing life pressures. Many citizens are experiencing situational aversion in daily commuting, and the deterioration in the traffic environment has led to psychological distress of varying degrees among urban dwellers. Cyclists, who account for about 7% of urban commuters, lack a sense of belonging in the urban space and experience significant deficiencies in the corresponding urban infrastructure, which causes more people to face significant barriers to choosing cycling as a mode of transportation. To address the aforementioned issues, this study proposes a bionic intelligent interaction helmet (BIIH) designed and validated based on the principles of bionics, which has undergone morphological design and structural validation. Constructed around the STM32-embedded development board, the BIIH is an integrated smart cycling helmet engineered to perceive environmental conditions and enable both human–machine interactions and environment–machine interactions. The system incorporates an array of sophisticated electronic components, including temperature and humidity sensors; ultrasonic sensors; ambient light sensors; voice recognition modules; cooling fans; LED indicators; and OLED displays. Additionally, the device is equipped with a mobile power supply, enhancing its portability and ensuring operational efficacy under dynamic conditions. Compared with conventional helmets designed for analogous purposes, the BIIH offers four distinct advantages. Firstly, it enhances the wearer’s environmental perception, thereby improving safety during operation. Secondly, it incorporates a real-time interaction function that optimizes the cycling experience while mitigating psychological stress. Thirdly, validated through bionic design principles, the BIIH exhibits increased specific stiffness, enhancing its structural integrity. Finally, the device’s integrated power and storage capabilities render it portable, autonomous, and adaptable, facilitating iterative improvements and fostering self-sustained development. Collectively, these features establish the BIIH as a methodological and technical foundation for exploring novel research scenarios and prospective applications. Full article