Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.
Integrating Multibeam Echosounder (MBES) data (bathymetry and backscatter) and underwater video technology allows scientists to study marine habitats. However, use of such data in modeling suitable seagrass habitats in Malaysian coastal waters is still limited. This study tested multiple spatial resolutions (1 and 50 m) and analysis window sizes (3 × 3, 9 × 9, and 21 × 21 cells) probably suitable for seagrass-habitat relationships in Redang Marine Park, Terengganu, Malaysia. A maximum entropy algorithm was applied, using 12 bathymetric and backscatter predictors to develop a total of 6 seagrass habitat suitability models. The results indicated that both fine and coarse spatial resolution datasets could produce models with high accuracy (>90%). However, the models derived from the coarser resolution dataset displayed inconsistent habitat suitability maps for different analysis window sizes. In contrast, habitat models derived from the fine resolution dataset exhibited similar habitat distribution patterns for three different analysis window sizes. Bathymetry was found to be the most influential predictor in all the models. The backscatter predictors, such as angular range analysis inversion parameters (characterization and grain size), gray-level co-occurrence texture predictors, and backscatter intensity levels, were more important for coarse resolution models. Areas of highest habitat suitability for seagrass were predicted to be in shallower (<20 m) waters and scattered between fringing reefs (east to south). Some fragmented, highly suitable habitats were also identified in the shallower (<20 m) areas in the northwest of the prediction models and scattered between fringing reefs. This study highlighted the importance of investigating the suitable spatial resolution and analysis window size of predictors from MBES for modeling suitable seagrass habitats. The findings provide important insight on the use of remote acoustic sonar data to study and map seagrass distribution in Malaysia coastal water.
The prosodic structure of under-researched languages in the Trade Malay language family is poorly understood. Although boundary marking has been uncontroversially shown as the major prosodic function in these languages, studies on the use of pitch accents to highlight important words in a phrase remain inconclusive. In addition, most knowledge of pitch accents is based on well-researched languages such as the ones from the Western-Germanic language family. This paper reports two word identification experiments comparing Papuan Malay with the pitch accent language American English, in order to investigate the extent to which the demarcating and highlighting function of prosody can be disentangled. To this end, target words were presented to native listeners of both languages and differed with respect to their position in the phrase (medial or final) and the shape of their f0 movement (original or manipulated). Reaction times for the target word identifications revealed overall faster responses for original and final words compared to manipulated and medial ones. The results add to previous findings on the facilitating effect of pitch accents and further improve our prosodic knowledge of underresearched languages.
Recordings of bat echolocation and social calls are used for many research purposes from ecological studies to taxonomy. Effective use of these relies on identification of species from the recordings, but comparative recordings or detailed call descriptions to support identification are often lacking for areas with high biodiversity. The ChiroVox website (https://www.chirovox.org) was created to facilitate the sharing of bat sound recordings together with their metadata, including biodiversity data and recording circumstances. To date, more than 30 researchers have contributed over 3,900 recordings of nearly 200 species, making ChiroVox the largest open-access bat call library currently available. Each recording has a unique identifier that can be cited in publications; hence the acoustic analyses are repeatable. Most of the recordings available through the website are from bats whose species identities are confirmed, so they can be used to determine species in recordings where the bats were not captured or could not be identified. We hope that with the help of the bat researcher community, the website will grow rapidly and will serve as a solid source for bat acoustic research and monitoring.
Although positive photobiomodulation response on wound healing, tissue repair, and therapeutic treatment has been widely reported, additional works are still needed to understand its effects on human blood. This research carried out acoustic measurements using A-scan (GAMPT) ultrasonic techniques to elucidate the photobiomodulation effects on in vitro human blood samples as therapeutic treatment measures. The human blood samples were irradiated using a 532-nm laser with different output laser powers (60 and 80 mW) at various exposure times. The ultrasonic velocity measured in the human blood samples after laser irradiation showed significant changes, most of which were within the acceptance limit for soft tissues (1570 [Formula: see text] 30 m/s). Abnormal cells (echinocyte and crenation) were observed due to excessive exposure during laser treatment.
Increasing protein demands directly require additional resources to those presently and recurrently available. Emerging green technologies have witnessed an escalating interest in "Cavitation Processing" (CP) to ensure a non-invasive, non-ionizing and non-polluting extraction. The main intent of this review is to present an integrated summary of cavitation extraction methods specifically applied to food protein sources. Along with a comparative assessment carried out for each type of cavitation model, protein extraction yield and implications on the extracted protein's structural and functional properties. The basic principle of cavitation is due to the pressure shift in the liquid flow within milliseconds. Hence, cavitation emerges similar to boiling; however, unlike boiling (temperature change), cavitation occurs due to pressure change. Characterization and classification of sample type is also a prime candidate when considering the applications of cavitation models in food processing. Generally, acoustic and hydrodynamic cavitation is applied in food applications including extraction, brewing, microbial cell disruption, dairy processing, emulsification, fermentation, waste processing, crystallisation, mass transfer and production of bioactive peptides. Micro structural studies indicate that shear stress causes disintegration of hydrogen bonds and Van der Waals interactions result in the unfolding of the protein's secondary and/or tertiary structures. A change in the structure is not targeted but rather holistic and affects the physicochemical, functional, and nutritional properties. Cavitation assisted extraction of protein is typically studied at a laboratory scale. This highlights limitations against the application at an industrial scale to obtain potential commercial gains.
Sonothrombolysis is a technique that utilises ultrasound waves to excite microbubbles surrounding a clot. Clot lysis is achieved through mechanical damage induced by acoustic cavitation and through local clot displacement induced by acoustic radiation force (ARF). Despite the potential of microbubble-mediated sonothrombolysis, the selection of the optimal ultrasound and microbubble parameters remains a challenge. Existing experimental studies are not able to provide a complete picture of how ultrasound and microbubble characteristics influence the outcome of sonothrombolysis. Likewise, computational studies have not been applied in detail in the context of sonothrombolysis. Hence, the effect of interaction between the bubble dynamics and acoustic propagation on the acoustic streaming and clot deformation remains unclear. In the present study, we report for the first time the computational framework that couples the bubble dynamic phenomena with the acoustic propagation in a bubbly medium to simulate microbubble-mediated sonothrombolysis using a forward-viewing transducer. The computational framework was used to investigate the effects of ultrasound properties (pressure and frequency) and microbubble characteristics (radius and concentration) on the outcome of sonothrombolysis. Four major findings were obtained from the simulation results: (i) ultrasound pressure plays the most dominant role over all the other parameters in affecting the bubble dynamics, acoustic attenuation, ARF, acoustic streaming, and clot displacement, (ii) smaller microbubbles could contribute to a more violent oscillation and improve the ARF simultaneously when they are stimulated at higher ultrasound pressure, (iii) higher microbubbles concentration increases the ARF, and (iv) the effect of ultrasound frequency on acoustic attenuation is dependent on the ultrasound pressure. These results may provide fundamental insight that is crucial in bringing sonothrombolysis closer to clinical implementation.
Emerging infectious diseases continue to pose a significant burden on global public health, and there is a critical need to better understand transmission dynamics arising at the interface of human activity and wildlife habitats. Passive acoustic monitoring (PAM), more typically applied to questions of biodiversity and conservation, provides an opportunity to collect and analyse audio data in relative real time and at low cost. Acoustic methods are increasingly accessible, with the expansion of cloud-based computing, low-cost hardware, and machine learning approaches. Paired with purposeful experimental design, acoustic data can complement existing surveillance methods and provide a novel toolkit to investigate the key biological parameters and ecological interactions that underpin infectious disease epidemiology.
The development of alternative techniques to efficiently inactivate bacterial suspensions is crucial to prevent transmission of waterborne illness, particularly when commonly used techniques such as heating, filtration, chlorination, or ultraviolet treatment are not practical or feasible. We examine the effect of MHz-order acoustic wave irradiation in the form of surface acoustic waves (SAWs) on Gram-positive (Escherichia coli) and Gram-negative (Brevibacillus borstelensis and Staphylococcus aureus) bacteria suspended in water droplets. A significant increase in the relative bacterial load reduction of colony-forming units (up to 74%) can be achieved by either increasing (1) the excitation power, or, (2) the acoustic treatment duration, which we attributed to the effect of the acoustic radiation force exerted on the bacteria. Consequently, by increasing the maximum pressure amplitude via a hybrid modulation scheme involving a combination of amplitude and pulse-width modulation, we observe that the bacterial inactivation efficiency can be further increased by approximately 14%. By combining this scalable acoustic-based bacterial inactivation platform with plasma-activated water, a 100% reduction in E. coli is observed in less than 10 mins, therefore demonstrating the potential of the synergistic effects of MHz-order acoustic irradiation and plasma-activated water as an efficient strategy for water decontamination.