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Abstract The estimation sound fields over space is of interest in sound field control and analysis, spatial audio, room acoustics and virtual reality. Sound fields can be estimated from a number of measurements distributed over space yet this remains a challenging problem due to the large experimental effort required. In this work we investigate sensor distributions that are optimal to estimate sound fields. Such optimization is valuable as it can greatly reduce the number of measurements required. The sensor positions are optimized with respect to the parameters describing a sound field, or the pressure reconstructed at the area of interest, by finding the positions that minimize the Bayesian Cramér-Rao bound (BCRB). The optimized distributions are investigated in a numerical study as well as with measured room impulse responses. We observe a reduction in the number of measurements of approximately 50% when the sensor positions are optimized for reconstructing the sound field when compared with random distributions. The results indicate that optimizing the sensors positions is also valuable when the vector of parameters is sparse, specially compared with random sensor distributions, which are often adopted in sparse array processing in acoustics.

Abstract This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) with almost no increasing calculation cost. It consists of three components: (i) multi-domain loss (MDL), (ii) bridging operation, which couples the individual instrument networks, and (iii) combination loss (CL). MDL enables the taking advantage of the frequency- and time-domain representations of audio signals. We modify the target network, i.e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information. MDL is then applied to the combinations of the output sources as well as each independent source; hence, we called it CL. MDL and CL can easily be applied to many DNN-based separation methods as they are merely loss functions that are only used during training and do not affect the inference step. Bridging operation does not increase the number of learnable parameters in the network. Experimental results showed that the validity of Open-Unmix (UMX), densely connected dilated DenseNet (D3Net) and convolutional time-domain audio separation network (Conv-TasNet) extended with our X-scheme, respectively called X-UMX, X-D3Net and X-Conv-TasNet, by comparing them with their original versions. We also verified the effectiveness of X-scheme in a large-scale data regime, showing its generality with respect to data size. X-UMX Large (X-UMXL), which was trained on large-scale internal data and used in our experiments, is newly available at https://github.com/asteroid-team/asteroid/tree/master/egs/musdb18/X-UMX .

Abstract End-to-end (E2E) automatic speech recognition (ASR) models, which consist of deep learning models, are able to perform ASR tasks using a single neural network. These models should be trained using a large amount of data; however, collecting speech data which matches the targeted speech domain can be difficult, so speech data is often used that is not an exact match to the target domain, resulting in lower performance. In comparison to speech data, in-domain text data is much easier to obtain. Thus, traditional ASR systems use separately trained language models and HMM-based acoustic models. However, it is difficult to separate language information from an E2E ASR model because the model learns both acoustic and language information in an integrated manner, making it very difficult to create E2E ASR models for specialized target domain which are able to achieve sufficient recognition performance at a reasonable cost. In this paper, we propose a method of replacing the language information within pre-trained E2E ASR models in order to achieve adaptation to a target domain. This is achieved by deleting the “implicit” language information contained within the ASR model by subtracting the source-domain language model trained with a transcription of the ASR’s training data in a logarithmic domain. We then integrate a target domain language model through addition in the logarithmic domain. This subtraction and addition to replace of the language model is based on Bayes’ theorem. In our experiment, we first used two datasets of the Corpus of Spontaneous Japanese (CSJ) to evaluate the effectiveness of our method. We then we evaluated our method using the Japanese Newspaper Article Speech (JNAS) and CSJ corpora, which contain audio data from the read speech and spontaneous speech domain, respectively, to test the effectiveness of our proposed method at bridging the gap between these two language domains. Our results show that our proposed language model replacement method achieved better ASR performance than both non-adapted (baseline) ASR models and ASR models adapted using the conventional Shallow Fusion method.

Abstract The disparities in phonetics and corpuses across the three major dialects of Tibetan exacerbate the difficulty of a single task model for one dialect to accommodate other different dialects. To address this issue, this paper proposes task-diverse meta-learning. Our model can acquire more comprehensive and robust features, facilitating its adaptation to the variations among different dialects. This study uses Tibetan dialect ID recognition and Tibetan speaker recognition as the source tasks for meta-learning, which aims to augment the ability of the model to discriminate variations and differences among different dialects. Consequently, the model’s performance in Tibetan multi-dialect speech recognition tasks is enhanced. The experimental results show that task-diverse meta-learning leads to improved performance in Tibetan multi-dialect speech recognition. This demonstrates the effectiveness and applicability of task-diverse meta-learning, thereby contributing to the advancement of speech recognition techniques in multi-dialect environments.

Abstract This paper introduces a simplified and controllable model for mode coupling in the context of modal synthesis. The model employs efficient coupled filters for sound synthesis purposes, intended to emulate the generation of sounds radiated by sources under strongly nonlinear conditions. Such filters generate tonal components in an interdependent way and are intended to emulate realistic perceptually salient effects in musical instruments in an efficient manner. The control of energy transfer between the filters is realized through a coupling matrix. The generation of prototypical sounds corresponding to nonlinear sources with the filter bank is presented. In particular, examples are proposed to generate sounds corresponding to impacts on thin structures and to the perturbation of the vibration of objects when it collides with an other object. The sound examples presented in the paper and available for listening on the accompanying site illustrate that a simple control of the input parameters allows the generation of sounds whose evocation is coherent and that the addition of random processes yields a significant improvement to the realism of the generated sounds.

Abstract End-to-end speech to text translation aims to directly translate speech from one language into text in another, posing a challenging cross-modal task particularly in scenarios of limited data. Multi-task learning serves as an effective strategy for knowledge sharing between speech translation and machine translation, which allows models to leverage extensive machine translation data to learn the mapping between source and target languages, thereby improving the performance of speech translation. However, in multi-task learning, finding a set of weights that balances various tasks is challenging and computationally expensive. We proposed an adaptive multi-task learning method to dynamically adjust multi-task weights based on the proportional losses incurred during training, enabling adaptive balance in multi-task learning for speech to text translation. Moreover, inherent representation disparities across different modalities impede speech translation models from harnessing textual data effectively. To bridge the gap across different modalities, we proposed to apply optimal transport in the input of end-to-end model to find the alignment between speech and text sequences and learn the shared representations between them. Experimental results show that our method effectively improved the performance on the Tibetan-Chinese, English-German, and English-French speech translation datasets.

Abstract Dysarthria is a speech disorder that affects the ability to communicate due to articulation difficulties. This research proposes a novel method for automatic dysarthria detection (ADD) and automatic dysarthria severity level assessment (ADSLA) by using a variable continuous wavelet transform (CWT) layered convolutional neural network (CNN) model. To determine their efficiency, the proposed model is assessed using two distinct corpora, TORGO and UA-Speech, comprising both dysarthria patients and healthy subject speech signals. The research study explores the effectiveness of CWT-layered CNN models that employ different wavelets such as Amor, Morse, and Bump. The study aims to analyze the models’ performance without the need for feature extraction, which could provide deeper insights into the effectiveness of the models in processing complex data. Also, raw waveform modeling preserves the original signal’s integrity and nuance, making it ideal for applications like speech recognition, signal processing, and image processing. Extensive analysis and experimentation have revealed that the Amor wavelet surpasses the Morse and Bump wavelets in accurately representing signal characteristics. The Amor wavelet outperforms the others in terms of signal reconstruction fidelity, noise suppression capabilities, and feature extraction accuracy. The proposed CWT-layered CNN model emphasizes the importance of selecting the appropriate wavelet for signal-processing tasks. The Amor wavelet is a reliable and precise choice for applications. The UA-Speech dataset is crucial for more accurate dysarthria classification. Advanced deep learning techniques can simplify early intervention measures and expedite the diagnosis process.

Abstract Polyphonic sound source localization and detection (SSLD) task aims to recognize the categories of sound events, identify their onset and offset times, and detect their corresponding direction-of-arrival (DOA), where polyphonic refers to the occurrence of multiple overlapping sound sources in a segment. However, vanilla SSLD methods based on convolutional recurrent neural network (CRNN) suffer from insufficient feature extraction. The convolutions with kernel of single scale in CRNN fail to adequately extract multi-scale features of sound events, which have diverse time-frequency characteristics. It results in that the extracted features lack fine-grained information helpful for the localization of sound sources. In response to these challenges, we propose a polyphonic SSLD network based on global-local feature extraction and recalibration (GLFER-Net), where the global-local feature (GLF) extractor is designed to extract the multi-scale global features through an omni-directional dynamic convolution (ODConv) layer and multi-scale feature extraction (MSFE) module. The local feature extraction (LFE) unit is designed for capturing detailed information. Besides, we design a feature recalibration (FR) module to emphasize the crucial features along multiple dimensions. On the open datasets of Task3 in DCASE 2021 and 2022 Challenges, we compared our proposed GLFER-Net with six and four SSLD methods, respectively. The results show that the GLFER-Net achieves competitive performance. The modules we designed are verified to be effective through a series of ablation experiments and visualization analyses.

Abstract While deep learning technologies have made remarkable progress in generating deepfakes, their misuse has become a well-known concern. As a result, the ubiquitous usage of deepfakes for increasing false information poses significant risks to the security and privacy of individuals. The primary objective of audio spoofing detection is to identify audio generated through numerous AI-based techniques. Several techniques for fake audio detection already exist using machine learning algorithms. However, they lack generalization and may not identify all types of AI-synthesized audios such as replay attacks, voice conversion, and text-to-speech (TTS). In this paper, a deep layered model, i.e., VGGish, along with an attention block, namely Convolutional Block Attention Module (CBAM) for spoofing detection, is introduced. Our suggested model successfully classifies input audio into two classes: Fake and Real, converting them into mel-spectrograms, and extracting their most representative features due to the attention block. Our model is a significant technique to utilize for audio spoofing detection due to a simple layered architecture. It captures complex relationships in audio signals due to both spatial and channel features present in an attention module. To evaluate the effectiveness of our model, we have conducted in-depth testing using the ASVspoof 2019 dataset. The proposed technique achieved an EER of 0.52% for Physical Access (PA) attacks and 0.07 % for Logical Access (LA) attacks.

Abstract A new method for estimating the first and second derivatives of discrete audio signals intended to achieve higher computational precision in analyzing the performance and characteristics of digital audio systems is presented. The method could find numerous applications in modeling nonlinear audio circuit systems, e.g., for audio synthesis and creating audio effects, music recognition and classification, time-frequency analysis based on nonstationary audio signal decomposition, audio steganalysis and digital audio authentication or audio feature extraction methods. The proposed algorithm employs the ordinary 7 point-stencil central-difference formulas with improvements that minimize the round-off and truncation errors. This is achieved by treating the step size of numerical differentiation as a regularization parameter, which acts as a decision threshold in all calculations. This approach requires shifting discrete audio data by fractions of the initial sample rate, which was obtained by fractional delay FIR filters designed with modified 11-term cosine-sum windows for interpolation and shifting of audio signals. The maximum relative error in estimating first and second derivatives of discrete audio signals are respectively in order of $$10^{-13}$$ 10 - 13 and $$10^{-10}$$ 10 - 10 over the entire audio band, which is close to double-precision floating-point accuracy for the first and better than single-precision floating-point accuracy for the second derivative estimation. Numerical testing showed that this performance of the proposed method is not influenced by the type of signal being differentiated (either stationary or nonstationary), and provides better results than other known differentiation methods, in the audio band up to 21 kHz.

Abstract This work introduces a large dataset comprising impulse responses of spatially distributed sources within a plane parallel to a planar microphone array. The dataset, named MIRACLE, encompasses 856,128 single-channel impulse responses and includes four different measurement scenarios. Three measurement scenarios were conducted under anechoic conditions. The fourth scenario includes an additional specular reflection from a reflective panel. The source positions were obtained by uniformly discretizing a rectangular source plane parallel to the microphone for each scenario. The dataset contains three scenarios with a spatial resolution of $$23\,\textrm{mm}$$ 23 mm at two different source-plane-to-array distances, as well as a scenario with a resolution of $$5\,\textrm{mm}$$ 5 mm for the shorter distance. In contrast to existing room impulse response datasets, the accuracy of the provided source location labels is assessed and additional metadata, such as the directivity of the loudspeaker used for excitation, is provided. The MIRACLE dataset can be used as a benchmark for data-driven modelling and interpolation methods as well as for various acoustic machine learning tasks, such as source separation, localization, and characterization. Two timely applications of the dataset are presented in this work: the generation of microphone array data for data-driven source localization and characterization tasks and data-driven model order reduction.

Abstract Time signature detection is a fundamental task in music information retrieval, aiding in music organization. In recent years, the demand for robust and efficient methods in music analysis has amplified, underscoring the significance of advancements in time signature detection. In this study, we explored the effectiveness of residual networks for time signature detection. Additionally, we compared the performance of the residual network (ResNet18) to already existing models such as audio similarity matrix (ASM) and beat similarity matrix (BSM). We also juxtaposed with traditional algorithms such as support vector machine (SVM), random forest, K-nearest neighbor (KNN), naive Bayes, and that of deep learning models, such as convolutional neural network (CNN) and convolutional recurrent neural network (CRNN). The evaluation is conducted using Mel-frequency cepstral coefficients (MFCCs) as feature representations on the Meter2800 dataset. Our results indicate that ResNet18 outperforms all other models thereby showing the potential of deep learning models for accurate time signature detection.

Abstract Limited data availability remains a significant challenge for Whisper’s low-resource speech recognition performance, falling short of practical application requirements. While previous studies have successfully reduced the recognition error rates of target language speech through fine-tuning, a comprehensive exploration and analysis of Whisper’s fine-tuning capabilities and the advantages and disadvantages of various fine-tuning strategies are still lacking. This paper aims to fill this gap by conducting comprehensive experimental exploration for Whisper’s low-resource speech recognition performance using five fine-tuning strategies with limited supervised data from seven low-resource languages. The results and analysis demonstrate that all fine-tuning strategies explored in this paper significantly enhance Whisper’s performance. However, different strategies vary in their suitability and practical effectiveness, highlighting the need for careful selection based on specific use cases and resources available.

Abstract In the era of advanced text-to-speech (TTS) systems capable of generating high-fidelity, human-like speech by referring a reference speech, voice cloning (VC), or zero-shot TTS (ZS-TTS), stands out as an important subtask. A primary challenge in VC is maintaining speech quality and speaker similarity with limited reference data for a specific speaker. However, existing VC systems often rely on naive combinations of embedded speaker vectors for speaker control, which compromises the capture of speaking style, voice print, and semantic accuracy. To overcome this, we introduce the Two-branch Speaker Control Module (TSCM), a novel and highly adaptable voice cloning module designed to precisely processing speaker or style control for a target speaker. Our method uses an advanced fusion of local-level features from a Gated Convolutional Network (GCN) and utterance-level features from a gated recurrent unit (GRU) to enhance speaker control. We demonstrate the effectiveness of TSCM by integrating it into advanced TTS systems like FastSpeech 2 and VITS architectures, significantly optimizing their performance. Experimental results show that TSCM enables accurate voice cloning for a target speaker with minimal data through both zero-shot or few-shot fine-tuning of pretrained TTS models. Furthermore, our TSCM-based VITS (TSCM-VITS) showcases superior performance in zero-shot scenarios compared to existing state-of-the-art VC systems, even with basic dataset configurations. Our method’s superiority is validated through comprehensive subjective and objective evaluations. A demonstration of our system is available at https://great-research.github.io/tsct-tts-demo/ , providing practical insights into its application and effectiveness.

Abstract Selective attention is a crucial ability of the auditory system. Computationally, following an auditory object can be illustrated as tracking its acoustic properties, e.g., pitch, timbre, or location in space. The difficulty is related to the fact that in a complex auditory scene, the information about the tracked object is not available in a clean form. The more cluttered the sound mixture, the more time and frequency regions where the object of interest is masked by other sound sources. How does the auditory system recognize and follow acoustic objects based on this fragmentary information? Numerous studies highlight the crucial role of top-down processing in this task. Having in mind both auditory modeling and signal processing applications, we investigated how computational methods with and without top-down processing deal with increasing sparsity of the auditory features in the task of estimating instantaneous voice states, defined as a combination of three parameters: fundamental frequency F0 and formant frequencies F1 and F2. We found that the benefit from top-down processing grows with increasing sparseness of the auditory data.

Abstract This work studies neural modeling of nonlinear parametric audio circuits, focusing on how the diversity of settings of the target device user controls seen during training affects network generalization. To study the problem, a large corpus of training datasets is synthetically generated using SPICE simulations of two distinct devices, an analog equalizer and an analog distortion pedal. A proven recurrent neural network architecture is trained using each dataset. The difference in the datasets is in the sampling resolution of the device user controls and in their overall size. Based on objective and subjective evaluation of the trained models, a sampling resolution of five for the device parameters is found to be sufficient to capture the behavior of the target systems for the types of devices considered during the study. This result is desirable, since a dense sampling grid can be impractical to realize in the general case when no automated way of setting the device parameters is available, while collecting large amounts of data using a sparse grid only incurs small additional costs. Thus, the result provides guidance for efficient collection of training data for neural modeling of other similar audio devices.

Abstract Visual speech recognition (VSR) is a challenging task that has received increasing interest during the last few decades. Current state of the art employs powerful end-to-end architectures based on deep learning which depend on large amounts of data and high computational resources for their estimation. We address the task of VSR for data scarcity scenarios with limited computational resources by using traditional approaches based on hidden Markov models. We present a novel learning strategy that employs information obtained from previous acoustic temporal alignments to improve the visual system performance. Furthermore, we studied multiple visual speech representations and how image resolution or frame rate affect its performance. All these experiments were conducted on the limited data VLRF corpus, a database which offers an audio-visual support to address continuous speech recognition in Spanish. The results show that our approach significantly outperforms the best results achieved on the task to date.

Abstract This article introduces Mi-Go, a tool aimed at evaluating the performance and adaptability of general-purpose speech recognition machine learning models across diverse real-world scenarios. The tool leverages YouTube as a rich and continuously updated data source, accounting for multiple languages, accents, dialects, speaking styles, and audio quality levels. To demonstrate the effectiveness of the tool, an experiment was conducted, by using Mi-Go to evaluate state-of-the-art automatic speech recognition machine learning models. The evaluation involved a total of 141 randomly selected YouTube videos. The results underscore the utility of YouTube as a valuable data source for evaluation of speech recognition models, ensuring their robustness, accuracy, and adaptability to diverse languages and acoustic conditions. Additionally, by contrasting the machine-generated transcriptions against human-made subtitles, the Mi-Go tool can help pinpoint potential misuse of YouTube subtitles, like search engine optimization.

Abstract Dynamic parameterization of acoustic environments has drawn widespread attention in the field of audio processing. Precise representation of local room acoustic characteristics is crucial when designing audio filters for various audio rendering applications. Key parameters in this context include reverberation time (RT $$_{60}$$ 60 ) and geometric room volume. In recent years, neural networks have been extensively applied in the task of blind room parameter estimation. However, there remains a question of whether pure attention mechanisms can achieve superior performance in this task. To address this issue, this study employs blind room parameter estimation based on monaural noisy speech signals. Various model architectures are investigated, including a proposed attention-based model. This model is a convolution-free Audio Spectrogram Transformer, utilizing patch splitting, attention mechanisms, and cross-modality transfer learning from a pretrained Vision Transformer. Experimental results suggest that the proposed attention mechanism-based model, relying purely on attention mechanisms without using convolution, exhibits significantly improved performance across various room parameter estimation tasks, especially with the help of dedicated pretraining and data augmentation schemes. Additionally, the model demonstrates more advantageous adaptability and robustness when handling variable-length audio inputs compared to existing methods.

Abstract In robotics, echolocation has been used to detect acoustic reflectors, e.g., walls, as it aids the robotic platform to navigate in darkness and also helps detect transparent surfaces. However, the transfer function or response of an acoustic system, e.g., loudspeakers/emitters, contributes to non-ideal behavior within the acoustic systems that can contribute to a phase lag due to propagation delay. This non-ideal response can hinder the performance of a time-of-arrival (TOA) estimator intended for acoustic reflector localization especially when the estimation of multiple reflections is required. In this paper, we, therefore, propose a robust expectation-maximization (EM) algorithm that takes into account the response of acoustic systems to enhance the TOA estimation accuracy when estimating multiple reflections when the robot is placed in a corner of a room. A non-ideal transfer function is built with two parameters, which are estimated recursively within the estimator. To test the proposed method, a hardware proof-of-concept setup was built with two different designs. The experimental results show that the proposed method could detect an acoustic reflector up to a distance of 1.6 m with $$60\%$$ 60 % accuracy under the signal-to-noise ratio (SNR) of 0 dB. Compared to the state-of-the-art EM algorithm, our proposed method provides improved performance when estimating TOA by $$10\%$$ 10 % under a low SNR value.

Abstract Speech signals are often distorted by reverberation and noise, with a widely distributed signal-to-noise ratio (SNR). To address this, our study develops robust, deep neural network (DNN)-based speech enhancement methods. We reproduce several DNN-based monaural speech enhancement methods and outline a strategy for constructing datasets. This strategy, validated through experimental reproductions, has effectively enhanced the denoising efficiency and robustness of the models. Then, we propose a causal speech enhancement system named Supervised Attention Multi-Scale Temporal Convolutional Network (SA-MSTCN). SA-MSTCN extracts the complex compressed spectrum (CCS) for input encoding and employs complex ratio masking (CRM) for output decoding. The supervised attention module, a lightweight addition to SA-MSTCN, guides feature extraction. Experiment results show that the supervised attention module effectively improves noise reduction performance with a minor increase in computational cost. The multi-scale temporal convolutional network refines the perceptual field and better reconstructs the speech signal. Overall, SA-MSTCN not only achieves state-of-the-art speech quality and intelligibility compared to other methods but also maintains stable denoising performance across various environments.

Abstract Spoofed speeches are becoming a big threat to society due to advancements in artificial intelligence techniques. Therefore, there must be an automated spoofing detector that can be integrated into automatic speaker verification (ASV) systems. In this study, we recommend a novel and robust model, named DeepDet, based on deep-layered architecture, to categorize speech into two classes: spoofed and bonafide. DeepDet is an improved model based on Yet Another Mobile Network (YAMNet) employing a customized MobileNet combined with a bottleneck attention module (BAM). First, we convert audio into mel-spectrograms that consist of time–frequency representations on mel-scale. Second, we trained our deep layered model using the extracted mel-spectrograms on a Logical Access (LA) set, including synthesized speeches and voice conversions of the ASVspoof-2019 dataset. In the end, we classified the audios, utilizing our trained binary classifier. More precisely, we utilized the power of layered architecture and guided attention that can discern the spoofed speech from bonafide samples. Our proposed improved model employs depth-wise linearly separate convolutions, which makes our model lighter weight than existing techniques. Furthermore, we implemented extensive experiments to assess the performance of the suggested model using the ASVspoof 2019 corpus. We attained an equal error rate (EER) of 0.042% on Logical Access (LA), whereas 0.43% on Physical Access (PA) attacks. Therefore, the performance of the proposed model is significant on the ASVspoof 2019 dataset and indicates the effectiveness of the DeepDet over existing spoofing detectors. Additionally, our proposed model is robust enough that can identify the unseen spoofed audios and classifies the several attacks accurately.

Abstract Technologies in healthcare, smart homes, security, ecology, and entertainment all deploy audio event detection (AED) in order to detect sound events in an audio recording. Effective AED techniques rely heavily on supervised or semi-supervised models to capture the wide range of dynamics spanned by sound events in order to achieve temporally precise boundaries and accurate event classification. These methods require extensive collections of labeled or weakly labeled in-domain data, which is costly and labor-intensive. Importantly, these approaches do not fully leverage the inherent variability and range of dynamics across sound events, aspects that can be effectively identified through unsupervised methods. The present work proposes an approach based on multi-rate autoencoders that are pretrained in an unsupervised way to leverage unlabeled audio data and ultimately learn the rich temporal dynamics inherent in natural sound events. This approach utilizes parallel autoencoders that achieve decompositions of the modulation spectrum along different bands. In addition, we introduce a rate-selective temporal contrastive loss to align the training objective with event detection metrics. Optimizing the configuration of multi-rate encoders and the temporal contrastive loss leads to notable improvements in domestic sound event detection in the context of the DCASE challenge.

Ring-array photoacoustic tomography (PAT) system has been widely used in noninvasive biomedical imaging. However, the reconstructed image usually suffers from spatially rotational blur and streak artifacts due to the non-ideal imaging conditions. To improve the reconstructed image towards higher quality, we propose a concept of spatially rotational convolution to formulate the image blur process, then we build a regularized restoration problem model accordingly and design an alternating minimization algorithm which is called blind spatially rotational deconvolution to achieve the restored image. Besides, we also present an image preprocessing method based on the proposed algorithm to remove the streak artifacts. We take experiments on phantoms and in vivo biological tissues for evaluation, the results show that our approach can significantly enhance the resolution of the image obtained from ring-array PAT system and remove the streak artifacts effectively.

Expansion microscopy (ExM) is a promising technology that enables nanoscale imaging on conventional optical microscopes by physically magnifying the specimens. Here, we report the development of a strategy that enables i) on-demand labeling of subcellular organelles in live cells for ExM through transfection of fluorescent proteins that are well-retained during the expansion procedure; and ii) non-fluorescent chromogenic color-development towards efficient bright-field and photoacoustic imaging in both planar and volumetric formats, which is applicable to both cultured cells and biological tissues. Compared to the conventional ExM methods, our strategy provides an expanded toolkit, which we term as expansion fluorescence and photoacoustic microscopy (ExFLPAM), by allowing on-demand fluorescent protein labeling of cultured cells, as well as non-fluorescent absorption contrast-imaging of biological samples.

Background: The differentiation between benign and malignant breast tumors extends beyond morphological structures to encompass functional alterations within the nodules. The combination of photoacoustic (PA) imaging and radiomics unveils functional insights and intricate details that are imperceptible to the naked eye. Purpose: This study aims to assess the efficacy of PA imaging in breast cancer radiomics, focusing on the impact of peritumoral region size on radiomic model accuracy. Materials and methods: From January 2022 to November 2023, data were collected from 358 patients with breast nodules, diagnosed via PA/US examination and classified as BI-RADS 3–5. The study used the largest lesion dimension in PA images to define the region of interest, expanded by 2 mm, 5 mm, and 8 mm, for extracting radiomic features. Techniques from statistics and machine learning were applied for feature selection, and logistic regression classifiers were used to build radiomic models. These models integrated both intratumoral and peritumoral data, with logistic regressions identifying key predictive features. Results: The developed nomogram, combining 5 mm peritumoral data with intratumoral and clinical features, showed superior diagnostic performance, achieving an AUC of 0.950 in the training cohort and 0.899 in validation. This model outperformed those based solely on clinical features or other radiomic methods, with the 5 mm peritumoral region proving most effective in identifying malignant nodules. Conclusion: This research demonstrates the significant potential of PA imaging in breast cancer radiomics, especially the advantage of integrating 5 mm peritumoral with intratumoral features. This approach not only surpasses models based on clinical data but also underscores the importance of comprehensive radiomic analysis in accurately characterizing breast nodules.

We present a sensitive and compact quantum cascade laser-based photoacoustic greenhouse gas sensor for the detection of CO2, CH4 and CO and discuss its applicability toward on-line real-time trace greenhouse gas analysis. Differential photoacoustic resonators with different dimensions were used and optimized to balance sensitivity with signal saturation. The effects of ambient parameters, gas flow rate, pressure and humidity on the photoacoustic signal and the spectral cross-interference were investigated. Thanks to the combined operation of in-house designed laser control and lock-in amplifier, the gas detection sensitivities achieved were 5.6 ppb for CH4, 0.8 ppb for CO and 17.2 ppb for CO2, signal averaging time 1 s and an excellent dynamic range beyond 6 orders of magnitude. A continuous outdoor five-day test was performed in an observation station in China’s Qinling National Botanical Garden (E longitude 108°29’, N latitude 33°43’) which demonstrated the stability and reliability of the greenhouse gas sensor.

The achievable sensitivity level of photo-acoustic trace-gas sensors essentially depends on the performances of the acoustic transducer. In this work, the mechanical response of different silicon-based micro-electro-mechanical systems (MEMS) is characterized, aiming at investigating both their mechanical properties, namely the resonance frequency and the quality factor, and the minimum detection limit (MDL) achievable when they are exploited as an acoustic-to-voltage transducer in a trace-gas photoacoustic setup. For this purpose, a 4.56µm Continuous-Wave (CW) quantum cascade laser (QCL) is used to excite a strong N2O roto-vibrational transition with a line strength of 2.14 × 10−19 cm/molecule, and the detection of MEMS oscillations is performed via an interferometric readout. As a general trend, the minimum detection limit decreases when the resonance frequency investigated increases, achieving a value of 15 parts per billion with a 3 dB cut-off lock-in bandwidth equal to 100 mHz, around 10 kHz.

Photoacoustic (PA) image reconstruction involves acoustic inversion that necessitates the specification of the speed of sound (SoS) within the medium of propagation. Due to the lack of information on the spatial distribution of the SoS within heterogeneous soft tissue, a homogeneous SoS distribution (such as 1540 m/s) is typically assumed in PA image reconstruction, similar to that of ultrasound (US) imaging. Failure to compensate for the SoS variations leads to aberration artefacts, deteriorating the image quality. Various methods have been proposed to address this issue, but they usually involve complex hardware and/or time-consuming algorithms, hindering clinical translation. In this work, we introduce a deep learning framework for SoS estimation and subsequent aberration correction in a dual-modal PA/US imaging system exploiting a clinical US probe. As the acquired PA and US images were inherently co-registered, the estimated SoS distribution from US channel data using a deep neural network was incorporated for accurate PA image reconstruction. The framework comprised an initial pre-training stage based on digital phantoms, which was further enhanced through transfer learning using physical phantom data and associated SoS maps obtained from measurements. This framework achieved a root mean square error of 10.2 m/s and 15.2 m/s for SoS estimation on digital and physical phantoms, respectively and structural similarity index measures of up to 0.88 for PA reconstructions compared to the conventional approach of 0.69. A maximum of 1.2 times improvement in the signal-to-noise ratio of PA images was further demonstrated with a human volunteer study. Our results show that the proposed framework could be valuable in various clinical and preclinical applications to enhance PA image reconstruction.

Quantitative photoacoustic tomography (qPAT) holds great potential in estimating chromophore concentrations, whereas the involved optical inverse problem, aiming to recover absorption coefficient distributions from photoacoustic images, remains challenging. To address this problem, we propose an extractor-attention-predictor network architecture (EAPNet), which employs a contracting–expanding structure to capture contextual information alongside a multilayer perceptron to enhance nonlinear modeling capability. A spatial attention module is introduced to facilitate the utilization of important information. We also use a balanced loss function to prevent network parameter updates from being biased towards specific regions. Our method obtains satisfactory quantitative metrics in simulated and real-world validations. Moreover, it demonstrates superior robustness to target properties and yields reliable results for targets with small size, deep location, or relatively low absorption intensity, indicating its broader applicability. The EAPNet, compared to the conventional UNet, exhibits improved efficiency, which significantly enhances performance while maintaining similar network size and computational complexity.

We proposed a non-contact photoacoustic (PA) detection method using spectral domain optical coherence tomography (SDOCT). Two interference spectrums (A-lines) were acquired before and after the PA excitation with SDOCT. PA signal propagated within the sample causing the vibration. The vibration inner the sample introduced phase change between the acquired two A-lines. Thus, the PA signal can be detected by evaluating the difference in phase between the two A-lines. Based on the method, an OCT-PAM dual-mode imaging system was constructed. In the system, SDOCT served as the detection unit for PAM. Thus, the combination of the two imaging modalities was simplified. Another advantage of the system is that it realizes non-contact all-optic detection, which is attractive for biomedical imaging. Using the system, we imaged phantoms of carbon fibers, asparagus leaves and human hairs. Furthermore, the cortical vasculature of rat was imaged in vivo and the flow status was evaluated quantitatively.

Microcirculatory dysfunction has been observed in the dermal white adipose tissue (dWAT) and subcutaneous white adipose tissue (scWAT) of obese humans and has been proposed as an early prediction marker for cardio-metabolic disease progression. In-vivo visualization and longitudinal monitoring of microvascular remodeling in these tissues remains challenging. We compare the performance of two optoacoustic imaging methods, i.e. multi-spectral optoacoustic tomography (MSOT) and raster-scanning optoacoustic mesoscopy (RSOM) in visualizing lipid and hemoglobin contrast in scWAT and dWAT in a mouse model of diet-induced obesity (DIO) undergoing voluntary wheel running intervention for 32 weeks. MSOT visualized lipid and hemoglobin contrast in murine fat depots in a quantitative manner even at early stages of DIO. We show for the first time to our knowledge that RSOM allows precise visualization of the dWAT microvasculature and provides quantitative readouts of skin layer thickness and vascular density in dWAT and dermis. Combination of MSOT and RSOM resolved exercise-induced morphological changes in microvasculature density, tissue oxygen saturation, lipid and blood volume content in dWAT and scWAT. The combination of MSOT and RSOM may allow precise monitoring of microcirculatory dysfunction and intervention response in dWAT and scWAT in a mouse model for DIO. Our findings have laid out the foundation for future clinical studies using optoacoustic-derived vascular readouts from adipose tissues as a biomarker for monitoring microcirculatory function in metabolic disease.

Spectral photoacoustic imaging in combination with unmixing techniques may be applied to retrieve information about high-risk features present in atherosclerotic plaques, possibly providing prognostic insights into future stroke events. We present the photoacoustic spectral contrast found in 12 systematically scanned advanced atherosclerotic plaques in the near-infrared wavelength range (850–1250 nm). The main absorbers are lipid, water, and hemoglobin, with the highest photoacoustic intensities at the lipid’s second overtone at 1190 and 1210 nm. Linear unmixing resulted in visualizing regions with high lipid and hemoglobin absorption, corresponding to the histological presence of lipid and intraplaque hemorrhage. A non-negative matrix factorization approach reveals differences in lipid spectral contrast, providing potential insights into the vulnerability of atherosclerotic plaque. These results provide a reference for future, more complex, in vivo photoacoustic imaging of carotid artery atherosclerosis, potentially contributing to assessing the risk of future events and treatment decision.

Meat is highly susceptible to contamination with harmful microorganisms throughout the production, processing, and storage chain, posing a significant public health risk. Traditional decontamination methods like chemical sanitizers and heat treatments often compromise meat quality, generate harmful residues, and require high energy inputs. This necessitates the exploration of alternative non-ionizing technologies for ensuring meat safety and quality. This review provides a comprehensive analysis of the latest advancements, limitations, and future prospects of non-ionizing technologies for meat decontamination, with a specific focus on ultrasonication. It further investigates the comparative advantages and disadvantages of ultrasonication against other prominent non-ionizing technologies such as microwaves, ultraviolet (UV) light, and pulsed light. Additionally, it explores the potential of integrating these technologies within a multi-hurdle strategy to achieve enhanced decontamination across the meat surface and within the matrix. While non-ionizing technologies have demonstrated promising results in reducing microbial populations while preserving meat quality attributes, challenges remain. These include optimizing processing parameters, addressing regulatory considerations, and ensuring cost-effectiveness for large-scale adoption. Combining these technologies with other methods like antimicrobial agents, packaging, and hurdle technology holds promise for further enhancing pathogen elimination while safeguarding meat quality.

The paper investigates the oil–water emulsification process inside a micro-venturi channel. More specifically, the possible influence of Kelvin-Helmholtz instability on the emulsification process. High-speed visualizations were conducted inside a square venturi constriction with throat dimensions of 450 µm by 450 µm, both under visible light and X-Rays. We show that cavity shedding caused by the instability results in the formation of several cavity vortices. Their rotation causes the deformation of the oil stream into a distinct wave-like shape, combined with fragmentation into larger drops due to cavitation bubble collapse. Later on, the cavity collapse further disperses the larger drops into a finer emulsion. Thus, it turns out that the Kelvin-Helmholtz instability is similarly characteristic for hydrodynamic cavitation emulsification inside a microchannel as is the Rayleigh-Taylor instability for acoustically driven emulsion formation.

Sonochemical decomposition effects of nickelocene, which sublimates easily were investigated to synthesize dispersant-free nickel fine particles at low temperature. In a hydrazine monohydrate and 2-propanol mixed solvent, the reduction of nickelocene was promoted by ultrasound irradiation, and nickel fine particles were synthesized while precluding the sublimation of nickelocene. Unlike the common hydrazine reduction of nickel salts, which requires multiple-step reactions, nickelocene was reduced directly without forming intermediates. The effect of the water-bath temperature (20–60 °C) was investigated, where larger fine particles were synthesized using a higher water-bath temperature (60 °C). When irradiated at 20 °C, the reduction rate of nickelocene was low, leading to the formation of nickel fine particles and organic nanoparticles via the reduction and decomposition of nickelocene. The ultrasound frequency was also investigated, where fine nickel particles were synthesized using low-frequency ultrasound irradiation. The formation of high-temperature hotspots led to the diffusion and growth of nickel on the surface of the nickel fine particles; therefore, raspberry-like nickel fine particles were synthesized. In this study, the difficult-to-handle nature of nickelocene, owing to its sublimation properties, was easily overcome by ultrasound irradiation. Instantaneous and localized reactions at hotspots contributed to inhibiting particle growth. Furthermore, Ni fine particles were synthesized via a direct reduction pathway, which differs from previous reactions. This method represents a new, dispersant-free, low-temperature process for synthesizing Ni fine particles.

This work offered a productive technique for resveratrol extraction from Polygonum Cuspidatum (P. Cuspidatum) using ionic liquids in synergy with ultrasound-enzyme-assisted extraction (UEAE). Firstly, ionic liquids with different carbon chains and anions were evaluated. Subsequently, a comprehensive investigation was carried out to evaluate the effect of seven crucial parameters on the resveratrol yield: pH value, enzyme concentration, extraction temperature, extraction time, ultrasonic power, concentration of ionic liquid (IL concentration) and the liquid–solid ratio. Employing the Plackett-Burman Design (PBD), the critical factors were effectively identified. Building upon this foundation, the process was further optimized through the application of Response Surface Methodology (RSM) and an Artificial Neural Network-Genetic Algorithm (ANN-GA). The following criteria were determined to be the ideal extraction conditions: an enzyme concentration of 2.18%, extraction temperature of 58 °C, a liquid–solid ratio of 29 mL/g, pH value of 5.5, extraction time of 30 min, ultrasonic power of 250 W, and extraction solvent of 0.5 mol/L 1-butyl-3-methylimidazolium bromide. Under these conditions, the resveratrol yield was determined to be 2.90 ± 0.15 mg/g. Comparative analysis revealed that the ANN-GA model provided a better fit to the experimental data of resveratrol yield than the RSM model, suggesting superior predictive capabilities of the ANN-GA approach. The introduction of a novel green solvent system in this experiment not only simplifies the extraction process but also enhances safety and feasibility. This research paves the way for innovative approaches to extracting resveratrol from botanical sources, showcasing its significant potential for a wide range of applications.

The study aimed to extract and encapsulate betalain pigment from prickly pear (Opuntia ficus-indica) using ultrasound-assisted extraction and eco-friendly glycerol. Subsequent analysis encompassed assessing its thermal stability, shelf-life, bio-accessibility, and biological properties. The process optimization employed Response Surface Methodology (RSM), focusing on glycerol concentration (20–50 %), sample to solvent ratio (1:10–1:20), temperature (30–60 °C), and time (10–30 min). Optimal conditions were determined as 23.15 % glycerol, 1:10 sample to solvent ratio, 10.43 min treatment time, and 31.15 °C temperature. Under these conditions, betalain content reached 858.28 mg/L with a 93.76 % encapsulation efficiency. Thermal stability tests (80–180 °C; 30 & 60 min) showed degradation of betalain with higher temperatures and longer durations, affecting the visual aspect (ΔE) of the pigment. Encapsulated betalain exhibited favorable shelf stability, with optimal storage life of 404.27 days at 4 °C in amber conditions, compared to 271.99 days at 4 °C without amber, 141.92 days at 25 °C without amber, and 134.22 days at 25 °C with amber. Bio-accessibility of encapsulated betalain was significantly higher (2.05 ± 0.03 %) than conventionally extracted pigment (1.03 ± 0.09 %). The encapsulated pigment displayed strong anti-inflammatory properties in dosages of 2–20 µL, with no cytotoxic effects. Additionally, incorporation into gummies was successful and visually approved by sensory panellists. Glycerol proved to be a green encapsulating agent for betalain, offering high shelf life and bio-accessibility, making it suitable for food industry applications. The encapsulated pigment demonstrated robust thermal stability and shelf life, making it suitable for food industry applications. This study highlights glycerol’s potential as a sustainable alternative for natural pigment extraction.

Quartz tuning fork (QTF)-based techniques of photoacoustic spectroscopy and thermoelastic spectroscopy play a significant role in trace gas sensing due to unique high sensitivity and compactness. However, the stability of both techniques remains plagued by the inevitable and unpredictable laser power variation and demodulation phase variation. Herein, we investigate the phase change of a QTF when integrating both techniques for enhanced gas sensing. By demonstrating harmonic phase-sensitive methane detection as an example, we achieve stable gas measurement at varying laser power (2.4–9.4 mW) and varying demodulation phase (−90–90°). Besides, this method shows more tolerance to resonant frequency drift, contributing to a small signal fluctuation of ≤ 6.4 % over a wide modulation range (>10 times of the QTF bandwidth). The realization of harmonic-phase detection allows strengthening the stability of QTF-based sensors in a simple manner, especially when stable parameters, such as laser power, demodulation phase, even resonant frequency, cannot always be maintained.

Mulberry leaf protein (MLP) is a nutrient-rich protein, but its applicability is limited because of its poor solubility. To address this issue, this study combines MLP with whey protein isolates (WPI), known for the high nutritional value, and subsequently forms composite protein nanoparticles using the ultrasound-assisted pH shifting method. Microscopic observation and SDS-PAGE confirmed the binding between these two proteins. Fluorescence spectra and Fourier Transform infrared spectroscopy (FTIR) analysis supported the involvement of electrostatic interactions, hydrophobic attractions, and hydrogen bonding in the formation of stable complex nanoparticles. The interactions between the proteins became stronger after ultrasound-assisted pH-shifting treatment. Solubility, emulsification capacity, foaming, and antioxidant activity, among other indicators, demonstrate that the prepared composite nanoparticles exhibit favorable functional properties. The study successfully illustrates the creation of protein-based complex nanoparticles through the ultrasound-assisted pH shifting method, with potential applications in the delivery of bioactive compounds.

This study employed segmented variable-frequency ultrasound synergistic hot-air drying (SVFU-HAD) for Rhubarb slices, selected two sets of time nodes for frequency conversion (60 min, 120 min, and 90 min, 150 min), and two sequences of frequency conversion (high-frequency to low-frequency, and low-frequency to high-frequency). It aimed to investigate the effects of SVFU-HAD on the drying characteristics, quality, and heat transfer of Rhubarb slices. The findings indicated that segmented variable-frequency ultrasound has advantages in increasing drying rate and improving uniformity of cavitation effects compared to constant-frequency ultrasound. Analysis of physical properties revealed that the rehydration performance of dried products subjected to ultrasonic variable-frequency treatment (90 min, 150 min) according to the drying rate was better (RR > 3.3). The transition mode from high-frequency to low-frequency in variable-frequency ultrasonic treatment contributes to maintaining the overall color of Rhubarb. Analysis of chemical properties unveiled that Rhubarb treated with 40 kHz (0 min)–28 kHz (60 min)–25 kHz (120 min) segmented variable-frequency ultrasound contained overall higher levels of tannins, dianthrones and free anthraquinones content, which exceeded the average values by 3.24%, 26.65%, and 14.42%, respectively. In addition, thermal analysis results based on ANSYS Workbench software demonstrated that the drying uniformity of SVFU-HAD is superior to that of hot-air drying and constant-frequency ultrasound synergistic hot-air drying (CFU-HAD). Overall, the SVFU-HAD method employed in this study presents an innovative approach to ultrasound synergistic hot-air drying research with promising potential for enhancing the efficiency and quality characteristics of Rhubarb slices.

Investigating the extraction of bioactive compounds represents a hopeful direction for maximizing the value of longan fruit byproducts. This study explored the influence of ultrasonic-assisted extraction (UAE) parameters—specifically ultrasonic power ratios, temperatures, and exposure times—utilizing water as a green solvent on several properties of the longan seeds extract (LSE). These properties encompassed the energy consumption of the UAE process (EC), extraction yield (EY), total phenolic contents (TPC), total flavonoid contents (TFC), and antioxidant activity (DPPH). Additionally, the study sought to optimize the conditions of UAE process and examine its thermodynamic properties. A three-level, three-factor full factorial design was utilized to assess the effects of different factors on LSE properties. Results indicated that EC, EY, TPC, TFC, and DPPH were significantly influenced by power ratios, temperatures, and exposure time. Moreover, the proposed models effectively characterized the variations in different properties during the extraction process. The optimized extraction conditions, aimed at minimizing EC while maximizing EY, TPC, TFC, and DPPH radical scavenging activity, were demonstrated as an ultrasonic power ratio of 44.4 %, a temperature of 60 °C, and an extraction time of 17.7 min. Optimization led to 563 kJ for EC, 7.85 % for EY, 47.21 mg GAE/mL for TPC, 96.8 mg QE/mL for TFC, and 50.15 % for DPPH radical scavenging activity. The results emphasized that the UAE process exhibited characteristics of endothermicity and spontaneity. The results provide valuable insights that could inform the enhancement of extraction processes, potentially benefiting industrial utilization and pharmaceutical formulations.

A fast scanner of optical-resolution photoacoustic microscopy is inherently vulnerable to perturbation, leading to severe image distortion and significant misalignment among multiple 2D or 3D images. Restoration and registration of these images is critical for accurately quantifying dynamic information in long-term imaging. However, traditional registration algorithms face a great challenge in computational throughput. Here, we develop an unsupervised deep learning based registration network to achieve real-time image restoration and registration. This method can correct artifacts from B-scan distortion and remove misalignment among adjacent and repetitive images in real time. Compared with conventional intensity based registration algorithms, the throughput of the developed algorithm is improved by 50 times. After training, the new deep learning method performs better than conventional feature based image registration algorithms. The results show that the proposed method can accurately restore and register the images of fast-scanning photoacoustic microscopy in real time, offering a powerful tool to extract dynamic vascular structural and functional information.

This study examined the impacts of ultrasonic power (0, 150, 300, 450, 600, and 750 W) and ultrasonic durations (3, 6, 9, 12, and 15 min) on the physicochemical properties and microstructure of diacylglycerol (DAG)-loaded emulsions stabilized with soybean protein isolate (SPI) and sodium alginate (SA). The findings indicated that the smallest particle size, zeta potential, and contact angle for SPI-SA-DAG emulsions were respectively 5.58 μm, −49.85 mV, and 48.65°, achieved at an ultrasonic power of 450 W. The emulsification properties, loss modulus, storage modulus, and apparent viscosity of the emulsions were optimal at this power setting and at a duration of 9 min. Analytical techniques, including confocal laser scanning-, scanning electron-, and atomic force microscopy, revealed that ultrasonication significantly altered emulsion aggregation state, with the surface roughness (Rq) being minimized at 450 W. These results demonstrated that the stability of SPI-SA-DAG emulsions can be effectively enhanced by an appropriate ultrasonic treatment at 450 W for 9 min. This research provides theoretical support for the broad application of sonication techniques in the food industry.

To obtain high-quality cherry products, ultrasound (US) combined with five chemical pretreatment techniques were used on cherry prior to radio frequency vacuum drying (RFV), including carboxymethyl cellulose coating (CMC), cellulase (CE), ethanol (EA), isomaltooligosaccharide (IMO), and potassium carbonate + ethyl oleate (PC + AEEO). The effect of different pretreatments (US-CMC, US-CE, US-EA, US-IMO, US-(PC + AEEO)) on the drying characteristics, quality properties, texture, and sensory evaluation of cherries was evaluated. Results showed that the dehydration time and energy consumption were decreased by 4.17 − 20.83 % and 3.22 − 19.34 %, respectively, and the contents of individual sugars, soluble solid, total phenolics (TPC), natural active substances, total flavonoids (TFC), and antioxidant properties (DPPH, ABTS and FRAP) were significantly increased after US combined with five chemical treatments (P

SBS (styrene–butadiene–styrene block copolymer) is currently the most widely used asphalt modifier, and SBS modified asphalt is usually prepared by high-speed shearing. This paper combines the cavitation effect of ultrasonic to assist in the preparation of SBS modified asphalt, and conducts numerical simulation and rheological properties research on the cavitation bubbles in the molten SBS modified asphalt fluid. The cavitation bubbles in the modified asphalt fluid will expand and contract as the pressure changes inside and outside the bubbles. When the cavitation bubble is compressed to the minimum and the pressure inside the bubble reaches 1.94 × 105Pa, the direction of the velocity vector near the cavitation bubble will change with the expansion and compression of the bubble. The expansion–contraction process of a single cavitation bubble can release 6.41 × 10-7J of energy, thus breaking the long bonds in asphalt and generating a large number of free radicals react with the unsaturated C = C bonds in the SBS molecules. According to the preparation process of modified asphalt, the influence of ultrasonic wave on rheological property of modified asphalt was studied through experiments. The results show that ultrasonic treatment can enhance the elasticity of asphalt and improve the temperature sensitivity of asphalt. With the increase of ultrasonic treatment time, the anti-rutting deformation ability of SBS modified asphalt is greatly improved. At the same temperature, the recovery rate of asphalt also increases with the increase of ultrasonic treatment time, and the non-recoverable compliance (Jnr) decreases Combined with the numerical simulation of cavitation bubbles, the ultrasonic process is added to asphalt production, which is of great significance for the green production of modified asphalt and the improvement of the rheological properties of modified asphalt.

Hydrogen cyanide (HCN) is a toxic industrial chemical, necessitating low-level detection capabilities for safety and environmental monitoring. This study introduces a novel approach for detecting hydrogen cyanide (HCN) using a clamp-type custom quartz tuning fork (QTF) integrated with a dual-tube acoustic micro-resonator (AmR) for enhanced photoacoustic gas sensing. The design and optimization of the AmR geometry were guided by theoretical simulation and experimental validation, resulting in a robust on-beam QEPAS (Quartz-Enhanced Photoacoustic Spectroscopy) configuration. To boost the QEPAS sensitivity, an Erbium-Doped Fiber Amplifier (EDFA) was incorporated, amplifying the laser power by approximately 286 times. Additionally, a transformer-based U-shaped neural network, a machine learning filter, was employed to refine the photoacoustic signal and reduce background noise effectively. This combination yielded a significantly low detection limit for HCN at 0.89 parts per billion (ppb) with a rapid response time of 1 second, marking a substantial advancement in optical gas sensing technologies. Key modifications to the QTF and innovative use of AmR lengths were validated under various experimental conditions, affirming the system's capabilities for real-time, high-sensitivity environmental monitoring and industrial safety applications. This work not only demonstrates significant enhancements in QEPAS but also highlights the potential for further technological advancements in portable gas detection systems.

A high-sensitivity photoacoustic spectroscopy (PAS) sensor based on differential Helmholtz photoacoustic cell (DHPAC) with dense spot pattern is reported in this paper for the first time. A multi-pass cell based on two concave mirrors was designed to achieve a dense spot pattern, which realized 212 times excitation of incident laser. A finite element analysis was utilized to simulate the sound field distribution and frequency response of the designed DHPAC. An erbium-doped fiber amplifier (EDFA) was employed to amplify the output optical power of the laser to achieve strong excitation. In order to assess the designed sensor's performance, an acetylene (C2H2) detection system was established using a near infrared diode laser with a central wavelength 1530.3 nm. According to experimental results, the differential characteristics of DHPAC was verified. Compared to the sensor without dense spot pattern, the photoacoustic signal with dense spot pattern had a 44.73 times improvement. The minimum detection limit (MDL) of the designed C2H2-PAS sensor can be improved to 5 ppb when the average time of the sensor system is 200 s.

Drying, as a critical step in the production of air-dried beef, has a direct impact on the quality of the final product. Innovatively, a composite system incorporating contact ultrasound (CU) and infrared radiation (IR) as auxiliary measures within a hot air drying (HAD) framework was built in this research, and the effects of these techniques on the drying kinetics, protein denaturation, and moisture transformation of air-dried beef were investigated. In comparison to HAD treatment, the integrated CU and IR (CU-IRD) system displayed marked enhancements in heat and moisture transport efficiency, thereby saving 36.84% of time expenditure and contributing favorably to the improved moisture distribution of the end-product. This was mainly ascribed to the denaturation of myosin induced by IR thermal effect and the micro-channel produced by CU sponge effect, thus increasing T2 relaxation time and the proportion of free water. In conclusion, the composite system solved the problem of surface hardening and reduces hardness and chewiness of air-dried beef by 40.42% and 45.25% respectively, but inevitably increased the energy burden by 41.60%.

To investigate the effect of ultrasound treatment on the flavor profile of beef during postmortem aging, a comprehensive analysis of beef flavor was conducted at 0, 7, and 12 d of aging using sensory evaluation and electronic nose. Furthermore, the key volatile flavor compounds were identified using gas chromatography-mass spectrometry (GC–MS), and the odor activity value (OAV) was further evaluated. In addition, the primary pathway involved in flavor formation during beef aging after ultrasound treatment was explored. The results indicated that ultrasound enhanced the flavor profile of beef during postmortem aging by modifying the OAV of hexanal, heptanal, octanal, nonanal, decanal, (Z)-2-nonenal, dodecanal, pentanal, 1-octen-3-ol, octanoic acid, and 2-pentylfuran. Lipid oxidation was a crucial pathway through which ultrasound promoted the generation of volatile flavor compounds in beef, confirmed by the improved oxidation level of fatty acids, particularly monounsaturated ones. The study indicates that ultrasound technology can be regarded as an effective method for enhancing the beef flavor profile during postmortem aging.