Institute of Acoustics, Chinese Academy of Sciences

Wang X., Zhong X., Bai L., Xu J., Gong F., Dong Z., Yang Z., Zeng Z., Liu Z., Cheng L.

Ultrasound (US)-triggered sonodynamic therapy (SDT) that enables noninvasive treatment of large internal tumors has attracted widespread interest. For improvement in the therapeutic responses to SDT, more effective and stable sonosensitizers are still required. Herein, ultrafine titanium monoxide nanorods (TiO1+x NRs) with greatly improved sono-sensitization and Fenton-like catalytic activity were fabricated and used for enhanced SDT. TiO1+x NRs with an ultrafine rodlike structure were successfully prepared and then modified with polyethylene glycol (PEG). Compared to the conventional sonosensitizer, TiO2 nanoparticles, the PEG-TiO1+x NRs resulted in much more efficient US-induced generation of reactive oxygen species (ROS) because of the oxygen-deficient structure of TiO1+x NR, which predominantly serves as the charge trap to limit the recombination of US-triggered electron-hole pairs. Interestingly, PEG-TiO1+x NRs also exhibit horseradish-peroxidase-like nanozyme activity and can produce hydroxyl radicals (•OH) from endogenous H2O2 in the tumor to enable chemodynamic therapy (CDT). Because of their efficient passive retention in tumors post intravenous injection, PEG-TiO1+x NRs can be used as a sonosensitizer and CDT agent for highly effective tumor ablation under US treatment. In addition, no significant long-term toxicity of PEG-TiO1+x NRs was found for the treated mice. This work highlights a new type of titanium-based nanostructure with great performance for tumor SDT.

Wang X., Mao D., Li X.

Bearing fault diagnosis is an important part of rotating machinery maintenance. Existing diagnosis methods based on single-modal signals not only have unsatisfactory accuracy, but also bear the inherent risk of being misguided by single-modal signal noise. A new method is put forward that fuses multi-modal sensor signals, i.e. the data collected by an accelerometer and a microphone, to realize more accurate and robust bearing-fault diagnosis. The proposed method extracts features from raw vibration signals and acoustic signals, and fuses them using the 1D-CNN-based networks. Extensive experimental results obtained on ten groups of bearings are used to evaluate the performance of the proposed method. By analyzing the loss function and accuracy rate under different SNRs, it is empirically found that the proposed method achieves higher rate of diagnosis accuracy than the algorithms based on a single-modal sensor. Moreover, a visualization analysis is also conducted to investigate the inner mechanism of the proposed 1D-CNN-based method.

Liang S., Xiao X., Bai L., Liu B., Yuan M., Ma P., Pang M., Cheng Z., Lin J.

The development of highly efficient, multifunctional, and biocompatible sonosensitizer is still a priority for current sonodynamic therapy (SDT). Herein, a defect-rich Ti-based metal-organic framework (MOF) (D-MOF(Ti)) with greatly improved sonosensitizing effect is simply constructed and used for enhanced SDT. Compared with the commonly used sonosensitizer TiO2 , D-MOF(Ti) results in a superior reactive oxygen species (ROS) yield under ultrasound (US) irradiation due to its narrow bandgap, which principally improves the US-triggered electron-hole separation. Meanwhile, due to the existence of Ti3+ ions, D-MOF(Ti) also exhibits a high level of Fenton-like activity to enable chemodynamic therapy. Particularly, US as the excitation source of SDT can simultaneously enhance the Fenton-like reaction to achieve remarkably synergistic outcomes for oncotherapy. More importantly, D-MOF(Ti) can be degraded and metabolized out of the body after completion of its therapeutic functions without off-target toxicity. Overall, this work identifies a novel Ti-familial sonosensitizer harboring great potential for synergistic sonodynamic and chemodynamic cancer therapy.

Zhang T., Jin J., Chen J., Fang Y., Han X., Chen J., Li Y., Wang Y., Liu J., Wang L.

Developing active single-atom-catalyst (SAC) for alkaline hydrogen evolution reaction (HER) is a promising solution to lower the green hydrogen cost. However, the correlations are not clear between the chemical environments around the active-sites and their desired catalytic activity. Here we study a group of SACs prepared by anchoring platinum atoms on NiFe-layered-double-hydroxide. While maintaining the homogeneity of the Pt-SACs, various axial ligands (−F, −Cl, −Br, −I, −OH) are employed via a facile irradiation-impregnation procedure, enabling us to discover definite chemical-environments/performance correlations. Owing to its high first-electron-affinity, chloride chelated Pt-SAC exhibits optimized bindings with hydrogen and hydroxide, which favor the sluggish water dissociation and further promote the alkaline HER. Specifically, it shows high mass-activity of 30.6 A mgPt−1 and turnover frequency of 30.3  H2 s−1 at 100 mV overpotential, which are significantly higher than those of the state-of-the-art Pt-SACs and commercial Pt/C catalyst. Moreover, high energy efficiency of 80% is obtained for the alkaline water electrolyser assembled using the above catalyst under practical-relevant conditions. Establishing robust structure/performance correlations is critical for the development of single-atom-catalysts with improved activity. Here, the axial ligand on Pt single-atom-catalyst is precisely adjusted and studied, showing that the ligand’s first electron affinity is crucial for the catalysis.

Liu W., Liu J., Hao C., Gao Y., Wang Y.

Multichannel adaptive signal detection uses test and training data jointly to form an adaptive detector to determine whether a target exists. The resulting adaptive detectors typically possess constant false alarm rate (CFAR) properties; thus, no additional CFAR processing is required. In addition, a filtering process is also not required because the filtering function is embedded in the adaptive detector. Adaptive detection typically exhibits better detection performance than the filtering-then-CFAR detection technique. It has been approximately 35 years since the first multichannel adaptive detector was proposed by Kelly in 1986. However, there are few overview articles on this topic. Thus, in this study, we present a tutorial overview of multichannel adaptive signal detection with an emphasis on the Gaussian background. We discuss the main design criteria for adaptive detectors, investigate the relationship between adaptive detection and filtering-then-CFAR detection techniques, investigate the relationship between adaptive detectors and adaptive filters, summarize typical adaptive detectors, present numerical examples, provide a comprehensive literature review, and discuss potential future research tracks.

Li A., Liu W., Zheng C., Fan C., Li X.

For challenging acoustic scenarios as low signal-to-noise ratios, current speech enhancement systems usually suffer from performance bottleneck in extracting the target speech from the mixtures within one step. To address this issue, we propose a novel complex spectral mapping approach with a two-stage pipeline for monaural speech enhancement in the time-frequency domain. The proposed algorithm aims to decouple the primal problem into multiple sub-problems, which follows the classic proverb, “two heads are better than one”. More specifically, in the first stage, only magnitude is estimated, which is incorporated with the noisy phase to obtain a coarse complex spectrum estimation. To facilitate the previous estimation, in the second stage, an auxiliary network serves as the post-processing module, where residual noise is further suppressed and the phase information is effectively modified. The global residual connection strategy is adopted in the second stage to accelerate the training convergence speed. To alleviate the parameter burden caused by the multi-stage pipeline, we propose a light-weight temporal convolutional module, which substantially decreases the trainable parameters and obtains even better objective performance over the original version. We conduct extensive experiments on three standard corpora, including WSJ0-SI84, DNS Challenge dataset, and Voice Bank + DEMAND dataset. Objective test results demonstrate that our proposed approach achieves state-of-the-art performance over previous advanced systems under various conditions. Meanwhile, subjective listening test results further validate the superiority of our proposed method in terms of subjective quality.

Ozanich E., Gerstoft P., Niu H.

This paper examines the relationship between conventional beamforming and linear supervised learning, then develops a nonlinear deep feed-forward neural network (FNN) for direction-of-arrival (DOA) estimation. First, conventional beamforming is reformulated as a real-valued, linear inverse problem in the weight space, which is compared to a support vector machine and a linear FNN model. In the linear formulation, DOA is quickly and accurately estimated for a realistic array calibration example. Then, a nonlinear FNN is developed for two-source DOA and for K-source DOA, where K is unknown. Two training methodologies are used: exhaustive training for controlled accuracy and random training for flexibility. The number of FNN model hidden layers, hidden nodes, and activation functions are selected using a hyperparameter search. In plane wave simulations, the 2-source FNN resolved incoherent sources with 1° resolution using a single snapshot, similar to Sparse Bayesian Learning (SBL). With multiple snapshots, K-source FNN achieved resolution and accuracy similar to Multiple Signal Classification and SBL for an unknown number of sources. The practicality of the deep FNN model is demonstrated on Swellex96 experimental data for multiple source DOA on a horizontal acoustic array.

Li S., Yan Q., Liu P.

Recent progress in vision-based fire detection is driven by convolutional neural networks. However, the existing methods fail to achieve a good tradeoff among accuracy, model size, and speed. In this paper, we propose an accurate fire detection method that achieves a better balance in the abovementioned aspects. Specifically, a multiscale feature extraction mechanism is employed to capture richer spatial details, which can enhance the discriminative ability of fire-like objects. Then, the implicit deep supervision mechanism is utilized to enhance the interaction among information flows through dense skip connections. Finally, a channel attention mechanism is employed to selectively emphasize the contribution between different feature maps. Experimental results demonstrate that our method achieves 95.3% accuracy, which outperforms the suboptimal method by 2.5%. Moreover, the speed and model size of our method are 3.76% faster on the GPU and 63.64% smaller than the suboptimal method, respectively.

Wang Y., Han J.Q., Li C.H.

The deterioration mechanism and fatigue fracturing evolution of granite with two pre-existing flaws experiencing freeze–thaw (F-T) treatment are investigated in this work. The flaws in the rock sample were prepared as a combination of a horizontal flaw with an upper inclined flaw above the horizontal flaw according to the joint characteristics in an open pit slope. In-situ acoustic emission monitoring combined with the post-test 3D computed tomography (CT) technique was employed to reveal the fracture evolution behaviors of rock treated with 0, 50, and 80 freeze–thaw cycles. Results show that the freeze–thaw damage impacts the frost heaving force, cyclic deformation, AE activates, crack coalescence pattern and fatigue life of the granite samples. The accumulative AE count/energy decreases with increasing number of freeze–thaw cycles, and the accumulated AE count/energy in a loading stage gradually grows faster. In addition, AE spectral frequency analysis reveals the impact of previous freeze–thaw damage on the formation of the crack scale, the sample is prone to producing large scale cracks under high freeze–thaw treatment. Moreover, 3D reconstructed CT images present an internal crack network pattern, and the most striking finding is that a simple crack network forms for a sample experiencing high F-T fatigue damage. It is suggested that deterioration of the rock bridge structure is strongly impacted by the accumulative freeze–thaw damage. The testing results are helpful to understand the influence of freeze–thaw and fatigue loading on the fracture evolution characteristics of rock in cold regions.

Han G., Zhou Z., Zhang T., Wang H., Liu L., Peng Y., Guizani M.

Underwater gliders are being increasingly used for data collection, and the development of methods for optimizing their routes has become a topic of active research. With this aim in mind, in this paper, a complete-coverage path-planning obstacle-avoidance (CCPP-OA) algorithm that ensures avoidance for underwater gliders in sea areas with thermoclines is proposed. First, the entire sea area with the thermocline layer is stratified based on the underwater communication radii of the gliders. Next, the glide angles and initial navigation points of the gliders are determined based on their communication radii at each level to construct the complete-coverage path. Finally, by combining the ant colony algorithm and the determined initial navigation points, the complete-coverage path with obstacle avoidance is planned for the gliders. Simulation results show that the proposed CCPP-OA algorithm enables complete coverage of the entire sea area. Furthermore, the length of the planned path is shorter and the amount of energy consumed is less than that of other algorithms.