The rapid monitoring of fluorine⁃containing substances in wastewater from the electrical industry serves as a critical approach for effectively managing environmental risks posed by emerging contaminants like perfluorooctanoic acid (PFOA)，holding significant implications for building a clean and beautiful world. This study develops a novel detection sensor based on gold nanoparticle⁃doped molecularly imprinted polymers (Au@MIP). By optimizing the o⁃phenylenediamine (o⁃PD)⁃chloroauric acid electropolymerization process (no⁃PD∶nPFOA=10∶1，30 cyclic scans)，highly selective PFOA detection is achieved with recovery rates of 109%~114%. Integrated with an early⁃warning module，the system enables quantitative PFOA analysis within 45 min across a range of 0.04~100 ng·L⁻¹，accomplishing both trace⁃level environmental monitoring and precise quantification in high⁃concentration contamination areas. The results demonstrate the superior performance of the Au@MIP electrode⁃based sensor，providing technical support for in⁃situ monitoring of industrial wastewater.
Sludge dewatering is a significant process and challenge in sewage treatment. Different Fe⁃modified sludge biochar derived at four different pyrolysis temperatures was used as the particle electrode in the three⁃dimensional neutral electro⁃Fenton. The performance and mechanisms of the three⁃dimensional neutral electro⁃Fenton for the sludge dewatering were analyzed. The results showed that BC@Fe⁃800 had the strongest improvement in sludge dewatering performance，with a specific resistance to filtration (SRF)、moisture content of filter cake and SV30 min reduction rate of 81.87%，35.61% and 31.12%. The mechanisms were the conversion of bound EPS and intracellular bound water into dissolved EPS and free water after the oxidation of free radicals. After treatment of the three⁃dimensional neutral electro⁃Fenton，the hydrophobic tryptophan protein content in the sludge increased，and the bacterial community structure underwent a significant change at both the phylum and genus levels. Additionally，the abundance of acid producing bacteria decreased，while the abundance of organic pollutant degrading bacteria，denitrifying bacteria，and phosphorus removing bacteria increased. Therefore，it might contribute to the resource utilization of sludge. The optimization results of the three⁃dimensional neutral Fenton system indicated that the optimal treatment conditions were 5% particle electrode addition and 20 mA·cm-2 current density. These results can provide the theoretical foundation for the resource utilization of sludge.
Obstacle detection technology based on road point cloud data is crucial in intelligent transportation systems and autonomous driving. The traditional density⁃based spatial clustering (DBSCAN) algorithm has poor clustering effect when processing high⁃dimensional or different density area data due to inefficient distance measurement and difficulty in determining parameter combinations. Therefore，a road obstacle point cloud clustering method based on improved DBSCAN is proposed. Firstly，the isolated kernel function is used to improve the traditional distance measurement method when determining the Eps area，which improves the adaptability and accuracy of DBSCAN clustering for different density areas. Secondly，in view of the shortcomings of the Cheetah Optimizer (CO) in information sharing and iterative updating，a CO optimization algorithm based on timely updating mechanisms and compatible metric strategies (TCCO) is proposed. The real⁃time update operation ensures that the excellent information of each iteration is communicated and shared in time，and the elimination mechanism is optimized based on non⁃dominated sorting and crowding distance during global update to balance the global search and local development capabilities，thereby improving the convergence speed and accuracy. Finally，the Eps field is improved by using the isolation metric，and the DBSCAN clustering is optimized by using TCCO to adaptively determine the parameters，thereby improving the clustering accuracy and efficiency. The simulation results show that the proposed TCCO⁃DBSCAN algorithm has significantly improved F⁃Measure，ARI and NMI indicators compared with CO⁃DBSCAN，SSA⁃DBSCAN，DBSCAN，and KMC methods，and has better clustering accuracy. The experimental verification of obstacle clustering of lidar point cloud data shows that TCCO⁃DBSCAN can effectively adapt to the changes in point cloud data density，obtain better road obstacle clustering effects，and provide support for obstacle detection in assisted driving.
The personalized Head⁃Related Transfer Function (HRTF) plays a crucial role in implementing immersive audio experiences. However, existing public HRTF databases often lack sufficient scale and diversity to effectively support the training of deep learning models, thereby hindering the advancement of personalized HRTF prediction. To address this challenge, this study constructed a large⁃scale HRTF database tailored for deep learning applications, integrating real HRTF measurements obtained in anechoic chambers and simulated HRTFs derived from high⁃precision head scans. Using both objective acoustic metrics and subjective listening experiments, the study systematically analyzed the significant differences between generic and personalized HRTFs across key frequency bands, particularly in the high⁃frequency range (>10 kHz). These differences were found to have a substantial impact on auditory localization accuracy, with the high⁃improvement group achieving an average enhancement of 5.9°. The database and its analysis not only provide a comprehensive data foundation and validation resource for deeplearning⁃driven HRTF prediction, but also offer empirical insights into the significance of personalization and the optimization of virtual auditory experiences.
Photoacoustic imaging technology is a biomedical imaging technology combining the merits of both acoustic imaging and optical imaging. The ultrasonic transducer determines the key imaging performance such as imaging quality and resolution of photoacoustic imaging directly. We developed a spherical focusing ultrasonic transducer based on PVDF (Polyvinlidene Fluoride) piezoelectric film. Experiments showed that the center frequency of the transducer is 15.5 MHz，and the -6 dB bandwidth is 14.89 MHz (96.1%). We applied the ultrasonic transducer to a photoacoustic microscope system and tested the lateral resolution and axial resolution of the photoacoustic microscope by imaging the resolution plate and a singe hair strand example respectively. The experimental results showed that the lateral resolution of the photoacoustic microscope is 125 μm，which is consistent with the theoretical result of 123.5 μm; the axial resolution is 113.6 μm，which accords with the theoretical result of 88 μm. Finally，we applied our self⁃developed ultrasonic transducer and photoacoustic imcroscope to image the ear of a living mouse，and obtained clear non⁃invasive image of the blood vessel network in the mouse ear. Our study indicates that the focused ultrasonic transducer based on PVDF film can provide high imaging resolution and quality for photoacoustic microscope，and has good prospects for biomedical applications.
To overcome the limitations of traditional materials in low⁃frequency noise reduction，a MAM (Membrane⁃type acoustic metamaterial) composed of double⁃layer membranes，mass rings，and cavities is proposed in this paper. The sound absorption mechanism is elucidated through the analysis of its resonance modes，equivalent parameters，and acoustic impedance. Utilizing finite element analysis，the sound absorption coefficient within the frequency range of 100 to 600 Hz is calculated，and the effects of the geometric parameters of the mass ring，membrane，and cavity on the sound absorption coefficients are discussed and compared. Besides，the sound absorption performance is optimized by parallel connecting multiple MAM units and changing the position of the mass ring. These results indicate that the sound absorption performance of the double⁃layer MAM cell structure at low frequencies is better than that of the conventional structure，and the corresponding operating frequencies can be altered by changing the geometric parameters，positions，and parallel connection of multiple units，thereby increasing the sound absorption coefficient and broadening the bandwidth. This study can provide implications for the optimization of sound absorption performance by MAM.
An objective evaluation method for spatial perception quality is proposed in this paper，integrating auditory filter model with acoustic feature and specifically tailored for binaural Ambisonics reproduction under low⁃reverberation conditions. First，the binaural input signals are processed through auditory filter models to extract objective spatial quality metrics. These metrics are combined with established spatial and general audio quality metrics to form a comprehensive acoustic feature set. A Gaussian process regression(GPR) model is then implemented to establish the mapping relationship between this feature set and subjective ratings，creating an objective quality prediction model. In order to validate the proposed method，subjective listening tests were conducted using binaural speech signals rendered by various Ambisonics reproduction algorithms in simulated anechoic and low⁃reverberation environments. The subjective ratings obtained were used to train and evaluate the objective model by cross⁃validation. The results demonstrate a significant improvement in prediction accuracy compared to existing evaluation models. Furthermore，a publicly available Ambix dataset is used for external validation，confirming the strong generalizability and robustness of the proposed model.
Active noise control headrests have attracted widespread attention in practical applications due to their good low⁃frequency performance and simple structure. For active noise reduction headrests with limited adaptive convergence speed, the noise reduction at the ears will significantly decrease when the human head deviates from the calibrated location. The variation law of noise reduction caused by the translation of the human head is investigated in active noise reduction headrests with two typical placements of secondary sources. Firstly, an analytical model for active noise reduction headrest with fixed control filter coefficients is established considering the acoustic scattering of a rigid sphere. Then, numerical simulations are conducted to verify the accuracy of the proposed model, and the attenuation of noise reduction at the ear after head translation is analyzed in the diffuse sound field. It is found that the attenuation of noise reduction due to the forward and backward translation is smaller when secondary sources are placed on both sides of the headrest, while the attenuation due to the leftward and rightward translation is smaller when secondary sources are placed on the shoulder of the seat. The main reason is that the changes in the secondary path during head translation are affected by the placement of secondary sources. Finally, experiments are conducted in the reverberation room to verify the numerical simulation results.
Anomalous refraction，key to wave acoustics，can break through the limitations of the conventioned laws of refraction and enables unconventional control of sound wave propagation. Recently，acoustic metasurfaces have shown significant progress in precise sound manipulation，but traditional designs face limitations like narrow bandwidth and strong dispersion，restricting applications in acoustic cloaking and noise reduction. This paper presents a machine learning⁃assisted broadband anomaly refractive acoustic metasurface comprising 16 subwavelength units with hybrid multiple resonances. Each unit achieves broadband high transmission (>98%) and strong linear phase fitting (>97%)，enabling constant refraction angles (
In the recent research at our laboratory，Faraday waves on liquid layers of concave bottoms were observed to manifest themselves as some polygonal patterns. More importantly，these shallow⁃water gravity waves were found to be analogous to the collective excitations in Bose⁃Einstein condensates. In that research，the water containers employed had the geometries of smooth bottom profiles，typically of the paraboloidal ones. The present work extends the investigation to the geometries of conical bottom shapes，specifically examining how the non⁃smoothness at the cone apex impacts the polygonal patterns and associated wave characteristics. It is observed that accompanying a pure angular⁃mode excitation is a “central breathing”，a radially symmetric vertical oscillation with a period exactly half that of the angular mode. This phenomenon is caused by the effect of the non⁃smooth conical apex on the polygonal patterns，further indicating the importance of the geometric constraint on the pattern formation. Also observed are other interesting phenomena including the polygonal patterns of even higher orders，mode competition，and radial⁃angular hybrid modes. By combining the numerical solutions to the mild⁃slope equation with numerical simulations and experimental observations，this study then elucidates the regulatory mechanisms of cone angle and water depth on modal eigenfrequencies，damping coefficients，and stability. These findings have deepened our understanding of the impacts of bottom topography on nonlinear shallow⁃water gravity waves.
In this paper，an ultrathin acoustic coding metasurface based on a coiling up structure is proposed. This structure is composed of 6×6 spatial coiling units，and the thickness of each unit is only one⁃tenth of the wavelength of the operating frequency. By adjusting the length of the transverse plate of the coiling structure，the phase of the transmitted sound wave can be effectively controlled，and the "0" and "1" digital coding of the units can be achieved. The coding units are arranged periodically in a certain order to form metasurfaces with different coding sequences，so as to effectively regulate the acoustic field beam. In order to verify the effectiveness of the metasurface designed in this paper，a coding metasurface is constructed by using 6×6 units，and the finite element simulation of the scattered acoustic field of the designed metasurface is carried out. The beam splitting effect on the transmitted sound wave is observed to confirm its acoustic performance. The simulation results show that the metasurface exhibits excellent performance at a frequency of 2 kHz，and can effectively realize different beam splitting functions. Our metasurface has a compact size and can be manipulated flexibly，which shows potential application prospect in acoustic detection，acoustic imaging and other fields.
A high⁃performance qutrit CZ gate is an indispensable component for the development of high⁃level quantum computing and holds great significance for achieving fault⁃tolerant quantum computing. This paper proposes an optimization scheme for the CZ gate in superconducting qutrits. The main principle of the scheme is to facilitate a tunable ZZ coupling interaction between qutrits with the assistance of the coupler，thus enabling the implementation of the qutrit CZ gate. Through simulation studies，the feasibility of the qutrit CZ gate has been demonstrated. To enhance its precision，we conducted an in⁃depth analysis of the primary factor influencing the performance of the qutrit CZ gate during its implementation，namely，the origin of non⁃adiabatic state leakage. To mitigate state leakage，we designed an optimization scheme for the flux pulse based on the Slepian window function and dynamic adjustment of the non⁃adiabatic factor，and verified the efficacy of the scheme. The optimization scheme for the CZ gate in qutrit proposed in this paper can effectively suppress the state leakage in the process of qutrit CZ gate and enhances adiabatic performance. The scheme can shorten the ramping time of the flux waveform to 70 ns and keep the overall state leakage at
High intensity focused ultrasound (HIFU)，as a cutting⁃edge non⁃invasive treatment technique，has made significant progress in clinical applications，and the cavitation effect is one of the key mechanisms for its therapeutic effect. Accurately distinguishing the characteristic components of stable and inertial cavitation signals generated during HIFU treatment plays a crucial role in precise regulation of HIFT treatment efficacy while ensuring its treatment safety. However，traditional passive cavitation detection (PCD) methods have certain limitations in analyzing the cavitation signals generated during HIFU treatment，making it difficult to accurately extract the relevant complex components in the signals and accurately analyze the states of cavitation activities. Therefore，this study innovatively proposes a singular value decomposition method based on Hankel matrix reconstruction (H⁃SVD) to achieve more accurate analyses of the cavitation signals obtained during HIFU treatment. This method first reconstructs the one⁃dimensional time⁃domain signal received by a single element PCD transducer into a Hankel matrix，and then uses singular value decomposition (SVD) algorithm to extract multi⁃scale features of the signal，achieving effective separation of HIFU fundamental frequency and harmonic signals，subharmonics/superharmonics，and broadband noise signals. Compared with traditional PCD techniques，the H⁃SVD method exhibits significant advantages in preserving detailed information of various frequency components of the signal，and can more accurately characterize the dynamic evolution of cavitation characteristics under different acoustic pressures. This method would provide a powerful analytical tool for a deeper understanding of the physical and chemical mechanisms induced by stable and inertial cavitation，and also builds up the technical foundation for real⁃time monitoring and precise regulation of cavitation effects generated during HIFU therapy.
Active noise control (ANC) in complex environments such as in⁃vehicle road noise typically requires a large number of sensors to achieve satisfactory noise control performance，which imposes substantial computational burdens and leads to slow convergence when conventional adaptive algorithms are employed. To address this problem，this paper proposes a Multi⁃Iterative Pre⁃Regularized Frequency Domain Filtered⁃x Least Mean Square (MIPR⁃FDFxLMS) algorithm，developed upon the Frequency Domain Filtered⁃x LMS (FDFxLMS) framework. The introduced multi⁃iteration scheme accelerates convergence without compromising computational efficiency，while the pre⁃regularization method enhances algorithmic stability. Simulation results using measured vehicle road noise data have verified that the proposed MIPR⁃FDFxLMS algorithm achieves significantly faster convergence compared to conventional approaches，demonstrating its viability for real⁃time active road noise control applications.
Neuromorphic circuits based on memristor crossbar arrays offer a highly promising technological route for energy⁃efficient implementation of neural network computation. However，existing schemes often require extensive analogue⁃to⁃digital conversion processes，leading to an efficiency bottleneck. This paper proposes a fully analog neural network architecture based on a 1T1R (1 Transistor 1 Resistor) memristor crossbar array and CMOS (Complementary Metal⁃Oxide⁃Semiconductor) activation functions，as well as a customized training method. By utilizing the 1T1R memristor crossbar array to achieve analog computation of matrix multiplication，the continuous tunable conductance of the memristors is leveraged to map neural network weights，and the parallel multiply⁃accumulate operations are performed in accordance with Ohm's Law and Kirchhoff's Law. Additionally，diverse CMOS analog circuits，such as pseudo⁃ReLU (Rectified Linear Unit)，Sigmoid，and Tanh，are designed to implement nonlinear activation functions. Through the customized training method that optimizes analog hardware characteristics，an accuracy rate of 98% is achieved on the MNIST (Modified National Institute of Standards and Technology) handwritten digit recognition task. Our results demonstrate the feasibility of the proposed architecture despite the programming noise of memristors，providing a new approach to improving the computational efficiency.
Traditional manual surveys and interviews have proven effective for mental assessment and screening among college students，yet they require substantial human and material resources. Benefiting from the massive data generated by campus informatization，machine learning techniques have garnered increasing attention from educators and researchers for predicting student mental health. However，students' intrinsic mental states are highly concealed，making sample labeling particularly challenging⁃especially for negative samples (i.e.，students without mental health issues)，which are difficult to define. Previous studies utilized evaluation results from psychometric scales (e.g.，Symptom Checklist⁃90) as sample labels for modeling，but the highly subjective nature of these scales compromises the authenticity and reliability of conclusions，significantly impairing model performance. To address the critical challenge of defining negative samples caused by the extreme concealment of students' true mental states，we propose PUMPS (Positive Unlabeled Learning for Mental Prediction of Students)，a mental health prediction method based on PU learning (Positive⁃Unlabeled Learning). This approach innovatively introduces the PU learning paradigm and integrates multi⁃view student⁃related data to construct a collaborative training framework with multiple ensemble classifiers. By leveraging complementary information across diverse data views，the model's performance is enhanced. Experimental results on real⁃world datasets demonstrate that the proposed method outperforms traditional approaches across four key metrics: accuracy，precision，recall，and F1⁃score. Notably，it achieves a 20.73 percentage point improvement in recall over the second⁃best method.
