Search Results (4,980)

Aerogels (AGs) are highly porous, low-density, disordered, ultralight macroscopic materials with immense surface areas. Traditionally synthesized using aqueous sol–gel chemistry, starting by molecular precursors, the nanoparticles (NPs) dispersions gelation method is nowadays the most used procedure to obtain AGs with improved crystallinity and broader structural, morphological and compositional complexity. The Sol–gel process consists of preparing a solution by hydrolysis of different precursors, followed by gelation, ageing and a drying phase, via supercritical, freeze-drying or ambient evaporation. AGs can be classified based on various factors, such as appearance, synthetic methods, chemical origin, drying methods, microstructure, etc. Due to their nonpareil characteristics, AGs are completely different from common NPs, thus covering different and more extensive applications. AGs can be applied in supercapacitors, acoustic devices, drug delivery, thermal insulation, catalysis, electrocatalysis, gas absorption, gas separation, organic and inorganic xenobiotics removal from water and air and radionucleotides management. This review provides first an analysis on AGs according to data found in CAS Content Collection. Then, an AGs’ classification based on the chemical origin of their precursors, as well as the different methods existing to prepare AGs and the current optimization strategies are discussed. Following, focusing on AGs of inorganic origin, silica and metal oxide-based AGs are reviewed, deeply discussing their properties, specific synthesis and possible uses. These classes were chosen based on the evidence that they are the most experimented, patented and marketed AGs. Several related case studies are reported, some of which have been presented in reader-friendly tables and discussed. Full article

Background/Objectives: During and after electric–acoustic stimulation (EAS) surgery (as well as regular cochlear implant surgery), the oral and/or intravenous administration of steroids is recommended to prevent acute inflammatory reactions and subsequent fibrosis. However, the effect does not last long. Therefore, with the hope of providing a sustained effect, a new dexamethasone (DEX)-eluting electrode (FLEX28 DEX) has recently been developed. Methods: A case study was performed at Shinshu University in February 2024 in which a DEX-eluting electrode array was utilized for a patient presenting with high-frequency hearing loss with a defined etiology (hearing loss due to a mitochondrial m.1555A > G variant). Results: Residual hearing was well preserved after EAS surgery, and post-operative impedance field telemetry was maintained at a very low level in contrast with a historical/retrospective control group (FLEX28 electrodes without DEX); therefore, it is expected that post-operative fibrosis will be minimized. Further, it was shown that the DEX-eluting electrode can also be applied to EAS. Conclusions: The DEX-eluting electrode was useful in maintaining post-operative impedance at a very low level, indicating that post-operative fibrosis could be minimized even after EAS surgery. Full article

Carbon dioxide (CO₂) fracturing and acid treatment are currently considered promising approaches to overcome the challenge of excessively high initiation pressure during conventional hydraulic fracturing in tight sandstone gas reservoirs. However, the mechanisms of these methods weaken the reservoir rock’s mechanical properties, remain unclear. Using a machine learning approach, we elucidate the differences in initiation mechanisms between CO₂ fracturing and hydraulic fracturing under acid-treated conditions, thereby providing a mechanistic explanation for the lower initiation pressure observed in CO₂ fracturing compared to conventional hydraulic fracturing. The tensile fractures, shear fractures, and acid-modified fractures have been identified by a specially trained AI model, which achieved exceptional accuracy (95.4%). Acoustic emission source locations show that CO₂ fracturing mainly causes shear fracture along acid-weakened planes, which promotes the propagation of composite tensile-shear fractures in untreated reservoir areas. Due to the significantly lower diffusivity of water compared to CO₂, hydraulic fracturing predominantly induces non-acidic mixed-mode (tensile-shear) fractures. This fundamental difference in fracture patterns accounts for the higher initiation pressure observed in hydraulic fracturing compared to CO₂ fracturing. These findings offer crucial insights into pressurized fluid-driven fracturing mechanisms and propose an optimized technical pathway for enhancing hydrocarbon recovery in low-permeability sandstone formations. Full article

Slab–column structures are susceptible to sudden punching shear failure at connections due to the absence of traditional beam support, prompting the need for effective damage monitoring. This study employs an acoustic emission (AE) technique to investigate the failure process of reinforced concrete slab–column specimens, analyzing basic AE parameters (hits, amplitude, energy), improved b-value (Ib-value), and RA–AF correlation, while introducing a Gaussian Mixture Model (GMM) to establish a unified index integrating crack type identification and energy information. Experimental results show that AE parameters can effectively track different stages of crack development, with Ib-value reflecting the transition from micro-crack to macro-crack growth. The correlation between AE energy and structural strain energy enables quantitative damage assessment, while RA–AF analysis and GMM clustering reveal the shift from bending-dominated to shear-dominated failure modes. This study provides a comprehensive framework for real-time damage evaluation and failure mode prediction in slab–column structures, demonstrating that AE-based multi-parameter analysis and data-driven clustering methods can characterize damage evolution and improve the reliability of structural health monitoring. Full article

Multi-core fiber (MCF) has drawn increasing attention for its potential application in distributed acoustic sensing (DAS) due to the compact optical structure of integrating several fiber cores in the same cladding, which indicates an intrinsic space-division-multiplexed (SDM) capability in a single piece of fiber. In this paper, a dual-channel DAS integrated with MCF is presented, of which the equivalent self-noise characteristic is analyzed. The equivalent self-noise of the system can be effectively suppressed by signal superposition with the phase matching method. Considering that the noise correlation among the cores is not zero, the signal-to-noise (SNR) gain after signal superposition is less than the theoretical value. The dual-channel DAS system is set up by a piece of 2 km long seven-core MCF, in which the dual-sensing channels are constructed by a four-core series and three-core series, respectively. The total noise correlation coefficient of the seven cores is 11.28, while the equivalent self-noise of the system can be suppressed by 6.32 dB with signal superposition. An equivalent self-noise suppression method based on a linear delay phase matching scheme is proposed for noise decorrelation in the DAS MCF system. After noise decorrelation, the suppression of the equivalent self-noise of the system can reach the theoretical value of 8.45 dB with a time delay of 1 ms, indicating a noise correlation among the seven cores of almost zero. The feasibility of the equivalent self-noise suppression method for the DAS system is verified for both single-frequency and broadband signals, which is of great significance for the detection of weak vibration signals based on a DAS system. Full article

Block caving is a cost-effective mining method that enables the highly efficient mining of thick and large ore bodies. During ore extraction in block caving operations, the ore-drawing roadways require especially high safety standards. However, the complex in situ stress conditions and cyclic loading from caved ore significantly deteriorate the stability of the surrounding rock. This makes rock mass control particularly challenging, such that it is crucial to study an effective method for maintaining the long-term stability of the roadways. This research proposes a comprehensive approach combining laboratory rock mechanics testing, numerical simulation, and field engineering validation to design effective support strategies for disturbance-affected roadways. Laboratory tests provide accurate mechanical parameters for the rock mass, the numerical simulations allow for the comprehensive analysis of deformation–failure mechanisms under disturbance conditions, and field validation ensures the reliability and practical applicability of the proposed support method. This study focuses on a −285 m ore-drawing roadway in the western section of the Yanqianshan Iron Mine. The in situ stress distribution was characterized through rock mechanics testing and acoustic emission monitoring. The propagation mechanisms of ore-drawing disturbance waves within the rock mass were analyzed, and numerical simulations revealed the deformation patterns and failure modes under dynamic disturbance, upon which the support scheme was designed. The results demonstrate that the designed bolt–mesh–shotcrete support scheme can effectively control surrounding rock deformation within 5 mm and resists the deformation induced by cyclic disturbances. This study provides valuable technical support for stability management in block caving mines with similar conditions. Full article

Simulated room impulse responses (RIRs) are important tools for studying architectural acoustics. Many methods exist to generate RIRs, each with unique properties that need to be considered when choosing an RIR synthesis technique. Despite the variation in synthesis techniques, there is a dearth of comparisons between these techniques. To address this, a comprehensive comparison of four major categories of RIR synthesis techniques was conducted: wave-based methods (hybrid FEM and modal analysis), geometrical acoustics methods (the image source method and ray tracing), delay-network reverberators (SDNs), and statistical methods (Sabine-NED). To compare these techniques, RIRs were recorded in a simple shoebox-shaped racquetball court, and we compared the synthesized RIRs against these recordings. We conducted both objective analyses, such as energy decay curves, normalized echo density, and frequency-dependent decay times, and a perceptual assessment of synthesized RIRs, which consisted of a listening assessment with 29 participants that utilized a MUSHRA comparison methodology. Our results reveal distinct advantages and limitations across synthesis categories. For example, the Sabine-NED technique was indistinguishable from the recorded IR, but it does not scale well with increasing geometric complexity. These findings provide valuable insights for selecting appropriate synthesis techniques for applications in architectural acoustics, immersive audio rendering, and virtual reality environments. Full article

Synchronous motors are pivotal to modern industrial systems, particularly those aligned with Industry 4.0 initiatives, due to their high precision, reliability, and energy efficiency. This review systematically examines fault detection and diagnostic techniques for synchronous motors from 2021 to 2025, emphasizing recent methodological innovations. A PRISMA-guided literature survey combined with scientometric analysis via VOSviewer 1.6.20 highlights growing reliance on data-driven approaches, especially deep learning models such as CNNs, RNNs, and hybrid ensembles. Model-based and hybrid techniques are also explored for their interpretability and robustness. Cross-domain methods, including acoustic and flux-based diagnostics, offer non-invasive alternatives with promising diagnostic accuracy. Key challenges persist, including data imbalance, non-stationary operating conditions, and limited real-world generalization. Emerging trends in sensor fusion, digital twins, and explainable AI suggest a shift toward scalable, real-time fault monitoring. This review consolidates theoretical frameworks, comparative analyses, and application-oriented insights, ultimately contributing to the advancement of predictive maintenance and fault-tolerant control in synchronous motor systems. Full article

Urban unmanned aerial vehicle (UAV) surveillance faces significant obstacles due to visual obstructions, inadequate lighting, small target dimensions, and acoustic signal interference caused by environmental noise and multipath propagation. To address these issues, this study proposes a multimodal detection framework that integrates an efficient YOLOv11-based visual detection module—trained on a comprehensive dataset containing over 50,000 UAV images—with a Capon beamforming-based acoustic imaging system using a 144-element spiral-arm microphone array. Adaptive compensation strategies are implemented to improve the robustness of each sensing modality, while detections results are validated through intersection-over-union and angular deviation metrics. The angular validation is accomplished by mapping acoustic direction-of-arrival estimations onto the camera image plane using established calibration parameters. Experimental evaluation reveals that the fusion system achieves outstanding performance under optimal conditions, exceeding 99% accuracy. However, its principal advantage becomes evident in challenging environments where individual modalities exhibit considerable limitations. The fusion approach demonstrates substantial performance improvements across three critical scenarios. In low-light conditions, the fusion system achieves 78% accuracy, significantly outperforming vision-only methods which attain only 25% accuracy. Under occlusion scenarios, the fusion system maintains 99% accuracy while vision-only performance drops dramatically to 9.75%, though acoustic-only detection remains highly effective at 99%. In multi-target detection scenarios, the fusion system reaches 96.8% accuracy, bridging the performance gap between vision-only systems at 99% and acoustic-only systems at 54%, where acoustic intensity variations limit detection capability. These experimental findings validate the effectiveness of the complementary fusion strategy and establish the system’s practical value for urban airspace monitoring applications. Full article

Resonant acoustic technology utilizes low-frequency vertical harmonic vibrations to induce full-field mixing effects in processed materials, and it is regarded as a “disruptive technology in the field of energetic materials”. Although numerous scholars have investigated the mechanisms of resonant acoustic mixing, there remains a lack of parameter selection methods for improving product quality and production efficiency in engineering practice. To address this issue, this study employs phase-field modeling and fluid–structure coupling methods to numerically simulate the transport process of glycerol during resonant acoustic mixing. The research reveals the mass transfer mechanism within the flow field, establishes a liquid-phase distribution index for quantitatively characterizing mixing effectiveness, and clarifies the enhancement effect of fluid transport on solid particle mixing through particle tracking methods. Furthermore, parameter studies on vibration frequency and amplitude were conducted, yielding a critical curve for guiding parameter selection in engineering applications. The results demonstrate that Faraday instability first occurs at the fluid surface, generating Faraday waves that drive large-scale vortices for global mass transfer, followed by localized mixing through small-scale vortices. The transport process of glycerol during resonant acoustic mixing comprises three distinct stages: stable Faraday wave oscillation, rapid mass transfer during flow field destabilization, and localized mixing upon stabilization. Additionally, increasing either vibration frequency or amplitude effectively enhances both the rate and effectiveness of mass transfer. These findings offer theoretical guidance for optimizing process parameters in resonant acoustic mixing applications. Full article

Modern inspection mobile robots can carry multiple sensors that can provide opportunities to take advantage of the fusion of information obtained from different sensors. In real-world condition monitoring, harsh environmental conditions can significantly affect the sensor’s accuracy. To address this issue in this paper, we introduced a fusion approach around information gaps to handle the portion of false information that can be captured by the employed sensors. To test our idea, we looked at various types of data, such as sounds, color images, and infrared images taken by a mobile robot inspecting a mining site to check the condition of the belt conveyor idlers. The RGB images are used to classify the rotating idlers as stuck ones (late-stage faults); on the other hand, the acoustic signals are employed to identify early-stage faults. In this work, the cyclostationary analysis approach is employed to process the captured acoustic data to visualize the bearing fault signature in the form of Cyclic Spectral Coherence. Since convolutional neural networks (CNNs) and their transfer learning (TL) forms are popular approaches for performing classification tasks, a comparison study of eight CNN-TL models was conducted to find the best models to classify different fault signatures in captured RGB images and acquired Cyclic Spectral Coherence. Finally, to combine the collected information, we suggest a method called dynamic weighted majority voting, where each model’s importance is regularly adjusted for each sample based on the surface temperature of the idler taken from IR images. We demonstrate that our method of combining information from multiple classifiers can work better than using just one sensor for monitoring conditions in real-world situations. Full article

This research provides a new systematic solution to the essential issue of self-noise interference in underwater acoustic sensing signals induced by unmanned surface vehicles (USVs) operating at sea. The self-noise pertains to the near-field interference noise generated by the growing diversity and volume of acoustic equipment utilized by USVs. The generating mechanism of self-noise is clarified, and a self-noise propagation model is developed to examine its three-dimensional coupling properties within spatiotemporal fluctuation environments in the time-frequency-space domain. On this premise, the YOLOv11 object identification framework is innovatively applied to the delay-Doppler (DD) feature maps of self-noise, thereby overcoming the constraints of traditional time-frequency spectral approaches in recognizing noise with delay spread and overlapping characteristics. A comprehensive comparison with traditional models like YOLOv8 and SSD reveals that the suggested delay-Doppler YOLO (DD-YOLO) algorithm attains an average accuracy of 87.0% in noise source identification. An enhanced denoising method, termed optimized time-frequency regularized overlapping group shrinkage (OTFROGS), is introduced, using structural sparsity alongside non-convex regularization techniques. Comparative experiments with traditional denoising methods, such as the normalized least mean square (NLMS) algorithm, wavelet threshold denoising (WTD), and the original time-frequency regularized overlapping group shrinkage (TFROGS), reveal that OTFROGS outperforms them in mitigating USV self-noise. This study offers a dependable technological approach for optimizing the performance of USV acoustic systems and proposes a theoretical framework and methodology applicable to different underwater acoustic sensing contexts. Full article

This study investigates the electromagnetic and NVH characteristics of an outer-rotor surface-mounted permanent magnet synchronous generator (SPMSG) for wave energy applications, focusing on the effect of rotor eccentricity. To reflect potential fault due to manufacturing or assembly defects, a 0.5 mm rotor eccentricity was introduced in finite element method (FEM) simulations. The torque ripple waveform was analyzed using fast Fourier transform (FFT) to identify dominant harmonic components that generate unbalanced electromagnetic forces and induce structural vibration. These harmonic components were further examined under variable marine operating conditions to evaluate their impact on acoustic radiation and vibration responses. Based on the simulation and analysis results, a design-stage methodology is proposed for predicting vibration and noise by targeting critical harmonic excitations, providing practical insights for marine generator design and improving long-term operational reliability in wave energy systems. Full article

The traditional research methods of the notch frequency phenomenon are mainly discussed by experimental observation or the semi-analytical finite element method. In this paper, the notch frequency characteristics of ultrasonic guided waves are simulated by the general finite element method. Firstly, the theoretical dispersion curve of the longitudinal mode in the axially loaded rod is derived by the acoustic elasticity theory, and the finite element simulation is carried out by ABAQUS/Explicit 6.14 to simulate the wave propagation in the seven-wire steel strand. In order to verify the model, laboratory experiments are carried out on three types of prestressed steel strands with diameters of 12.7 mm, 15.2 mm, and 17.8 mm, respectively. Each specimen is gradually loaded from 50 kN to 110 kN in increments of 30 kN. At each loading level, the ultrasonic signal is obtained, and the corresponding notch frequency is extracted from the spectrum. The experimental results confirm the accuracy of the model, and the maximum deviation between the predicted notch frequency and the measured value is 3%. The results show that the proposed method provides a robust and non-destructive means for structural health monitoring in civil engineering applications, and has the potential to be more widely used in complex waveguide structures. Full article

This study presents the complete analysis of a (2 + 1)-dimensional nonlinear wave-type partial differential equation with anisotropic power-law nonlinearities and a general power-law source term, which arises in physical domains such as fluid dynamics, nonlinear acoustics, and wave propagation in elastic media, yet their symmetry properties and exact solution structures remain largely unexplored for arbitrary nonlinearity exponents. To fill this gap, a complete Lie symmetry classification of the equation is performed for arbitrary values of m and n, providing all admissible symmetry generators. These generators are then employed to systematically reduce the PDE to ordinary differential equations, enabling the construction of exact analytical solutions. Traveling wave and soliton solutions are derived using Jacobi elliptic function and sine-cosine methods, revealing rich nonlinear dynamics and wave patterns under anisotropic conditions. Additionally, conservation laws associated with variational symmetries are obtained via Noether’s theorem, yielding invariant physical quantities such as energy-like integrals. The results extend the existing literature by providing, for the first time, a full symmetry classification for arbitrary m and n, new families of soliton and traveling wave solutions in multidimensional settings, and associated conserved quantities. The findings contribute both computationally and theoretically to the study of nonlinear wave phenomena in multidimensional cases, extending the catalog of exact solutions and conserved dynamics of a broad class of nonlinear partial differential equations. Full article