Pharyngeal health plays a vital role in essential human functions such as breathing, swallowing, and vocalization. Early detection of swallowing abnormalities, also known as dysphagia, is crucial for timely intervention. However, current diagnostic methods often rely on radiographic imaging or invasive procedures. In this study, we propose an automated framework for detecting dysphagia using portable and noninvasive acoustic sensing coupled with applied machine learning. By capturing subtle acoustic signals from the neck during swallowing tasks, we aim to identify patterns associated with abnormal physiological conditions. Our approach achieves promising test-time abnormality detection performance, with an AUC-ROC of 0.904 under 5 independent train-test splits. This work demonstrates the feasibility of using noninvasive acoustic sensing as a practical and scalable tool for pharyngeal health monitoring.
The control and manipulation of waves in optical and/or acoustic science and engineering are widespread. Recently, despite reported studies on high frequency acoustic waves via time modulated media, none of these approaches has demonstrated success with broadband frequency exceeding that of the mechanical system, and all of them were not able to emit ultralow frequency waves. There are both fundamental and practical issues. Achieving ultra-low frequency underwater acoustic wave control via time interfaces remains a major challenge for integrated underwater acoustic devices. Here, we explore the design of a marine vibrator source system based on hydraulic-acoustic energy conversion. The system consists of a hydraulic servo system kept aboard a boat on one side while the other side comprises an underwater vibrator transducer. The transfer of wave energy is a fundamental mechanism for emitting acoustic waves, yet the rules of conventional reaction-mass force intrinsically limit the vibrator force based on the displacement and the acceleration of the reaction mass. We show that this intrinsic limit can be broken for acoustic waves, where the acoustics become controllable by the arrangement of the vibrator system. The marine vibrator acoustic waves open new frontiers in acoustic control and enables diverse focusing and imaging. Time interfaces have shown potential in wave control, but applying this ultra-low frequency underwater acoustics remains a challenge. Xingguo Huang and colleagues explore the design of a marine vibrator system using hydraulic acoustic energy conversion, achieving ultra-low frequency wave control
The ability to manipulate acoustic fields in a real-time and high-resolution manner can open up many opportunities for engineering and medical applications. Realising this would demand an acoustic metamaterial that can modulate acoustic waves in a programmable manner. We achieve this goal using a crosslinked semi-crystalline polymer for which any arbitrary modulus pattern can be repeatedly encoded/erased in roughly 13 minutes. Critically and surprisingly, the material allows acoustic wave transmission with low attenuation, despite its multiphase nature. With the modulus pattern and acoustic transparency, reconfigurable phase holograms can be created. Combined with an electrically switchable and compact partitioned piezo-electric transducer, the device allows generating acoustic fields with a high modulation resolution of 10000 pixels/cm2 at an ultra-fast switching rate of 50000 fps for specified dynamic holography, far exceeding existing approaches. By programming the semi-crystalline polymer with different phase holograms, together with the selective excitation of partitioned piezo-electric transducer for incident wavefront modulation, it allows an unprecedented opportunity to create acoustic movies and remote thermal writing, with strong implications for many other future technological possibilities. Ultra-fast dynamic acoustic holography that can be reconfigured is realised using partitioned transducer and programmable phase hologram. These attributes allow creating acoustic movies and dynamic ultrasound stimulus, with implications for future possibilities in broad acoustics, such as sonodynamic therapy and neuromodulation.
The significance of the liftgate's role in vehicle low-frequency boom noise is highlighted by its modal coupling with the vehicle's acoustic cavity modes. The liftgate's acoustic sensitivity and susceptibility to vehicle vibration excitation are major contributors to this phenomenon. This paper presents a CAE (Computer-Aided Engineering) methodology for designing vehicle liftgates to reduce boom risk. Empirical test data commonly show a correlation between high levels of liftgate vibration response to vehicle excitations and elevated boom risk in the vehicle cabin. However, exceptions to this trend exist; some vehicles exhibit low boom risk despite high vibration responses, while others show high boom risk despite low vibration responses. These discrepancies indicate that liftgate vibratory response alone is not a definitive measure of boom risk. Nonetheless, evidence shows that establishing a vibration level control guideline during the design stage results in lower boom risk. The analysis of numerous vehicles confirms that most vehicles achieve a low forced vibration response, leading to the proposal of a vibratory response target. Furthermore, the relationship between liftgate modal coupling and the vehicle's acoustic cavity modes reveals that liftgate boom risk is a product of the liftgate's Frequency Response Function (FRF) and its acoustic sensitivity. Based on this relationship, we propose an objective target setting to minimize liftgate boom risk. Additionally, the linearity of the liftgate's response to forced vibration excitation was experimentally examined. The CAE model used in this analysis was confirmed to be accurate. The study also examines the effectiveness of the liftgate damper in reducing boom risk. This comprehensive study underscores the critical role of the liftgate in vehicle acoustics and the necessity for precise modeling to effectively mitigate low-frequency boom noise.
Seyed Hamidreza Afzalimir, A. Dana, I. Dabo
et al.
Hydrogen embrittlement, a critical concern for the mechanical response of engineering materials, can arise due to an influx of hydrogen atoms at interstitial sites and at grain boundaries. The acoustic nonlinearity parameter (ANP) is used in nondestructive evaluation as a sensitive parameter for the early detection of material degradation. From a measurement perspective, the ANP can be determined from the distortion of elastic waves. From a modeling perspective, the ANP is computed from second and third-order elastic constants. This study investigates the influence of solute hydrogen atoms on the ANP in aluminum using results of density functional theory calculations as input to continuum-scale computations of elastic constants. Based on the sensitivity of the ANP to hydrogen solute atoms, the findings suggest that an additive decomposition of the ANP is not applicable. Additionally, approaches based upon the stress or strain caused by local heterogeneity (such as solute atoms), without including the heterogeneity itself may be misleading with regard to the ANP. Moreover, the general expectation that atomistic and microscale defects increase ANP may not be universally valid because we observed a decrease in ANP due to interstitial hydrogen atoms and grain boundaries. This work provides novel insights into the application of nonlinear acoustics for detecting atomistic-scale defects and lays the groundwork for a more accurate connection between acoustic measurements and hydrogen-related degradation in materials.
Spin–orbit couplings (SOCs) underlie several key concepts of topological matter. However, acoustic waves lack intrinsic spin and SOCs, which makes some topological phases impossible. We develop in the present work a realistic scheme to synthesize simultaneously the intrinsic and Rashba–Dresselhaus SOCs in acoustic systems and explore the symmetry-protected topological phase induced by the SOCs. To be precise, we construct a two-leg ladder composed of acoustic resonators and linking tubes. Utilizing the concept of pseudospin, the spin-1/2 is encoded by the leg degree of freedom of the ladder, and meanwhile, the SOCs are achieved by engineering the couplings between resonators. We further highlight the emergence of the symmetry-protected topological phase respecting the chiral unitary (AIII) symmetry in such acoustic SOC lattices. This scheme is confirmed by the full-wave simulations. Our acoustic structure is within immediate experimental reach and enables the direct visualization of symmetry-protected topological boundary states, not yet been observed experimentally. Our results represent a route to synthesize the SOCs and will benefit an in-depth study of the spin–orbit physics in acoustics.
Muhammad Tayyab Khan, Zane Yong, Lequn Chen
et al.
Engineering drawings are fundamental to manufacturing communication, serving as the primary medium for conveying design intent, tolerances, and production details. However, interpreting complex multi-view drawings with dense annotations remains challenging using manual methods, generic optical character recognition (OCR) systems, or traditional deep learning approaches, due to varied layouts, orientations, and mixed symbolic-textual content. To address these challenges, this paper proposes a three-stage hybrid framework for the automated interpretation of 2D multi-view engineering drawings using modern detection and vision language models (VLMs). In the first stage, YOLOv11-det performs layout segmentation to localize key regions such as views, title blocks, and notes. The second stage uses YOLOv11-obb for orientation-aware, fine-grained detection of annotations, including measures, GD&T symbols, and surface roughness indicators. The third stage employs two Donut-based, OCR-free VLMs for semantic content parsing: the Alphabetical VLM extracts textual and categorical information from title blocks and notes, while the Numerical VLM interprets quantitative data such as measures, GD&T frames, and surface roughness. Two specialized datasets were developed to ensure robustness and generalization: 1,000 drawings for layout detection and 1,406 for annotation-level training. The Alphabetical VLM achieved an overall F1 score of 0.672, while the Numerical VLM reached 0.963, demonstrating strong performance in textual and quantitative interpretation, respectively. The unified JSON output enables seamless integration with CAD and manufacturing databases, providing a scalable solution for intelligent engineering drawing analysis.
Johan Cederbladh, Loek Cleophas, Eduard Kamburjan
et al.
With the current trend in Model-Based Systems Engineering towards Digital Engineering and early Validation & Verification, experiments are increasingly used to estimate system parameters and explore design decisions. Managing such experimental configuration metadata and results is of utmost importance in accelerating overall design effort. In particular, we observe it is important to 'intelligent-ly' reuse experiment-related data to save time and effort by not performing potentially superfluous, time-consuming, and resource-intensive experiments. In this work, we present a framework for managing experiments on digital and/or physical assets with a focus on case-based reasoning with domain knowledge to reuse experimental data efficiently by deciding whether an already-performed experiment (or associated answer) can be reused to answer a new (potentially different) question from the engineer/user without having to set up and perform a new experiment. We provide the general architecture for such an experiment manager and validate our approach using an industrial vehicular energy system-design case study.
Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar
et al.
This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.
M. P. Peiró-Torres, M. J. Parrilla Navarro, M. Ferri
et al.
This article presents the use of advanced tools applied to the design of devices that can solve specific acoustic problems, improving the already existing devices based on classic technologies. Specifically, we have used two different configurations of a material called Sonic Crystals, which is formed by arrays of acoustic scatterers, to obtain acoustic screens with high diffusion properties by means of an optimization process. This design procedure has been carried out using a multiobjective evolutionary algorithm along to an acoustic simulation model developed with the numerical method called Finite Difference Time Domain (FDTD). The results obtained are discussed in terms of both the acoustic performance and the robustness of the devices achieved.
Modern systems are increasingly connected and more integrated with other existing systems, giving rise to \textit{systems-of-systems} (SoS). An SoS consists of a set of independent, heterogeneous systems that interact to provide new functionalities and accomplish global missions through emergent behavior manifested at runtime. The distinctive characteristics of SoS, when contrasted to traditional systems, pose significant research challenges within Software Engineering. These challenges motivate the need for a paradigm shift and the exploration of novel approaches for designing, developing, deploying, and evolving these systems. The \textit{International Workshop on Software Engineering for Systems-of-Systems} (SESoS) series started in 2013 to fill a gap in scientific forums addressing SoS from the Software Engineering perspective, becoming the first venue for this purpose. This article presents a study aimed at outlining the evolution and future trajectory of Software Engineering for SoS based on the examination of 57 papers spanning the 11 editions of the SESoS workshop (2013-2023). The study combined scoping review and scientometric analysis methods to categorize and analyze the research contributions concerning temporal and geographic distribution, topics of interest, research methodologies employed, application domains, and research impact. Based on such a comprehensive overview, this article discusses current and future directions in Software Engineering for SoS.
Siddhant Dutta, Iago Leal de Freitas, Pedro Maciel Xavier
et al.
Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community with an accessible introduction to the discipline. Supported by a hands-on tutorial and a comprehensive collection of examples, it explores the application of FL in tasks such as manufacturing optimization, multimodal data integration, and drug discovery while addressing the unique challenges of protecting proprietary information and managing distributed datasets. The tutorial was built using key frameworks such as $\texttt{Flower}$ and $\texttt{TensorFlow Federated}$ and was designed to provide chemical engineers with the right tools to adopt FL in their specific needs. We compare the performance of FL against centralized learning across three different datasets relevant to chemical engineering applications, demonstrating that FL will often maintain or improve classification performance, particularly for complex and heterogeneous data. We conclude with an outlook on the open challenges in federated learning to be tackled and current approaches designed to remediate and improve this framework.
It is challenging to predict the patterns of radiated acoustic field from a complex moving source while this topic is important in the field of ocean acoustics. A possible method is to decompose the complex field into the addition of fundamental sources such as monopolar and dipolar ones. In this study, we derive the analytical solution of radiated acoustic fields generated by a moving monopolar or dipolar source, and the combination of them based on the Lorentz transformation. The analytical expressions of the radiated sound pressure by a moving monopolar or dipolar source are given with no limit on the moving direction and excitation frequency. Numerical simulations are conducted to reveal the Doppler effect of the moving sources and the potential application of the combination of monopolar and dipolar sources to simulate complex fields. This work may help design acoustic models to predict the radiated sound field by complex sources in the field of ocean engineering.
Prof. Zhi Zong, Prof. Minqing Wang, Prof. Zhuojia Fu
et al.
The purpose of the three-day 2023 2nd International Conference on Acoustics, Fluid Mechanics and Engineering (AFME 2023), held during November 17th to 19th, 2023 in Nanjing, China, was to create a timely forum for multi-disciplinary discussions related to the recent developments on acoustics, fluid mechanics and engineering. Highly eventful program of the Conference, the list of well-established organizations-participants and devoted engagement of many colleagues throughout all the stages of the Conference preparation instill confidence in practical importance of mutual initiative. As usual, the program of the meeting mainly consisted of plenary speeches, invited speeches, oral presentations and poster presentations, discussing significant problems facing technologies related to areas of acoustics, fluid mechanics and engineering, proposing innovative ideas and approaches to solution of the problems, and considering new possibilities of application and development of cutting-edge technology. In the framework of experimental and theoretical approaches, the Conference gathered 150 delegates from all over the world and addressed a number of highly relevant aspects of acoustics, fluid mechanics and engineering. Covering topics on Acoustic Materials, Hydrodynamic Acoustics, Ultrasonics, Physical Chemical Hydrodynamics, Hydromechanical Hydrodynamics, Engineering Fluid Mechanics, etc., the selected contributions of this special issue provide guidance for future interdisciplinary developments. In this way, the multi-scale aspects of acoustics, fluid mechanics and engineering should be considered. We received various manuscripts of invited papers and other contributions presented at the Conference. All of these papers have gone through a rigorous peer review process and are collected in this volume. We would like to thank all the anonymous colleagues who have acted as referees to assess the suitability of the various articles for publication in Journal of Physics: Conference Series. We are confident that the high quality of both invited and contributed papers contained in this Proceedings will be appreciated by relevant communities. We would like to express our thanks to all the authors for their time and genuine contributions, and to the reviewers for their fruitful comments during the preparation of this volume. We also acknowledge the support provided in various ways by Guangdong University of Technology, Dalian Maritime University, School of Marine Science and Technology, Northwestern Polytechnical University, and University of Toronto. List of Committee Member is available in this pdf.
The boundary element method is widely used in practical engineering problems, especially in the field of acoustics. For flow-induced noise, the main target of acoustic calculations is to solve the wave equation with the flow field information. However, the sound field distribution of noncompact structures in half-space is especially complex because of the strong scattering effect, while the object surface boundary integration often brings a large workload and generates numerical singularities. In this paper, an improved boundary element method for predicting the aeroacoustic noise of noncompact structures is proposed, which can consider the characteristic distribution of sound field induced by complex structures in half-space. The smooth permeable boundary surrounding the object is used as the integration boundary, while the scattering effect of the ground boundary is investigated by combining the mirror Green’s function method, and the numerical prediction of aeroacoustic noise is carried out for the dipole source and NACA0012 airfoil in half-space. Numerical results show that the far-field noise obtained by using the permeable surface is consistent with that obtained by integrating the direct object boundary under the influence of ground boundary scattering. The mirror image Green’s function method is able to finely capture the ground scattering effect, which has a significant effect on the sound field as the frequency increases.
Dissipation in mechanics, optics, acoustics, and electronic circuits is nowadays recognized to be not always detrimental but can be exploited to achieve non-Hermitian topological phases or properties with functionalities for potential device applications. As elementary excitations of ordered magnetic moments that exist in various magnetic materials, magnons are the information carriers in magnonic devices with low-energy consumption for reprogrammable logic, non-reciprocal communication, and non-volatile memory functionalities. Non-Hermitian topological magnonics deals with the engineering of dissipation and/or gain for non-Hermitian topological phases or properties in magnets that are not achievable in the conventional Hermitian scenario, with associated functionalities cross-fertilized with their electronic, acoustic, optic, and mechanic counterparts, such as giant enhancement of magnonic frequency combs, magnon amplification, (quantum) sensing of the magnetic field with unprecedented sensitivity, magnon accumulation, and perfect absorption of microwaves. In this review article, we address the unified approach in constructing magnonic non-Hermitian Hamiltonian, introduce the basic non-Hermitian topological physics, and provide a comprehensive overview of the recent theoretical and experimental progress towards achieving distinct non-Hermitian topological phases or properties in magnonic devices, including exceptional points, exceptional nodal phases, non-Hermitian magnonic SSH model, and non-Hermitian skin effect. We emphasize the non-Hermitian Hamiltonian approach based on the Lindbladian or self-energy of the magnonic subsystem but address the physics beyond it as well, such as the crucial quantum jump effect in the quantum regime and non-Markovian dynamics. We provide a perspective for future opportunities and challenges before concluding this article.
Physics-Informed machine learning models have recently emerged with some interesting and unique features that can be applied to reservoir engineering. In particular, physics-informed neural networks (PINN) leverage the fact that neural networks are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations. The transient diffusivity equation is a fundamental equation in reservoir engineering and the general solution to this equation forms the basis for Pressure Transient Analysis (PTA). The diffusivity equation is derived by combining three physical principles, the continuity equation, Darcy's equation, and the equation of state for a slightly compressible liquid. Obtaining general solutions to this equation is imperative to understand flow regimes in porous media. Analytical solutions of the transient diffusivity equation are usually hard to obtain due to the stiff nature of the equation caused by the steep gradients of the pressure near the well. In this work we apply physics-informed neural networks to the one and two dimensional diffusivity equation and demonstrate that decomposing the space domain into very few subdomains can overcome the stiffness problem of the equation. Additionally, we demonstrate that the inverse capabilities of PINNs can estimate missing physics such as permeability and distance from sealing boundary similar to buildup tests without shutting in the well.