Sound & Vibration Joins Membership of the Acoustical Society of China

2026-01-21

At the beginning of 2026, Sound & Vibration, an academic journal published by Singapore Academic Publishing, officially joined The Acoustical Society of China (ASC) as a corporate member. This significant collaboration marks a new chapter for the journal, fostering enhanced international academic exchange and deeper integration within China's acoustics community.

Sound & Vibration is an international academic journal specializing in acoustics, vibration, and noise control. Its scope encompasses a wide range of research areas, including physical acoustics, engineering acoustics, structural vibration, acoustic materials, noise and vibration control technologies, and their interdisciplinary applications. The journal remains dedicated to providing a high-caliber academic exchange platform for global researchers and engineering professionals, actively driving innovation in both theoretical and applied engineering developments within the field.

The Acoustical Society of China is recognized as one of the most authoritative and influential academic organizations in China’s acoustics sector. It plays a pivotal role in advancing fundamental research, technological progress, academic exchange, and talent cultivation. The journal’s affiliation with the ASC will further strengthen its ties with the Chinese acoustics community, facilitate the sharing of international academic resources, and bolster its scholarly influence across China and the Asia-Pacific region.

Academic Publishing stated that Sound & Vibration will leverage the ASC’s academic platform to participate actively in conferences, thematic seminars, and public outreach events. The journal encourages Chinese scholars to publish their high-quality research findings within its pages. Concurrently, the journal will continue to optimize its peer-review processes and enhance publishing services to promote international collaboration and the coordinated development of the acoustics and vibration fields.

This collaboration not only provides fresh momentum for the long-term growth of Sound & Vibration but also offers members of The Acoustical Society of China broader channels for international academic exchange. Both parties will work in synergy to advance the progress of global acoustics and vibration science and technology.

This paper introduces a universal framework for understanding the vibration responses of systems subjected to harmonic excitation. By examining a simplified cylinder-spring-damper model, the study refurbishes traditional scaling methods for the excitation frequency ratio and critical damping ratio. The findings indicate that in damped systems, the maximum amplitude of vibration does not align with the natural frequency. This observation leads to the introduction of a new scaling method for reduced frequency. This new approach aligns resonance peaks at the new reduced velocity of 1.0 across different damping ratios, providing a consistent characterization of vibration behavior. A new critical damping ratio of 0.707 is identified for an excited system as opposed to the traditional damping ratio of 1.0 for an unexcited system. Key properties such as maximum amplitude, phase lag, bandwidth, and quality factor are analyzed, demonstrating that the proposed reduced frequency and critical damping ratio effectively capture the dynamics of both damped and undamped excited systems. The findings offer significant insights for practical applications in engineering and various scientific fields.

This study makes significant contributions to the field of ultrasonic testing (UT) by offering a novel approach to the identification of artificially introduced defects within Japanese cedar wood (Cryptomeria japonica). The findings are of particular relevance for the heritage conservation and construction sectors, where non-invasive defect detection is paramount. The study establishes a robust framework for assessing the structural integrity of timber by correlating ultrasonic wave velocity reductions with defect size and distribution. Big-sized defects led to more substantial decreases in wave velocity. The study establishes a robust framework for assessing the structural integrity of historical timber by correlating ultrasonic wave velocity reductions with defect size and distribution. This framework has the potential to be applicable to diverse wood species and defect types.

The control of vehicle interior noise has become a critical metric for assessing noise, vibration, and harshness (NVH) in vehicles. During the initial phases of vehicle development, accurately predicting the impact of road noise on interior noise is essential for reducing noise levels and expediting the product development cycle. In recent years, data-driven methods based on machine learning have gained significant attention due to their robust capability in navigating complex data mapping relationships. Notably, surrogate models have demonstrated exceptional performance in this domain. Numerous researchers have integrated diverse intelligent algorithms into the study of vehicle noise, leveraging advantages such as the elimination of precise modeling requirements, extensive solution space exploration, continuous learning from data, and robust algorithmic versatility. However, in NVH engineering applications, data-driven models face inherent limitations, particularly in interpretability and stability. To address these issues, this paper introduces an improved Long Short-Term Memory (LSTM) network that combines knowledge and data. Inspired by the physical information neural network concept, this approach incorporates values calculated through empirical formulas into the neural network as constraints. Comparative assessments with traditional LSTM networks highlight the advantages of this deep learning model. By integrating empirical formulas constraints, the model not only enhances interpretability but also achieves robust generalization with fewer data samples. The proposed method is validated on a specific vehicle model, showing significant improvements in prediction accuracy and efficiency.