Sound & Vibration

Acknowledgment to the Reviewers of Sound & Vibration in 2025

2026-02-06

The Editors and Publisher of Sound & Vibration extend their sincere appreciation to all reviewers who contributed their time, expertise, and scholarly judgment to the peer-review process in 2025.

Peer review is a cornerstone of high-quality academic publishing. The careful, fair, and constructive evaluations provided by our reviewers play a critical role in maintaining the scientific rigor, integrity, and credibility of the journal. Their contributions not only support editorial decision-making but also assist authors in improving the clarity, validity, and impact of their research.

We deeply appreciate the commitment demonstrated by reviewers, whose voluntary service represents an essential contribution to the global academic community. The journal remains firmly committed to recognizing the value of peer review and to continuously enhancing the transparency, efficiency, and quality of its editorial and review processes.

The following individuals served as reviewers for the journal during 2025.

Names are listed alphabetically.

Please refer to the attachment in the announcement.

[SV] Acknowledgment to the Reviewers in 2025.pdf

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.

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.

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Acknowledgment to the Reviewers of Sound & Vibration in 2025

Sound & Vibration Joins Membership of the Acoustical Society of China