Search Results (286)

Due to the effect of room acoustics on musical interpretation, a musician’s rehearsal may be greatly enhanced by leveraging virtual and augmented reality technology. This paper presents a preliminary study on a rehearsal tool designed for musicians, enabling practice in a virtual acoustic environment with audience-positioned playback. Fourteen participants, both professional and non-professional musicians, were recruited to practice with the rehearsal tool prior to performing in an unfamiliar venue. Throughout the rehearsal, the subjects either played in a virtual environment that matched the acoustics of the performance venue or one that was acoustically different. A control group rehearsed in an acoustically dry room with no virtual acoustic environment. The tool’s effectiveness was evaluated with two 16-item questionnaires that assessed quality, usefulness, satisfaction with the rehearsal, and aspects of the performance. Findings indicate that rehearsing in a virtual acoustic environment that matches the performance venue improves acoustic awareness during the performance and enhances ease and comfort on stage compared to practising in a different environment. These results support the integration of virtual acoustics in rehearsal tools to help musicians better adapt their performance to concert settings. Full article

Nonlinear narrowband low-frequency noise generated during tractors’ operation significantly affects operators’ comfort and working efficiency. Traditional linear active noise control algorithms often struggle to effectively suppress such complex acoustic disturbances. To address this challenge, this paper proposes a momentum-enhanced Volterra filter-based active noise control (ANC) algorithm that improves both the modeling capability of nonlinear acoustic paths and the convergence performance of the system. The proposed approach integrates the nonlinear representation power of the Volterra filter with a momentum optimization mechanism to enhance convergence speed while maintaining robust steady-state accuracy. Simulations are conducted under second- and third-order nonlinear primary paths, followed by performance validation using multi-tone signals and real in-cabin tractor noise recordings. The results demonstrate that the proposed algorithm achieves superior performance, reducing the NMSE to approximately −35 dB and attenuating residual noise energy by 3–5 dB in the 200–800 Hz range, compared to FXLMS and VFXLMS algorithms. The findings highlight the algorithm’s potential for practical implementation in nonlinear and narrowband active noise control scenarios within complex mechanical environments. Full article

The COVID-19 pandemic’s rapid shift to working from home has fundamentally challenged residential acoustic design, which traditionally prioritises rest and relaxation rather than sustained concentration. However, a clear gap exists in understanding how acoustic needs and the subjective evaluation of soundscape appropriateness (SA) differ between these conflicting activities within the same domestic space. Addressing this gap, this study reveals critical differences in how people experience and evaluate home soundscapes during work versus relaxation activities in the same residential spaces. Through an online survey of 247 Chinese participants during lockdown, we assessed soundscape perception attributes, the perceived saliencies of various sound types, and soundscape appropriateness (SA) ratings while working and relaxing at home. Our findings demonstrate that working at home creates a more demanding acoustic context: participants perceived indoor soundscapes as significantly less comfortable and less full of content when working compared to relaxing (p < 0.001), with natural sounds becoming less noticeable (−13.3%) and distracting household sounds more prominent (+7.5%). Structural equation modelling revealed distinct influence mechanisms: while comfort significantly mediates SA enhancement in both activities, the effect is stronger during relaxation (R² = 0.18). Critically, outdoor man-made noise, building-service noise, and neighbour sounds all negatively impact SA during work, with neighbour sounds showing the largest detrimental effect (total effect size = −0.17), whereas only neighbour sounds and outdoor man-made noise significantly disrupt relaxation activities. Additionally, natural sounds act as a positive factor during relaxation. These results expose a fundamental mismatch: existing residential acoustic environments, designed primarily for rest, fail to support the cognitive demands of work activities. This study provides evidence-based insights for acoustic design interventions, emphasising the need for activity-specific soundscape considerations in residential spaces. As hybrid work arrangements become the norm post-pandemic, our findings highlight the urgency of reimagining residential acoustic design to accommodate both focused work and restorative relaxation within the same home. Full article

This current review examines modeling approaches for renovating reinforced concrete (RC) buildings for vertical forest (VF) application, taking into account structural retrofitting, energy systems, forestry integration, and occupant comfort. The study assesses research conducted with an advanced 3D finite element analysis and the use of retrofitting modeling techniques, including textile-reinforced mortar (TRM), fiber-reinforced polymer (FRP), seismic joints, and green concrete applications. The energy system modeling methods are reviewed, taking into account the complexity of incorporating vegetation and seasonal variations. During forestry integration, three main design parameters are identified, namely, root systems, trunks, and crowns, for their critical role in the structural stability and optimal environmental performance. The comfort models are identified evolving from static to adaptive models incorporating thermal, acoustic, visual and air quality parameters. The current review consists of more than one hundred studies indicating that the integration of natural systems to buildings requires a multidimensional and multidisciplinary approach with sophisticated systems. The findings of this review provide the basis for implementing VF models to RC buildings, while highlighting areas requiring further research and validation. Full article

Automotive NVH (Noise, Vibration, and Harshness) performance significantly impacts driving comfort and traffic safety. Vehicles exhibiting superior NVH characteristics are more likely to achieve consumer acceptance and enhance their competitiveness in the marketplace. In the development of automotive NVH performance, traditional vibration reduction methods have proven to be mature and widely implemented. However, due to constraints related to size and weight, these methods typically address only high-frequency vibration control. Consequently, they struggle to effectively mitigate vehicle body and component vibration noise at frequencies below 200 Hz. In recent years, acoustic metamaterials (AMMs) have emerged as a promising solution for suppressing low-frequency vibrations. This development offers a novel approach for low-frequency vibration control. Nevertheless, conventional design methodologies for AMMs predominantly rely on empirical knowledge and necessitate continuous parameter adjustments to achieve desired bandgap characteristics—an endeavor that entails extensive calculations and considerable time investment. With advancements in machine learning technology, more efficient design strategies have become feasible. This paper presents a tandem neural network (TNN) specifically developed for the design of AMMs. The trained neural network is capable of deriving both the bandgap characteristics from the design parameters of AMMs as well as deducing requisite design parameters based on specified bandgap targets. Focusing on addressing low-frequency vibrations in the back frame of automobile seats, this method facilitates the determination of necessary AMMs design parameters. Experimental results demonstrate that this approach can effectively guide AMMs designs with both speed and accuracy, and the designed AMMs achieved an impressive vibration attenuation rate of 63.6%. Full article

This research introduces an innovative passive vibration control methodology employing acoustic black hole (ABH) structures to mitigate vibration transmission in electric automotive steering machines—a prevalent issue adversely affecting driving comfort and vehicle safety. Leveraging the inherent bending wave manipulation properties of ABH configurations, we conceive an integrated vibration suppression framework synergizing advanced computational modeling with intelligent optimization algorithms. A high-fidelity finite element (FEM) model integrating ABH-attached steering machine system was developed and subjected to experimental validation via rigorous modal testing. To address computational challenges in design optimization, a hybrid modeling strategy integrating parametric design (using Latin Hypercube Sampling, LHS) with Kriging surrogate modeling is proposed. Systematic parameterization of ABH geometry and damping layer dimensions generated 40 training datasets and 12 validation datasets. Surrogate model verification confirms the model’s precise mapping of vibration characteristics across the design space. Subsequent multi-objective genetic algorithm optimization targeting RMS velocity suppression achieved substantial vibration attenuation (29.2%) compared to baseline parameters. The developed methodology provides automotive researchers and engineers with an efficient suitable design tool for vibration-sensitive automotive component design. Full article

Eczema, or atopic dermatitis (AD), is a chronic inflammatory skin condition characterized by persistent itching and scratching, significantly impacting patients’ quality of life. Effective monitoring of scratching behaviour is crucial for assessing disease severity, treatment efficacy, and understanding the relationship between itch and sleep disturbances. This review explores current technological approaches for detecting and monitoring scratching and itching in AD patients, categorising them into contact-based and non-contact-based methods. Contact-based methods primarily involve wearable sensors, such as accelerometers, electromyography (EMG), and piezoelectric sensors, which track limb movements and muscle activity associated with scratching. Non-contact methods include video-based motion tracking, thermal imaging, and acoustic analysis, commonly employed in sleep clinics and controlled environments to assess nocturnal scratching. Furthermore, emerging artificial intelligence (AI)-driven approaches leveraging machine learning for automated scratch detection are discussed. The advantages, limitations, and validation challenges of these technologies, including accuracy, user comfort, data privacy, and real-world applicability, are critically analysed. Finally, we outline future research directions, emphasizing the integration of multimodal monitoring, real-time data analysis, and patient-centric wearable solutions to improve disease management. This review serves as a comprehensive resource for clinicians, researchers, and technology developers seeking to advance objective itch and scratch monitoring in AD patients. Full article

Sustainable informal learning environments are increasingly recognized as critical components of educational architecture, yet their environmental and behavioral dynamics remain underexplored. Informal learning spaces (ILS) support flexible, student-driven learning beyond formal classrooms. While prior research often isolates individual environmental factors, integrated multi-domain interactions and reciprocal occupant–space dynamics receive less attention. This study adopts a dual-perspective analytical framework, combining spatial analysis and student surveys (n = 1048) across 130 ILS in five academic buildings in China. The findings highlight several environmental dimensions influencing student experience. One extracted factor combines acoustic and thermal comfort with learning atmosphere—domains seldom grouped together—indicating their collective relevance to student experience. Additionally, spatial openness and natural connectivity further enhance student experience. Importantly, the results show that frequently used spaces receive lower physical quality ratings, group collaboration areas outperform individual study zones, and spontaneously formed spaces—informally appropriated, unplanned areas such as corridors or leftover corners—score lowest. These patterns may reflect mismatches between spatial supply and use intensity, institutional investment priorities, and differing levels of student autonomy and environmental control. This research extends conventional post-occupancy evaluations by introducing a comprehensive dual-perspective framework that links spatial characteristics with user-driven dynamics, and by identifying the combined effects of multi-domain physical environmental and supportive elements on student experience. The insights offer empirical grounding and actionable strategies for campus planners and architects, including prioritizing sensory comfort, enhancing spatial diversity, and supporting student-led adaptations to promote sustainable learning environments. Full article

This study presents the development and characterization of sustainable bio-bricks incorporating agricultural residues—specifically coffee husks and bovine excreta—as partial substitutes for cement. A mixture design optimized through response surface methodology (RSM) identified the best-performing formulation, namely 960 g of cement, 225 g of lignin (extracted from coffee husks), and 315 g of bovine excreta. Experimental evaluations included compressive and flexural strength, water absorption, density, thermal conductivity, transmittance, admittance, and acoustic transmission loss. The optimal mixture achieved a compressive strength of 1.70 MPa and a flexural strength of 0.56 MPa, meeting Colombian technical standards for non-structural masonry. Its thermal conductivity (~0.19 W/(m×K)) and transmittance (~0.20 W/(m²×K)) suggest good insulation performance. Field tests in three Colombian climate zones confirmed improved thermal comfort compared to traditional clay brick walls, with up to 8 °C internal temperature reduction. Acoustic analysis revealed higher sound attenuation in bio-bricks, especially at low frequencies. Chemical and morphological analyses (SEM-EDS, FTIR, and TGA) confirmed favorable thermal stability and the synergistic interaction of organic and inorganic components. The findings support bio-bricks’ potential as eco-efficient, low-carbon alternatives for sustainable building applications. Full article

With the continual expansion of global urbanization and population growth, urban energy demands have intensified, and anthropogenic activities have precipitated profound shifts in the global climate. These climatic changes directly alter urban environmental conditions, which in turn exert indirect effects on human physiological function. Consequently, the comfort of outdoor community environments has emerged as a critical metric for assessing the quality of human habitation. Although existing studies have focused on improving singular environmental factors—such as wind or thermal comfort—they often lack an integrated, multi-factor coupling mechanism, and adaptive strategy systems tailored to hot-summer, cold-winter regions remain underdeveloped. This study examines the Minhe Community in Qian’an City to develop a performance evaluation framework for outdoor spaces grounded in subjective comfort and to close the loop from theoretical formulation to empirical validation via an interdisciplinary approach. We first synthesized 25 environmental factors across eight categories—including wind, thermal, and lighting parameters—and applied the Analytic Hierarchy Process (AHP) to establish factor weights, thereby constructing a comprehensive model that encompasses both physiological and psychological requirements. Field surveys, meteorological data collection, and ENVI-met (V5.1.1) microclimate simulations revealed pronounced issues in the community’s wind distribution, thermal comfort, and acoustic environment. In response, we proposed adaptive interventions—such as stratified vegetation design and permeable pavement installations—and validated their efficacy through further simulation. Post-optimization, the community’s overall comfort score increased from 4.64 to 5.62, corresponding to an efficiency improvement of 21.3%. The innovative contributions of this research are threefold: (1) transcending the limitations of single-factor analyses by establishing a multi-dimensional, coupled evaluation framework; (2) integrating AHP with ENVI-met simulation to realize a fully quantified “evaluation–simulation–optimization” workflow; and (3) proposing adaptive strategies with broad applicability for the retrofit of communities in hot-summer, cold-winter climates, thereby offering a practical technical pathway for urban microclimate enhancement. Full article

The front wall acoustic package system plays a crucial role in automotive design, and its performance directly affects the quality and comfort of the interior noise. In response to the limitations of traditional experimental and simulation methods in terms of accuracy and efficiency, this paper proposes a convolutional neural network (AFWL-CNN) based on adaptive weighted feature learning. Using a data-driven method, the sound insulation performance of the entire vehicle’s front wall acoustic package system was predicted and analyzed based on the original parameters of the front wall acoustic package components, thereby effectively avoiding the shortcomings of traditional TPA and CAE methods. Compared to the traditional CNN model (RMSE = 0.042, MAE = 3.89 dB, I-TIME = 13.67 s), the RMSE of the proposed AFWL-CNN model was optimized to 0.031 (approximately 26.19% improvement), the mean absolute error (MAE) was reduced to 2.84 dB (approximately 26.99% improvement), and the inference time (I-TIME) increased to 17.16 s (approximately 25.53% increase). Although the inference time of the AFWL-CNN model increased by 25.53% compared to the CNN model, it achieved a more significant improvement in prediction accuracy, demonstrating a reasonable trade-off between efficiency and accuracy. Compared to AFWL-LSTM (RMSE = 0.039, MAE = 3.35 dB, I-TIME = 19.81 s), LSTM (RMSE = 0.044, MAE = 4.07 dB, I-TIME = 16.71 s), and CNN–Transformer (RMSE = 0.040, MAE = 3.74 dB, I-TIME = 19.55 s) models, the AFWL-CNN model demonstrated the highest prediction accuracy among the five models. Furthermore, the proposed method was verified using the front wall acoustic package data of a new car model, and the results showed the effectiveness and reliability of this method in predicting the acoustic package performance of the front wall system. This study provides a powerful tool for fast and accurate performance prediction of automotive front acoustic packages, significantly improving design efficiency and providing a data-driven framework that can be used to solve other vehicle noise problems. Full article

In response to the growing demand for sustainable and performance-driven building design, this study proposes an integrated digital methodology that combines Building Information Modeling (BIM), parametric scripting, and multi-criteria decision-making (MCDM) to optimize external wall assemblies. The approach leverages Autodesk Revit and Dynamo to automate the parametrization of insulation thickness while ensuring compliance with regulatory thresholds for thermal transmittance and surface mass. Acoustic performance is estimated using ECHO software, and a Weighted Sum Model (WSM) is applied to evaluate and rank configurations based on four criteria: economic cost, Global Warming Potential (GWP), embodied energy, and acoustic insulation. A case study involving 24 wall assemblies—generated from eight base stratigraphies and three insulation materials—demonstrates the method’s ability to balance environmental impact, occupant comfort, and construction feasibility. The results indicate that natural and bio-based materials, such as rammed earth and cork, offer the best overall performance, while conventional systems remain competitive in terms of cost. The proposed workflow reduces design time, increases transparency, and supports informed decision-making during early design stages. This research contributes to the digitalization of sustainability assessment in architecture by promoting integrative, replicable, and regulation-aligned practices for low-impact building envelopes. Full article

Background: Chronic obstructive pulmonary disease (COPD) is projected to be the third-leading cause of death by 2030. Traditional spirometry for the monitoring of the forced expiratory volume in one second (FEV1) can provoke discomfort and anxiety. This study aimed to validate AI models using daily audio recordings as an alternative for FEV1 estimation in home settings. Methods: Twenty-three participants with moderate to severe COPD recorded daily audio readings of standardized texts and measured their FEV1 using spirometry over nine months. Participants also recorded biomarkers (heart rate, temperature, oxygen saturation) via tablet application. Various machine learning models were trained using acoustic features extracted from 2053 recordings, with K-nearest neighbor, random forest, XGBoost, and linear models evaluated using 10-fold cross-validation. Results: The K-nearest neighbors model achieved a root mean square error of 174 mL/s on the validation data. The limit of agreement (LoA) ranged from −333.21 to 347.26 mL/s. Despite an error range of −1252 to 1435 mL/s, most predictions fell within the LoA, indicating good performance in estimating the FEV1. Conclusions: The predictive model showed promising results, with a narrower LoA compared to traditional unsupervised spirometry methods. The AI models effectively used audio to predict the FEV1, suggesting a viable non-invasive approach for COPD monitoring that could enhance patient comfort and accessibility in home settings. Full article

Sustainable development requires efficient planning and management of both natural and built resources. The identification of urban forms that best balance exposure to solar radiation and urban noise, ensuring compliance with residential construction regulations and European directives may be carried out through simulations. The proposed methodology involves simulating various scenarios and adjusting parameters of selected urban forms to evaluate the availability of solar radiation and the noise exposure on building façades within a specific context. In addressing the requirements for solar and noise optimization, predictive models (solar and noise) were employed, utilizing urban form indicators to relate these three variables. The case study demonstrates the inverse behavior of these variables in relation to the same urban forms. The findings highlight the optimal urban forms for each scenario. The enclosed form was identified as the most suitable for minimizing noise exposure, while the linear form is optimal for maximizing solar radiation exposure. This approach allows the designer to make informed decisions that balance these competing requirements, achieving a compromise between optimizing thermal and acoustic performance. The ultimate goal is to enhance the overall comfort of the building, reduce energy consumption, and promote a sustainable building solution. Full article

Current research on aged housing prioritizes community planning and environmental enhancement over older adults’ needs, creating a retrofit mismatch amid population aging. To investigate the relationship between indoor environmental quality and residential satisfaction among elderly occupants, this study examines 72 households in aged residential buildings, analyzing four environmental indicators (thermal, lighting, acoustic environments, and air quality). The environmental measurements reveal that 81.9% of thermal environment parameters fall below the ASHRAE-55 comfort range, with winter average temperatures reaching only 13.94 °C. Insufficient illumination exists in kitchen and bedroom areas. Lifestyle patterns including infrequent air conditioning use (87%) and window ventilation substituting range hoods (32%) may deteriorate thermal comfort and air quality. An ordered logistic regression analysis demonstrates significant correlations between all four environmental indicators and elderly satisfaction levels. Thermal comfort emerges as the priority focus for aging-adapted retrofitting. Air quality improvement shows particularly significant potential for enhancing residential satisfaction. Although prolonged window opening (73%) exacerbates low-temperature/high-humidity conditions and noise exposure, it still contributes positively to overall satisfaction. This research provides crucial insights for aligning aged residential retrofitting with home-based elderly care requirements, promoting housing development that better accommodates the lifestyle patterns of older populations, thereby improving quality of life for aging-in-place residents. Full article