Search Results (165)

Mycelium-based composites (MBCs) are an emerging category of cost-effective and environmentally sustainable materials that are attracting significant research and commercial interest across various industries, including construction, manufacturing, agriculture, and biomedicine. These materials harness the natural growth of fungi as a low-energy bio-fabrication method, converting abundant agricultural by-products and waste into sustainable alternatives to energy-intensive synthetic construction materials. Their affordability and eco-friendly characteristics make them attractive for both research and commercialisation. Currently, mycelium-based foams and sandwich composites are being actively developed for applications in construction. These materials offer exceptional thermal insulation, excellent acoustic absorption, and superior fire safety compared to conventional building materials like synthetic foams and engineered wood. As a result, MBCs show great potential for applications in thermal and acoustic insulation. However, their foam-like mechanical properties, high water absorption, and limited documentation of material properties restrict their use to non- or semi-structural roles, such as insulation, panelling, and furniture. This paper presents a comprehensive review of the fabrication process and the factors affecting the production and performance properties of MBCs. It addresses key elements such as fungal species selection, substrate choice, optimal growth conditions, dehydration methods, post-processing techniques, mechanical and physical properties, termite resistance, cost comparison, and life cycle assessment. Full article

In recent years, passive acoustic monitoring (PAM) technology has significantly contributed to advancements in aquaculture techniques, system iterations, and increased production yields within intelligent feeding systems for Penaeus vannamei. However, current PAM-based intelligent feeding systems do not incorporate environmental factors into the decision process, limiting the improvement of monitoring accuracy in complex environments such as ponds. To establish a connection between environmental factors and the feeding acoustics of P. vannamei, this study utilized PAM technology combined with video analysis to investigate the effects of three key environmental factors—temperature, ammonia nitrogen, and nitrite nitrogen—on the feeding behavioral characteristics of shrimp, with a specific focus on acoustic signals “clicks”. The results demonstrated a significant correlation between the number of clicks and feed consumption in shrimp across different treatments, establishing this stable relationship as a reliable indicator for assessing shrimp feeding status. When water temperature increased from 20 °C to 32 °C, shrimp feed consumption showed an elevation from 0.46 g to 0.95 g per 30 min, with the average number of clicks increasing from 388 to 2947.58 and sound pressure levels rising accordingly. Conversely, ammonia nitrogen at 12 mg/L reduced feed consumption by 0.15 g and decreased click counts by 911.75 pulses compared to controls, while nitrite nitrogen at 40 mg/L similarly suppressed feed consumption by 0.15 g and the average number of clicks by 304.75. A rise in water temperature stimulated shrimp behaviors such as feeding, swimming, and foraging, while elevated concentrations of ammonia nitrogen and nitrite nitrogen significantly inhibited shrimp activity. Redundancy analysis revealed that temperature was the most prominent factor among the three environmental factors influencing shrimp feeding. This study is the first to quantify the specific effects of common environmental factors on the acoustic feeding signals and feeding behavior of P. vannamei using PAM technology. It confirms the feasibility of using PAM technology to assess shrimp feeding conditions under diverse environmental conditions and the necessity of integrating environmental monitoring modules into future feeding systems. This study provides behavioral evidence for the development of precise feeding technologies and the upgrade of intelligent feeding systems for P. vannamei. Full article

The underwater acoustic (UWA) communication system faces challenges due to environmental factors, extensive multipath spread, and rapidly changing propagation conditions. Deep learning based solutions, especially for orthogonal frequency division multiplexing (OFDM) receivers, have been shown to improve performance. However, the UWA channel characteristics are highly dynamic and depend on the specific underwater conditions. Therefore, these models suffer from model mismatch when deployed in environments different from those used for training, leading to performance degradation and requiring costly, time-consuming retraining. To address these issues, we propose a transfer learning (TL)-based pre-trained model for OFDM based UWA communication. Rather than training separate models for each underwater channel, we aggregate received signals from five distinct WATERMARK channels, across varying signal to noise ratios (SNRs), into a unified dataset. This diverse training set enables the model to generalize across various underwater conditions, ensuring robust performance without extensive retraining. We evaluate the pre-trained model using real-world data from Qingdao Lake in Hangzhou, China, which serves as the target environment. Our experiments show that the model adapts well to these challenging environment, overcoming model mismatch and minimizing computational costs. The proposed TL-based OFDM receiver outperforms traditional methods in terms of bit error rate (BER) and other evaluation metrics. It demonstrates strong adaptability to varying channel conditions. This includes scenarios where training and testing occur on the same channel, under channel mismatch, and with or without fine-tuning on target data. At 10 dB SNR, it achieves an approximately 80% improvement in BER compared to other methods. Full article

Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karabük, Türkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy. 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

Fruit quality testing plays a crucial role in the advancement of fruit industry, which is related to market competitiveness, consumer satisfaction and production process optimization. In recent years, nondestructive testing technology has become a research hotspot due to its outstanding advantages. In this paper, the principle, application, advantages and disadvantages of optical, acoustic, electromagnetics, dielectric properties research and electronic nose non-destructive testing technology in fruit quality testing are systematically reviewed. These technologies can detect a variety of chemical components of fruit, realize the assessment of maturity, damage degree, disease degree, and are suitable for orchard picking, quality grading, shelf life prediction and other fields. However, there are limitations to these techniques. The optical, acoustic and electronic nose technologies are susceptible to environmental factors, the electromagnetic technology has defects in the detection of complex molecules and fruit internal quality, and the dielectric characteristics are greatly affected by the shape and state of the sample surface. In the future, efforts should be made to enhance the implementation of non-destructive testing technology in the fruit industry through technology integration, optimization algorithm, cost reduction, and expansion of industrial chain application, so as to help the premium growth of the fruit industry. Full article

Cemented paste backfill (CPB) plays an important role in sustainable mining by providing structural support and reducing surface subsidence. While traditional destructive testing methods such as unconfined compressive strength (UCS) tests offer valuable understanding of material strength, they require a lot of resources, are time-consuming, and environmentally unfriendly. However, non-destructive testing (NDT) techniques such as ultrasonic pulse velocity (UPV), electrical resistivity (ER), and acoustic emission (AE) provide sustainable alternatives by preserving sample integrity, minimizing waste, and enabling real-time monitoring. This study systematically reviews and quantitatively compares the effectiveness of UPV, ER, and AE in predicting the strength of CPB. Meta-analysis of 30 peer-reviewed studies reveals that UPV and AE provide the most consistent and reliable correlations with UCS, with R² values of 0.895 and 0.896, respectively, while ER shows more variability due to its sensitivity to environmental factors. Additionally, a synthetic model combining UPV, AE and ER demonstrates improved accuracy in predicting strength. This hybrid approach enhances predictions of material performance while supporting sustainability in mining and construction. Our research advocates for better testing practices and presents a promising direction for future infrastructure projects, where real-time, non-invasive monitoring can enhance material performance evaluation and optimize resource use. 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

Lightweight artificial aggregates (LWAs) are widely used in civil construction, but their conventional production depends on pure clays, a finite natural resource that negatively impacts the environment. This study aims to contribute to minimizing this issue by exploring the use of sustainable ternary mixtures of kaolinitic clay (KC), chamotte residues (CHT), and eucalyptus firewood ash (EFA), promoting a more environmentally friendly approach to the manufacture of LWAs. Thus, the aim was to develop and optimize LWAs using different replacements of industrial waste. Furthermore, the Taguchi method is employed to identify the optimal manufacturing parameters, such as waste content, sintering temperature, and heating time. The research involved the production of 32 distinct mixtures with different proportions of KC, CHT, and EFA, processed through grinding and sintering at temperatures ranging from 1075 °C to 1180 °C. The samples were evaluated for density, water absorption, mechanical strength, and expansion index. Statistical analysis was conducted using ANOVA to validate the most significant factors. The results revealed that mixtures with 80% of waste presented an aggregate expansion index of up to 60%, a minimum bulk density of 1.20 g/cm³ (which aligns with requirements for structural applications but exceeds the maximum bulk density for some lightweight aggregates), and crushing strength higher than 5 MPa, satisfying the normative criteria for commercial LWAs. In addition, 63 industrial applications were identified for the developed materials, ranging from structural lightweight concretes to thermal and acoustic insulation with varied microstructures. Therefore, the partial replacement of clay by CHT and EFA waste represents a promising alternative for producing sustainable LWAs, helping to reduce environmental impacts while providing quality materials for various applications in the most diverse industrial sectors. Full article

Despite the formal protection of many natural areas, the problem of noise pollution poses a serious challenge to the preservation of their ecological integrity and biodiversity. Traffic noise generated by vehicle traffic on public roads disrupts natural biological processes, negatively affecting animals and the quality of the audiosphere. This research aimed to assess the acoustic impact of the public road network crossing the Roztocze National Park (RPN, Poland) and to characterize noise propagation as a factor polluting the environment and disrupting the functioning of natural forest ecosystems. The equivalent sound pressure level (L_Aeq) was measured at different distances from four public roads crossing the park. A terrain analysis was also taken into account to determine the impact of height differences on sound propagation. To enhance the acoustic analysis, recordings of environmental sounds were made, and their components, including both natural and anthropogenic sounds, were identified. It was found that traffic noise dominated natural sounds at distances 250 m from roads. The results obtained indicate the need for an integrated approach to protected area management, including noise monitoring, the implementation of noise protection regulations, and environmental education. Full article

Underwater wireless communications face significant challenges due to high attenuation, turbulence, and water turbidity. Traditional methods like acoustic and radio frequency (RF) communication suffer from low data rates (<100 kbps), high latency (>1 s), and limited transmission distances (<10 km).Visible Light Communication (VLC) emerges as a promising alternative, offering high-speed data transmission (up to 5 Gbps), low latency (<1 ms), and immunity to electromagnetic interference. This paper provides an in-depth review of underwater VLC, covering fundamental principles, environmental factors (scattering, absorption), and dynamic water properties. We analyze modulation techniques, including adaptive and hybrid schemes (QAM-OFDM achieving 4.92 Gbps over 1.5 m), and demonstrate their superiority over conventional methods. Practical applications—underwater exploration, autonomous vehicle control, and environmental monitoring—are discussed alongside security challenges. Key findings highlight UVLC’s ability to overcome traditional limitations, with experimental results showing 500 Mbps over 150 m using PAM4 modulation. Future research directions include integrating quantum communication and Reconfigurable Intelligent Surfaces (RISs) to further enhance performance, with simulations projecting 40% improved spectral efficiency in turbulent conditions. Full article

The transmission efficiency of underwater acoustic is doubly constrained by absorption attenuation and geometric spreading losses, with the relative interaction between these loss mechanisms exhibiting complex dynamic variations across the frequency spectrum. Achieving dynamic equilibrium between these frequency-dependent loss mechanisms is key to enhancing acoustic energy transmission performance. To address this, this paper proposes a multi-variable coupled acoustic energy transmission model that systematically integrates the cumulative effects of the propagation distance, the geometric configuration of acoustic source arrays, and the interactive influences of critical environmental factors such as the salinity, temperature, and depth to comprehensively analyze the synergistic mechanisms of absorption loss and geometric spreading loss in practical underwater environments. Based on dynamic response analysis in the frequency dimension, the model identifies and determines the optimal working frequency ranges (i.e., dynamic equilibrium points) for maximizing the efficiency of energy transmission under various propagation conditions and environmental configurations. Both theoretical derivations and numerical simulations consistently reveal a frequency band within the low-to-mid frequency range (approximately 20–100 kHz) which is associated with significantly enhanced transmission efficiency under specific parameter settings. These research findings provide a scientific basis and engineering guidance for frequency selection and the structural optimization of underwater acoustic energy systems, offering substantial theoretical value and application prospects that can strongly support the development of acoustic technologies in ocean engineering, resource exploration, and national defense security. Full article

Despite the growing emphasis on cognitive health, evidence regarding individual and environmental factors influencing cognitive functioning remains limited. We aimed to examine the association of personal and environmental factors, specifically perceived acoustic environment exposure and nonrestorative sleep, with cognitive functioning among Chinese adults. Between July and August 2022, we recruited 970 Chinese adults from all districts in Hong Kong for our cross-sectional study. Univariable and structured multiphase linear regression analyses were conducted to identify the contributory factors. Among 970 Chinese adults, the structured multiphase linear regression model revealed that being in their 30s (β = 0.160; 95% CI: 0.004, 0.315) and 40s (β = 0.186; 95% CI: 0.029, 0.343), being female (β = 0.095; 95% CI: 0.018, 0.173), being former smokers, and having medical consultations, medical histories (eczema, hearing problems, and insomnia), perceived acoustic environment exposure (traffic and mechanical sounds (β = 0.011; 95% CI: 0.008, 0.013), nature and music sounds (β = 0.004; 95% CI: 0.001, 0.007), and human sounds (β = 0.002; 95% CI: 0.0004, 0.005)), and psychological symptoms (perceived stress, depressive, and anxiety symptoms) were associated with worse cognitive functioning. Moreover, more nonrestorative sleep (β = −0.015; 95% CI: −0.022, −0.007) was also associated with worse cognitive functioning. This study revealed that increased perceived acoustic environment exposure and a higher degree of nonrestorative sleep were associated with poorer cognitive functioning among Chinese adults. This underscores the need for public health strategies and policies aimed at fostering a healthy acoustic environment and promoting sleep hygiene education in the community. Full article

In response to the demand for high-precision positioning within confined or indoor environments, the application of acoustic ranging methods has been widely adopted by numerous engineers. Currently, time-of-flight (TOF)-based acoustic ranging positioning systems face challenges such as the susceptibility of sound velocity to environmental factors and the loss of acoustic signals at both short and long distances, which leads to a reduction in positioning accuracy. This paper addresses these issues by proposing a high-precision confidence interval weighting method for acoustic ranging and further introduces a method for base station deployment and self-optimization positioning within fixed indoor base station scenarios. The method is based on trilateration positioning, establishing criteria for the division of central and boundary areas. It categorizes mobile terminal nodes based on their coordinates from the previous moment, selects distance information from nearby base stations in different modes, and employs weights for decision-making and computation, ultimately yielding two-dimensional positioning coordinates. Experiments demonstrate that the proposed method can effectively enhance the positioning accuracy of acoustic positioning systems compared to traditional four-base station weighted average positioning algorithms. Full article

This study investigates the critical role of indoor environmental quality (IEQ) adaptations in influencing human physiological responses within commercial building settings. By integrating environmental engineering and human physiology, this research offers empirical insights into the relationship between IEQ modifications and occupant well-being, particularly in the context of energy performance and efficiency. This study examines correlations between human physiological responses and key IEQ components, including indoor air quality (IAQ), thermal comfort, lighting, and acoustics, using data collected from two office areas with 14 participants. Sensors tracked environmental parameters, while wearable devices monitored physiological responses. Cross-correlation analysis revealed significant relationships between physiological indicators and environmental factors, with indoor temperature, PM_2.5, and relative humidity showing the strongest impacts on electrodermal activity, skin temperature, and stress levels, respectively (p < 0.05). Furthermore, supervised machine learning techniques were employed to develop predictive models that evaluate IAQ and thermal comfort at both personal and general levels. Individual models achieved 84.76% accuracy for IAQ evaluation and 70.5% for thermal comfort prediction, outperforming the general model (69.7% and 64.3%, respectively). Males showed greater overall sensitivity to IEQ indicators, while females demonstrated higher sensitivity specifically to air quality and thermal comfort conditions. The findings underscore the potential of physiological signals to predict environmental satisfaction, providing a foundation for designing energy-efficient buildings that prioritize occupant health and comfort. This research bridges a critical gap in the literature by offering data-driven approaches to align sustainable building practices with human-centric needs. Future studies should expand participant diversity and explore broader demographics to enhance the robustness and applicability of predictive models. Full article