Search Results (49)

Smart-beehive technologies represent a paradigm shift in beekeeping, transitioning from traditional, reactive methods toward proactive, data-driven management. This systematic literature review investigates the current landscape of intelligent systems applied to beehives, focusing on the integration of IoT-based monitoring, sensor modalities, machine learning techniques, and their applications in precision apiculture. The review adheres to PRISMA guidelines and analyzes 135 peer-reviewed publications identified through searches of Web of Science, IEEE Xplore, and Scopus between 1990 and 2025. It addresses key research questions related to the role of intelligent systems in early problem detection, hive condition monitoring, and predictive intervention. Common sensor types include environmental, acoustic, visual, and structural modalities, each supporting diverse functional goals such as health assessment, behavior analysis, and forecasting. A notable trend toward deep learning, computer vision, and multimodal sensor fusion is evident, particularly in applications involving disease detection and colony behavior modeling. Furthermore, the review highlights a growing corpus of publicly available datasets critical for the training and evaluation of machine learning models. Despite the promising developments, challenges remain in system integration, dataset standardization, and large-scale deployment. This review offers a comprehensive foundation for the advancement of smart apiculture technologies, aiming to improve colony health, productivity, and resilience in increasingly complex environmental conditions. Full article

Escherichia coli (E. coli) is one of the most common strains that produce Shiga toxin, which can contaminate food and water, causing serious diseases and even endangering life. Therefore, the detection of E. coli is crucial for protecting public health. At present, most traditional methods have disadvantages such as long detection cycles, high cost, and complex operations. This article proposed a novel commercial Rayleigh surface acoustic wave (R-SAW) biosensor for the detection of trace amounts of E. coli, which utilized the coordination reaction between carboxyl (-COOH) groups and aluminum ions (Al³⁺) to form the bio-enhanced probes, enabling the 5-terminal -COOH-modified aptamers to be preferentially enriched and directionally immobilized on the electrode surface. The biosensor could complete the detection within 100 s, with a linear detection range of 10³–10⁸ cells/mL, a limit of detection (LOD) as low as 732 cells/mL, and a selectivity ratio of 3270:1. This article conducted spiked detection on six types of food, indicating that the biosensor had the advantages of rapid speed, high sensitive, wide detection range, low LOD, strong specificity, and low cost, providing an economical and convenient solution for detecting trace amounts of E. coli in food. Full article

With the increasing severity of urban noise pollution, its detrimental impact on public health has garnered growing attention. However, accurate identification and classification of noise sources in complex urban acoustic environments remain major technical challenges for achieving refined noise management. To address this issue, this study presents two key contributions. First, we construct a new urban noise classification dataset, namely the urban noise 15-category dataset (UN15), which consists of 1620 audio clips from 15 representative categories, including traffic, construction, crowd activity, and commercial noise, recorded from diverse real-world urban scenes. Second, we propose a novel deep neural network architecture based on a residual network and integrated with a time–frequency attention mechanism, referred to as residual network with temporal–frequency attention (ResNet-TF). Extensive experiments conducted on the UN15 dataset demonstrate that ResNet-TF outperforms several mainstream baseline models in both classification accuracy and robustness. These results not only verify the effectiveness of the proposed attention mechanism but also establish the UN15 dataset as a valuable benchmark for future research in urban noise classification. Full article

Background: Perceptual analysis has highlighted that the voice characteristics of patients with rare congenital genetic syndromes differ from those of normophonic subjects. In this paper, we describe the voice phenotype, also called the phonotype, of patients with Crisponi/cold-induced sweating syndrome type 1 (CS/CISS1). Methods: We conducted an observational study at the Department of Life Sciences and Public Health, Rome. Thirteen patients were included in this study (five males; mean age: 16 years; SD: 10.63 years; median age: 12 years; age range: 6–44 years), and five were adults (38%). We prospectively recorded and analyzed acoustical features of three corner vowels [a], [i], and [u]. For perceptual analysis, the GIRBAS (grade, instability, roughness, breathiness, asthenia, and strain) scale was utilized. Acoustic analysis was performed through BioVoice software. Results: We found that CS/CISS1 patients share a common phonotype characterized by articulation disorders and hyper-rhinophonia. Conclusions: This study contributes to delineating the voice of CS/CISS1 syndrome. The phonotype can represent one of the earliest indicators for detecting rare congenital conditions, enabling specialists to reduce diagnosis time and better define a spectrum of rare and ultra-rare diseases. Full article

Motorcycle horns are a dominant source of urban noise in many Southeast Asian cities, driven by high two-wheeler density and limited public transport infrastructure. Although automobiles have been in use for over a century, regulations governing horn design and volume control remain inadequate. This study investigates horn use behavior in Vietnamese urban traffic, identifying distinct acoustic patterns categorized as “attention” and “warning” signals. Measurements conducted in an anechoic chamber reveal that these patterns can increase sound pressure levels by up to 17 dB compared to standard horn use, with notable differences in frequency components. These levels often exceed the daytime noise thresholds recommended by the World Health Organization (WHO), indicating potential risks for adverse health outcomes, such as elevated stress, hearing damage, sleep disturbance, and cardiovascular effects. The findings are contextualized within broader efforts to manage traffic noise in rapidly developing urban areas. Drawing parallels with studies on aircraft noise exposure in Japan, this study suggests that long-term exposure, rather than peak noise levels alone, plays a critical role in shaping community sensitivity. The study results support the need for updated noise regulations that address both the acoustic and perceptual dimensions of road traffic noise. Full article

Trees play a vital role in urban environments by mitigating heat islands, floods, and pollution, while promoting public health and well-being. Acoustic tomography is an effective tool for assessing tree integrity, but its high-cost limits widespread use. To reduce costs, this study evaluated the use of ultrasonic tomography with standardized velocity thresholds (VTs) for detecting cavities and decay in trunks. A total of 38 discs from 21 trees species were analyzed using different VTs (35%, 40%, 45%, and 50%). The results showed that thresholds of 35% Vmax for cavity detection and 50% Vmax for cavity with decay detection can be adopted for tomographic image assessments of trees, regardless of species. Using the same velocity thresholds regardless of species enables the practical application of the technology, with average accuracy losses (below 5%) that are quite reasonable considering the variability of the material under inspection. These findings support the broader use of technology in tree failure risk assessments. Full article

The increasing demands for the reliability of modern industrial equipment and structures necessitate advanced techniques for design, monitoring, and analysis. This review article presents the latest research advancements in the application of machine learning techniques to vibration and acoustic signal analysis from 2015 to 2024. A total of 96 peer-reviewed scientific publications were examined, selected using a systematic Scopus-based search. The main research areas include processes such as modeling and design, health management, condition monitoring, non-destructive testing, damage detection, and diagnostics. In the context of these processes, a review of machine learning techniques was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoders, support vector machines (SVMs), decision trees (DTs), nearest neighbor search (NNS), K-means clustering, and random forests. These techniques were applied across a wide range of engineering domains, including civil infrastructure, transportation systems, energy installations, and rotating machinery. Additionally, this article analyzes contributions from different countries, highlighting temporal and methodological trends in this field. The findings indicate a clear shift towards deep learning-based methods and multisensor data fusion, accompanied by increasing use of automatic feature extraction and interest in transfer learning, few-shot learning, and unsupervised approaches. This review aims to provide a comprehensive understanding of the current state and future directions of machine learning applications in vibration and acoustics, outlining the field’s evolution and identifying its key research challenges and innovation trajectories. 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

Unmanned aerial systems (UAS), commonly known as drones, have gained widespread use due to their affordability and versatility across various domains, including military, commercial, and recreational sectors. Applications such as remote sensing, aerial imaging, agriculture, firefighting, search and rescue, infrastructure inspection, and public safety have extensively adopted this technology. However, environmental impacts, particularly noise, have raised concerns among the public and local communities. Unlike traditional crewed aircraft, drones typically operate in low-altitude airspace (below 400 feet or 122 m), making their noise impact more significant when they are closer to houses, people, and livestock. Numerous studies have explored methods for monitoring, assessing, and predicting the noise footprint of drones. This study employs a bibliometric analysis of relevant scholarly works in the Web of Science Core Collection, published from 2015 to 2024, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) data collection and screening procedures. The International Journal of Environmental Research and Public Health, Aerospace Science and Technology, and the Journal of the Acoustical Society of America are the top three preferred outlets for publications in this area. This review unveils trends, topics, key authors and institutions, and national contributions in the field through co-authorship analysis, co-citation analysis, and other statistical methods. By addressing the identified challenges, leveraging emerging technologies, and fostering collaborations, the field can move towards more effective noise abatement strategies, ultimately contributing to the broader acceptance and sustainable integration of UASs into various aspects of society. Full article

High-frequency noise exceeding 1 kHz has emerged as a pressing public health issue in industrial and occupational settings. In response to this challenge, the present study explores the development of a graphene oxide–polyethyleneimine (GO-PEI) foam (GPF) featuring a hierarchically porous structure. The synthesis and optimization of GPF were carried out using a range of analytical techniques, including Raman spectroscopy, scanning electron microscopy (SEM), Braunauer–Emmett–Teller (BET) analysis, X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FT-IR). To evaluate its acoustic properties, GPF was subjected to sound absorption tests over the 1000–6400 Hz frequency range, where it was benchmarked against conventional melamine foam. The findings demonstrated that GPF with a GO-to-PEI composition ratio of 1:3 exhibited enhanced sound absorption performance, with improvements ranging from 15.0% to 118%, and achieved a peak absorption coefficient of 0.97. Additionally, we applied the Johnson–Champoux–Allard (JCA) model to further characterize the foam’s acoustic behavior, capturing key parameters such as porosity, flow resistivity, and viscous/thermal losses. The JCA model exhibited a superior fit to the experimental data compared to traditional models, providing a more accurate prediction of the foam’s complex microstructure and sound absorption properties. These findings underscore GPF’s promise as an efficient solution for mitigating high-frequency noise in industrial and environmental applications. Full article

Optimizing the visual, thermal, and acoustic environments of urban public spaces in severely cold regions can significantly enhance the psychological restoration of the elderly, addressing the increasing mental health demands in an aging society. Despite its importance, the mechanisms, strategies, and seasonal effects of various environmental variables on psychological restoration remain inadequately studied. This research uses Harbin as a case study, employing field surveys and tests to systematically examine the elderly’s psychological restoration across different seasons. By integrating environmental stimulus variables with a psychological restoration evaluation model, the study investigates the impact of urban public spaces on mental health. The key findings are: (1) The spring environment of urban public spaces has the most significant positive effect on psychological restoration, with an effect size of η² = 0.360. (2) A significant correlation exists between environmental variables and psychological restoration year-round, with the panoramic green view index in winter showing the highest positive impact (correlation coefficient = 0.301, p < 0.01). (3) The influence of environmental stimulus variables on psychological restoration varies notably across seasons; the acoustic environment in spring contributes most significantly, with an R² = 17.03%, while visual factors dominate in winter and summer. (4) Conditional probability analysis reveals the effects of various environmental variables on psychological restoration, proposing season-specific environment optimization strategies. Based on these findings, the paper presents a model for optimizing urban public space environments in severely cold regions, aiming to maximize elderly psychological restoration by tailoring environmental stimulus variables to their mental health needs. Full article

Attention is a pivotal component and a central vehicle of mindfulness, a psychological factor improving mental health. Despite architecture’s potential to encourage attention and mindfulness, there is still a research gap. This study aimed to investigate architectural design strategies that promotes attention in order to foster mindfulness. The research was carried out in three primary stages. The first step entailed conducting a systematic review by searching publications related to architecture that promotes attention from Scopus in February 2024. After considering the suitability and accessibility, 32 articles were included. No studies were found to have investigated the field of enhancing mindfulness. The second step utilized content analysis to decode the selected articles using a framework developed from literature reviews. All three coders decoded the data independently, allowing the main researcher to compile it into the final dataset. Finally, the data underwent Python meta-analysis for word frequency and association. The result revealed certain qualities that help achieve attention through architecture. The architectural atmosphere is most effective when it features natural forms and spaces that evoke a sense of enclosure. The lighting should emphasize natural light and uniformity, whereas the sound designs primarily concern acoustics, ambient, and noises, with controlled weather emphasizing air aspects. The building should utilize natural materials and incorporate object elements; the facade and entrance are particularly crucial components. Moreover, the colors of brick and green and views encompassing gardens and vegetation are among the qualities mentioned. Based on the analysis, the material, view, and color features were most congruent with the biophilic design concept. All these factors are expected to foster mindfulness, thereby improving mental health. Full article

Public open spaces make crucial contributions to the livability of communities and promote physical and mental health. Soundscapes play an important role in the overall physical comfort of public open spaces. However, owing to insufficient studies of high-density situations, soundscapes are ignored in public open spaces in high-density residential areas. This paper presents a case study of a soundscape in the overseas Chinese town (OCT) of Shenzhen, China. Through in situ observation, four distinct soundscapes were easily identified by performing soundscape conceptualization according to the ISO. In terms of the four identified soundscape areas, subjective evaluations of acoustic comfort and annoyance and their influencing factors were thoroughly explored. The results reveal that the natural soundscape had the best evaluations, whereas the artificial one did not have the worst result. It is interesting to note that acoustic factors do not always significantly influence a soundscape’s evaluation. A non-acoustic factor such as the spatial function may play a role as it is related to the context of an individual perceiving an acoustic environment. This study provides first-hand empirical evidence for understanding soundscapes and the influencing factors present in high-density residential public open spaces. The results provide useful knowledge for enhancing soundscape quality in such spaces. Full article

Improving street jogging environments can promote urban health and sustainability, especially within urban built-up areas. Public attention to the environment during jogging has not been sufficiently studied, and the recommended route data from the fitness app offers an opportunity for multiscale spatial exploration. Initially, by analyzing the recommendation reason text, the study identified 22 environmental factors and proposed the Environmental Factor Public Attention Intensity (EFPAI) for quantification, with subsequent comparisons to expert opinions. Further, by analyzing five types of data from the recommended routes, the relationships between EFPAI and jogging frequency, route shape, spatial distribution, and seasonal variations were uncovered. The study revealed: (1) discrepancies between expert opinions and public attention, with experts more likely to overlook factors such as slope, visual quality, width, and acoustic quality; (2) the public tends to notice factors like pedestrians, green spaces, waterfront spaces, landscapes, culture, sports facilities, and accessibility on routes with high jogging frequency; (3) EFPAI for traffic infrastructure, waterfront spaces, landscapes, residential areas, and campuses is associated with route shape; (4) EFPAI of certain environmental factors is influenced by urban geographical spatial factors, and substantial differences exist between different cities. Specific recommendations were provided from three different scales—site scale, street block scale, and urban scale—to guide the resolution of issues in planning and decision-making processes for urban renewal. Full article

This review analyzed scientific publications on noise pollution in 22 Arab countries from 1983 to 2022. The objective of this study was to assess the current state of research on noise pollution in the Arab world, identify trends and gaps in the literature, and subsequently formulate a thorough research agenda. A comprehensive search of the Scopus database was conducted using relevant search terms and inclusion criteria. Out of 249 results, 104 studies were selected. The data extraction from the 104 studies includes the country where the study was conducted/designed; the publication year; the journal of publication; sponsorship details; and the research methodology used. VOSviewer software (VOSviewer 1.6.18) was used to visualize the literature data and co-occurrence networks, collaborations, and research fronts. The analysis revealed that Saudi Arabia, Jordan, and Kuwait were the most active countries in noise pollution research, while Sudan and Libya had the lowest number of publications in this field. Additionally, countries like Somalia, Syria, Yemen, and Mauritania had no publications on this subject. Research on noise pollution in this region mainly focuses on noise in urban zones and workplaces, particularly in Saudi Arabia, Kuwait, and Jordan. Topics vary across other Arab countries. The results highlight the increasing significance of noise pollution research in the Arab world, particularly evident in recent periods. However, scientific output from Arab countries remains significantly limited compared to other global contexts. This study emphasizes the pressing requirement for increased investment in noise pollution research, aiming to achieve knowledge of the acoustic situation in Arab countries in order to develop strategies to mitigate population exposure to noise pollution, improve the well-being of citizens, and protect public health. Full article