Search Results (2,446)

Urban green spaces are essential for promoting human health and well-being, especially in cities facing increasing noise pollution and ecological stress. This study investigates the effects of audio-visual interaction on restorative outcomes across three soundscape types (park, residential, and street), focusing on the compensatory role of positive visual stimuli in low-quality soundscape environments. Thirty-two university students participated in a controlled evaluation using soundscapes and corresponding visual materials derived from 30 urban green spaces. A two-way repeated measures ANOVA revealed significant main effects of soundscape type and modality (auditory vs. audio-visual), as well as a significant interaction between these factors. Audio-visual conditions consistently outperformed auditory conditions, with the strongest restorative effects observed in noisy street soundscapes when paired with positive visual stimuli. Further analysis highlighted that visual cleanliness and structural clarity significantly enhanced restorative outcomes in challenging environments. These findings align with existing theories of sensory integration and extend their application to large-scale urban settings. This study shows that multi-sensory optimization can mitigate urban environmental stressors, supporting healthier, more resilient, and sustainable urban environments. Future research should explore long-term and cross-cultural applications to inform evidence-based urban planning and public health policies. Full article

Urban traffic noise has become an increasingly significant environmental and public health issue, with many cities—particularly those experiencing rapid urban growth, such as Kuwait—recording levels that often exceed recommended limits. In this study, we present a detailed, data-driven approach for assessing and predicting equivalent continuous noise levels (LAeq) in residential neighborhoods. The analysis draws on measurements taken at 12 carefully chosen sites covering different road types and urban settings, resulting in 21,720 matched observations. A range of predictors was considered, including road classification, traffic composition, meteorological variables, spatial context, and time of day. Four predictive models—Linear Regression, Support Vector Machine (SVM), Gaussian Process Regression, and Bagged Trees—were evaluated through 5-fold cross-validation. Among these, the Bagged Trees model achieved the strongest performance (R² = 0.91, RMSE = 2.13 dB(A)). To better understand how the model made its predictions, we used SHAP (SHapley Additive Explanations) analysis, which showed that road classification, location, heavy vehicle volume, and time of day had the greatest influence on noise levels. The results identify the main determinants of traffic noise in Kuwait’s urban areas and emphasize the role of targeted design and planning in its mitigation. Full article

Fish species’ biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (Miichthys miiuy), hampers data-driven bioacoustic methodology development. In this study, we present a framework for reconstructing brown croaker vocalizations by integrating fk14 wavelet synthesis, PSO-based parameter optimization (with an objective combining correlation and normalized MSE), and deep learning-based validation. Sensitivity analysis using a normalized Bartlett processor identified delay and scale (length) as the most critical parameters, defining valid ranges that maintained waveform similarity above 98%. The reconstructed signals matched measured calls in both time and frequency domains, replicating single-pulse morphology, inter-pulse interval (IPI) distributions, and energy spectral density. Validation with a ResNet-18-based Siamese network produced near-unity cosine similarity (~0.9996) between measured and reconstructed signals. Statistical analyses (95% confidence intervals; residual errors) confirmed faithful preservation of SPL values and minor, biologically plausible IPI variations. Under noisy conditions, similarity decreased as SNR dropped, indicating that environmental noise affects reconstruction fidelity. These results demonstrate that the proposed framework can reliably generate acoustically realistic and morphologically consistent fish vocalizations, even under data-limited scenarios. The methodology holds promise for dataset augmentation, PAM applications, and species-specific call simulation. Future work will extend this framework by using reconstructed signals to train generative models (e.g., GANs, WaveNet), enabling scalable synthesis and supporting real-time adaptive modeling in field monitoring. Full article

To address environmental pollution issues and optimize the utilization of waste biomass resources, this study proposes a novel eco-friendly sound-absorbing material based on maple leaf waste and tests its sound absorption performance. The fibers were extracted from maple leaf waste through a wet decomposition and grinding process. Metallurgical microscopy was employed to observe the microstructural characteristics of maple leaf fibers to identify the potential synergistic effect. The effects of two key factors—sample thickness and mass density—on sound absorption performance were investigated. The sound absorption coefficients were measured using the transfer function method in a dual-microphone impedance tube to evaluate their sound-absorbing performance. Experimental results demonstrate that the prepared maple leaf fibers, as acoustic materials, exhibit excellent acoustic performance across a wide frequency range, with an average sound absorption coefficient of 0.7. Increasing sample thickness improves the sound absorption coefficient in low- and mid-frequency ranges. Additionally, increased sample mass density was found to enhance acoustic performance in low- and mid-frequency bands. This study developed an eco-friendly material with lightweight and efficient acoustic absorption properties using completely biodegradable maple leaf waste. The results provide high-performance, economical, and ecologically sustainable solutions for controlling building and traffic noise while promoting the development of eco-friendly acoustic materials. Full article

The systematic economic development of European Union member states has resulted in a dynamic increase in road transport, accompanied by adverse environmental impacts. Consequently, research efforts have focused on identifying technical solutions to reduce fuel and/or energy consumption. One promising approach involves the formation of homogeneous and heterogeneous vehicle platoons. This study presents the results of numerical simulations and analyses of aerodynamic and aeroacoustic phenomena generated by heterogeneous vehicle platoons composed of passenger cars, delivery vans, and trucks. A total of 54 numerical models were developed in various configurations, considering three vehicle speeds and three inter-vehicle distances. The analysis was conducted using Computational Fluid Dynamics (CFD) methods with the following two turbulence models: the k–ω Shear Stress Transport (SST) model and Large Eddy Simulation (LES), combined with the Ffowcs Williams–Hawkings acoustic analogy to determine sound pressure levels. Verification calculations were performed using methods dedicated to environmental noise analysis, supplemented by acoustic field measurements. The results conclusively demonstrate that vehicle movement in specific platoon configurations can lead to significant fuel and/or energy savings, as well as reductions in harmful emissions. This solution may be implemented in the future as an integral component of Intelligent Transportation Systems (ITSs) and Intelligent Environmental Management Systems (IEMSs). Full article

This paper presents a comprehensive evaluation of keypoint detection and description algorithms for stereo vision-based odometry in dynamic environments. Five widely used methods—FAST, GFTT, ORB, BRISK, and KAZE—were analyzed in terms of detection accuracy, robustness to image distortions, computational efficiency, and suitability for embedded systems. Using the KITTI dataset, the study assessed the influence of image resolution, noise, blur, and contrast variations on keypoint performance. The matching quality between stereo image pairs and across consecutive frames was also examined, with particular attention to drift—cumulative trajectory error—during motion estimation. The results show that while FAST and ORB detect the highest number of keypoints, GFTT offers the best balance between matching quality and processing time. KAZE provides high robustness but at the cost of computational load. The findings highlight the trade-offs between speed, accuracy, and resilience to environmental changes, offering practical guidance for selecting keypoint algorithms in real-time stereo visual odometry systems. The study concludes that GFTT is the most suitable method for trajectory estimation in dynamic, real-world conditions. Full article

In intensive broiler production, rapid environmental fluctuations can induce heat stress, adversely affecting flock welfare and productivity. Apparent temperature (AT), integrating temperature, humidity, and wind speed, provides a comprehensive thermal index, guiding predictive climate control. This study develops a multi-step AT forecasting model based on a hybrid SE-TCN–Transformer architecture enhanced with Kalman filtering. The temporal convolutional network with SE attention extracts short-term local trends, the Transformer captures long-range dependencies, and Kalman smoothing reduces prediction noise, collectively improving robustness and accuracy. The model was trained on multi-source time-series data from a commercial broiler house and evaluated for 5, 15, and 30 min horizons against LSTM, GRU, Autoformer, and Informer benchmarks. Results indicate that the proposed model achieves substantially lower prediction errors and higher determination coefficients. By combining multi-variable feature integration, local–global temporal modeling, and dynamic smoothing, the model offers a precise and reliable tool for intelligent ventilation control and heat stress management. These findings provide both scientific insight into multi-step thermal environment prediction and practical guidance for optimizing broiler welfare and production performance. Full article

Addressing the challenge of acquiring high-precision leader Unmanned Ground Vehicle (UGV) pose information in real time for communication-denied leader–follower formations, this study proposed Pillar-Bin, a 3D object detection algorithm based on the PointPillars framework. Pillar-Bin introduced an Interval Discretization Strategy (Bin) within the detection head, mapping critical target parameters (dimensions, center, heading angle) to predefined intervals for joint classification-residual regression optimization. This effectively suppresses environmental noise and enhances localization accuracy. Simulation results on the KITTI dataset demonstrate that the Pillar-Bin algorithm significantly outperforms PointPillars in detection accuracy. In the 3D detection mode, the mean Average Precision (mAP) increased by 2.95%, while in the bird’s eye view (BEV) detection mode, mAP was improved by 0.94%. With a processing rate of 48 frames per second (FPS), the proposed algorithm effectively enhanced detection accuracy while maintaining the high real-time performance of the baseline method. To evaluate Pillar-Bin’s real-vehicle performance, a leader UGV pose extraction scheme was designed. Real-vehicle experiments show absolute X/Y positioning errors below 5 cm and heading angle errors under 5° in Cartesian coordinates, with the pose extraction processing speed reaching 46 FPS. The proposed Pillar-Bin algorithm and its pose extraction scheme provide efficient and accurate leader pose information for formation control, demonstrating practical engineering utility. Full article

Interior Permanent Magnet Synchronous Motors (IPMSMs) are widely used in the electrification sector; however, reliance on rare-earth magnets imposes constraints stemming from supply instability and mining-related environmental impacts, raising sustainability concerns. To address these issues, this study investigates an IPMSM employing a consequent pole (CP) structure, in which one permanent magnet pole is replaced by iron. Because flux asymmetry in CP IPMSMs can cause torque ripple and associated vibration and noise, we propose an Intersect Consequent Pole (ICP) rotor geometry and evaluate it against a conventional IPMSM under identical stator conditions. The proposed ICP topology reduces permanent magnet usage and provides a rare-earth-reduced design alternative that addresses the vibration/noise trade-off, with a particular focus on electric power steering (EPS) applications. Electromagnetic characteristics and performance were analyzed using finite element analysis (FEA) and verified via FEA-based comparisons. Full article

Long Range Wide Area Network (LoRaWAN) has become an attractive option for maritime communication because it is low-cost, long-range, and energy-efficient. Yet its performance at sea is often limited by fading, interference, and the strict energy budgets of maritime Internet of Things (IoT) devices. This review, prepared in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, examines 23 peer-reviewed studies published between 2019 and 2025 that explore adaptive antenna solutions for LoRaWAN in marine environments. The work covered four main categories: switched-beam, phased array, reconfigurable, and Artificial Intelligence or Machine Learning (AI/ML)-enabled antennas. Results across studies show that adaptive approaches improve gain, beam agility, and signal reliability even under unstable conditions. Switched-beam antennas dominate the literature (45%), followed by phased arrays (30%), reconfigurable designs (20%), and AI/ML-enabled systems (5%). Unlike previous reviews, this study emphasizes maritime propagation, environmental resilience, and energy use. Despite encouraging results in signal-to-noise ratio (SNR), packet delivery, and coverage range, clear gaps remain in protocol-level integration, lightweight AI for constrained nodes, and large-scale trials at sea. Research on reconfigurable intelligent surfaces (RIS) in maritime environments remains limited. However, these technologies could play an important role in enhancing spectral efficiency, coverage, and the scalability of maritime IoT networks. Full article

The growing need for efficient and safe high-energy lithium-ion batteries (LIBs) in electric vehicles and grid storage necessitates advanced internal monitoring solutions. This work presents a comprehensive simulation model of a novel integrated optical sensor based on ethylene carbonate-filled photonic crystal fiber (EC-PCF). The proposed design synergistically combines a chirped fiber Bragg grating (FBG) and an extrinsic Fabry–Pérot interferometer (EFPI) on a multiplexed platform for the multifunctional sensing of refractive index (RI), temperature, strain, and pressure (via strain coupling) within LIBs. By matching the RI of the PCF cladding to the battery electrolyte using ethylene carbonate, the design maximizes light–matter interaction for exceptional RI sensitivity, while the cascaded EFPI enhances mechanical deformation detection beyond conventional FBG arrays. The simulation framework employs the Transfer Matrix Method with Gaussian apodization to model FBG reflectivity and the Airy formula for high-fidelity EFPI spectra, incorporating critical effects like stress-induced birefringence, Transverse Electric (TE)/Transverse Magnetic (TM) polarization modes, and wavelength dispersion across the 1540–1560 nm range. Robustness against fabrication variations and environmental noise is rigorously quantified through Monte Carlo simulations with Sobol sequences, predicting temperature sensitivities of ∼12 pm/°C, strain sensitivities of ∼1.10 pm/$\mathsf{\mu }$$\mathsf{\epsilon }$, and a remarkable RI sensitivity of ∼1200 nm/RIU. Validated against independent experimental data from instrumented battery cells, this model establishes a robust computational foundation for real-time battery monitoring and provides a critical design blueprint for future experimental realization and integration into advanced battery management systems. Full article

Environmental noise is a growing public health issue, particularly in dense urban environments and areas adjacent to transport infrastructure. Passive acoustic barriers have been a widely adopted solution, although their functional and aesthetic limitations have driven the development of alternatives, such as Sonic Crystal Acoustic Barriers. These structures can incorporate resonant elements, such as Helmholtz cavities, with the aim of enhancing attenuation in particular frequency bands. However, determining the precise dimensions of these resonators is a persistent challenge, given that classical models are based on ideal geometries and do not incorporate interaction with the periodic structural environment. This study sets a new frequency-dependent correction factor, obtained both numerically and experimentally, which allows the classical Helmholtz resonance expression to be adjusted to the case of resonators embedded in a Sonic Crystal (SC). The proposed model is validated through simulations and experimental measurements, showing a significant improvement in the prediction of the resonance frequency. The results obtained in this study allow us to move towards a more efficient and realistic design of passive acoustic barriers that are lightweight and adaptable to the urban environment. Full article

The integration of miniaturized magnetometers with Unmanned Aerial Vehicles (UAVs) has revolutionized magnetic surveying, offering flexible, high-resolution, and cost-effective solutions for geophysical applications also in remote areas. This study presents a comparative analysis of two configurations using UAV-borne scalar magnetometers through several surveys conducted in the Altopiano di Verteglia (Southern Italy), chosen as a test-site since buried pipes are present. The two systems differ significantly in sensor–platform arrangement, noise sensitivity, and flight configuration. Specifically, the first employs the MagNimbus magnetometer with two sensors rigidly attached on two masts at fixed distances, respectively, above and below the UAV, enabling the vertical gradient estimation while increasing noise due to proximity to the platform. The second involves the use of the MagArrow magnetometer suspended at 3 m below the UAV through non-rigid ropes, which benefits from minimal electromagnetic interference but suffers from oscillation-related instability. The retrieved magnetic anomaly maps effectively indicate the location and orientation of buried pipes within the studied area. Our comparative analysis emphasizes the trade-offs between the two systems: the MagNimbus-based configuration offers greater stability and operational efficiency, whereas the MagArrow-based one provides cleaner signals, which deteriorate with the vertical gradient computation. This work underscores the need to optimize UAV-magnetometer configurations based on environmental, operational, and survey-specific constraints to maximize data quality in drone-borne magnetic investigations. Full article

We investigate the quantum dynamics of coherence in the hyperfine structure of hydrogen atoms subjected to dephasing noise, modeled using the Lindblad master equation. The effective Hamiltonian describes the spin–spin interaction between the electron and proton, with dephasing introduced via Lindblad operators. Analytical solutions for the time-dependent density matrix are derived for various initial states, including separable, partially entangled, and maximally entangled configurations. Quantum coherence is quantified through the $l_1$-norm measures, while purity is evaluated to assess mixedness. Results demonstrate that coherence exhibits oscillatory decay modulated by the dephasing rate, with antiparallel spin states showing greater resilience against noise compared to parallel configurations. These findings highlight the interplay between coherent hyperfine dynamics and environmental dephasing, offering insights into preserving quantum resources in atomic systems for applications in quantum information science. Full article

Indoor environmental quality in offices, comprising thermal, acoustic, lighting, and air quality domains, is known to influence human comfort, yet the relative importance of their sub-factors—particularly in severe cold regions—remains unclear. This study addresses this gap by integrating objective (Criteria Importance Through Intercriteria Correlation, CRITIC) and subjective (Analytic Hierarchy Process, AHP) weighting methods, supported by field measurements and questionnaire surveys in open-plan offices in three provinces in northeastern China. Cluster analysis categorized acoustic sub-factors into outdoor traffic, outdoor entertainment, people conversation, burst sound, and people movement. Results show that temperature is the dominant thermal comfort driver (39.7% CRITIC; 45.5% AHP), exceeding air velocity and humidity, which had nearly equal influence. Indoor sound exerted greater impact than outdoor sound, with people conversation ranked highest among indoor noise sources, and burst sound and movement showing similar but slightly lower weights. Natural light outweighed artificial light in importance (54.2% CRITIC; 61.0% AHP), while air freshness and pollution were nearly equally influential. Compared to CRITIC, AHP produced more dispersed weights, reflecting subjective bias toward pronounced differences. These findings provide a quantitative basis for prioritizing environmental design interventions—such as controlling indoor conversational noise, optimizing natural lighting, and stabilizing temperature—to enhance comfort in offices in severe cold regions. Full article