Search Results (917)

Unmanned Aerial Vehicle (UAV)-borne laser wavefront sensing technology holds significant application prospects for high-precision vibration detection in the field; however, the acquired signals are highly susceptible to complex, nonlinear environmental noise interference. Existing high-precision deep learning denoising models typically rely on massive computational resources, making them difficult to deploy on resource-constrained edge devices. Consequently, practical engineering exploration is often confined to an inefficient “blind sampling followed by offline processing” mode, incurring a high risk of data invalidation. To explore solutions for real-time quality control at the edge, this paper proposes a lightweight time-frequency state space model (SWIFT-Mamba), aiming to provide an efficient algorithmic foundation and an engineering proof-of-concept for portable devices moving toward in situ verification. Through rigorous evaluation on over 60,000 laboratory-measured and controlled synthetic wavefront vibration data samples, SWIFT-Mamba achieves an average Signal-to-Noise Ratio (SNR) gain of 19.64 dB and a Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) gain of 16.10 dB, with an extremely low computational overhead requiring only 0.066 M parameters and 0.147 GFLOPs. Experimental results demonstrate that while significantly reducing computational costs, the proposed model can effectively extract the physical manifold of the signal and precisely preserve high-frequency phase features. Full article

The rapid global expansion of data center infrastructure has prompted substantial clean technology research on energy, water, and carbon impacts, while the acoustic health dimension of these facilities remains virtually unstudied. Existing occupational and environmental noise assessments rely on A-weighted (dBA) metrics, which apply more than 26 decibels (dB) of attenuation at 63 hertz (Hz) and exceed 50 dB at infrasound frequencies, sharply discounting their sensitivity to infrasound and low-frequency noise (ILFN) generated by data center cooling fans, heating, ventilation, and air conditioning (HVAC) systems, backup generators, and power transformers. This narrative review synthesizes evidence from established ILFN health research alongside the emerging data center acoustics literature, identifying a consequential gap: no published study has measured the ILFN spectrum of an operational data center, nor examined health outcomes in workers or surrounding communities with respect to sub-audible acoustic exposure. Evidence from wind turbine, industrial, and laboratory contexts documents non-auditory ILFN pathways, including sleep disturbance, cardiovascular stress responses, cognitive impairment, and audiovestibular symptoms—effects that operate below the auditory threshold and are substantially undercounted by standard dBA monitoring. A prioritized research agenda is proposed, beginning with G-weighted and flat-response ILFN characterization of operational data centers across at least 1–200 Hz—a prerequisite for evidence-based acoustic design standards and health-protective infrastructure development consistent with clean technology principles. Full article

Distributed fiber optic sensing (DFOS) has emerged as a transformative technology for structural health monitoring (SHM) of railway infrastructure, offering continuous, high-resolution measurements along extended optical fiber lengths, capabilities that conventional point sensors such as strain gauges and accelerometers cannot match. This review critically examines DFOS technology and its railway SHM applications, covering system components, interrogator units, optical fiber cables, and data acquisition systems, alongside the three principal scattering mechanisms: Rayleigh, Brillouin, and Raman, each offering distinct trade-offs in spatial resolution, sensing range, and measurand sensitivity. Field applications across track and sleeper monitoring, bridge health evaluation, tunnel lining assessment, and embankment stability are reviewed and critically compared. The integration of artificial intelligence (AI) and machine learning (ML) with DFOS data streams is discussed, demonstrating detection accuracy exceeding 97% in recent studies. Its main application rail embankment monitoring is discussed. Key challenges are identified, including high interrogator costs, large data volumes, installation complexity in retrofit scenarios, and environmental noise under operational train speeds. Future research priorities include lower-cost interrogation hardware, automated signal processing pipelines, digital twin integration, and standardized performance frameworks to accelerate large-scale adoption across railway networks worldwide. Full article

The Central Sterile Supply Department (CSSD) functions as the critical control point for hospital infection prevention, yet its equipment-intensive environment generates occupational noise exposure that has been systematically overlooked in both the healthcare quality and occupational health literature. We conducted a focused narrative review across PubMed, Web of Science, and Scopus (up to March 2026) to examine the prevalence, psychophysiological mechanisms, and management of noise in CSSD settings. Four peer-reviewed studies investigating CSSD noise exposure were identified, conducted in Chinese and Brazilian hospitals. The evidence indicates that air guns and pressure steam sterilizers constitute the dominant noise sources, with self-reported exposure associated with psychological symptoms and sleep disturbance in approximately one-quarter of staff. Notably, health concerns may mediate a substantial proportion of the noise–psychology relationship, suggesting a cognitive–affective pathway that may be as theoretically significant as direct physiological damage, although this cross-sectional finding requires longitudinal confirmation. No study has empirically linked CSSD noise to sterilization failures, device damage, or patient-level outcomes. Drawing on this nascent evidence base and adjacent occupational noise theory, we advance three testable propositions: that CSSD noise may constitute a systemic quality risk factor, that subjective cognitive–affective appraisal plays a primary mediating role, and that effective management requires multi-level integration across engineering, administrative, and psychological domains. This review reframes CSSD noise from an ergonomic nuisance to an embedded environmental stressor and charts a structured research agenda for objective measurement, mechanism validation, and intervention evaluation. Full article

Long-term dam safety assessment relies on continuous monitoring data from multiple spatially distributed measurement points. However, monitoring data are affected by measurement noise, environmental disturbances, and model errors, while the spatial correlation among monitoring points is often ignored, leading to biased uncertainty estimation and unreliable prediction intervals. To address these issues, this study proposes a spatial-correlation-aware distribution-adaptive interval prediction method for dam monitoring via two-level uncertainty fusion. Measurement random noise is first separated using a filtering strategy, and its uncertainty is updated by incorporating the spatial correlation among multiple monitoring points. A regression model is then established based on the filtered monitoring data, and model prediction uncertainty is quantified from the residual distribution. The two uncertainty components are further integrated to construct distribution-adaptive asymmetric prediction intervals. The proposed method is verified using deformation monitoring data from the PB high core rockfill dam. The results show that the proposed method achieves an average PICP of 0.9898 on the training set, close to the target coverage level of 0.99, while reducing the average NMPIW by 16.9% and 13.4% compared with the symmetric interval method and the traditional 3σ method, respectively. On the validation set, the average NMPIW is further reduced by 18.4% and 26.5%, demonstrating that the proposed method can provide more compact and informative prediction intervals for refined dam safety monitoring. Full article

A cooperative guidance law based on the experience-guided multi-agent proximal policy optimization (E-MAPPO) algorithm is proposed for multiple unmanned aerial vehicles (UAVs) to track dynamic points of interest in civilian applications, such as collaborative search and rescue and environmental monitoring. In multi-UAV cooperative tracking, accurate arrival-time coordination is important for improving collaborative task execution, but it remains challenging because of continuous action spaces, target maneuvering, uncertain time-to-go estimation, and inefficient exploration in multi-agent reinforcement learning. Specifically, a multi-UAV cooperative guidance environment is formulated, and the problem is modeled as a Markov decision process. To address the challenges of large action spaces and poor convergence in multi-agent reinforcement learning, an experience-guided MAPPO framework is introduced to enhance training efficiency and policy stability. Different from standard MAPPO, the proposed E-MAPPO introduces proportional-navigation-guided experience only during the early training stage to guide exploration, while the final policy is still optimized through the MAPPO objective. Subsequently, a composite reward function is designed by integrating distance-based heuristic terms with auxiliary guidance signals, thereby improving exploration efficiency and facilitating coordinated rendezvous and tracking of dynamic references. Comparative simulations with cooperative proportional navigation guidance (CPNG), sliding mode control (SMC), and standard MAPPO are conducted under different target motion scenarios. The results show that E-MAPPO reduces the average convergence step by 17.07% compared with MAPPO. In the straight-moving target scenario, E-MAPPO reduces the cooperative time error by 55.10% compared with CPNG and by 8.33% compared with MAPPO. In the S-type maneuvering target scenario, E-MAPPO reduces the cooperative time error by 55.81% compared with CPNG and by 9.52% compared with MAPPO. Monte Carlo experiments further verify its effectiveness and robustness. Additional robustness tests under Gaussian measurement noise, observation bias, and communication delay show that the proposed method maintains acceptable tracking accuracy and cooperative timing performance under different uncertainty conditions. In addition, the results indicate that the proposed method generalizes well to different types of maneuvering targets. Full article

In semiconductor and optoelectronics manufacturing, the reliability of turbo molecular pumps (TMPs) is vital for maintaining vacuum integrity and ensuring product yield. However, acoustic monitoring in cleanrooms faces severe challenges due to ambient noise levels routinely exceeding 80 dBA and the scarcity of labeled anomaly data. This study proposes an unsupervised acoustic anomaly detection and localization system to address these issues. The performance of the proposed framework is evaluated using an acoustic dataset collected from operational turbopumps in an industrial semiconductor cleanroom environment, encompassing both normal operations and naturally occurring failure conditions. We introduce a Convolutional Autoencoder (CAE) based on ResNet-18, integrated with a Convolutional Block Attention Module (CBAM) to adaptively suppress high-decibel environmental noise. To enhance sensitivity to structural spectral defects, a hybrid loss function combining Mean Squared Error (MSE) and structural similarity index measure (SSIM) is implemented. Experimental results, supported by rigorous hyperparameter sensitivity analysis, demonstrate that the proposed model achieves an outstanding AUC of 0.9535 and a fault recall of 99.06% under a validation-calibrated threshold, significantly outperforming standard U-Net architectures. Furthermore, the system generates anomaly heatmaps for precise time–frequency localization, enabling explainable diagnostics. With a model inference throughput of 330.28 FPS and an end-to-end processing rate of ≈73× in real time, the proposed framework provides an efficient and robust solution for real-time predictive maintenance in noisy industrial settings. Full article

Urban environments concentrate spatially distributed stressors, including heat, noise, pollution, and biodiversity loss, that are increasingly recognized as determinants of population mental health. However, current approaches rarely integrate environmental structure, lived exposure, and climate-related emotional responses within a unified spatial framework applicable to public health. This article introduces Eco-Affective Health Mapping (EAHM) as a conceptual, spatially explicit framework, grounded in the recently formalized Eco-Affective Health theoretical model, for understanding how environmental conditions may shape climate-related emotional responses, including eco-anxiety, solastalgia, and ecological grief, across urban socio-ecological systems. Drawing on evidence from spatial epidemiology, landscape ecology, environmental mental health, and digital phenotyping, I argue that affective responses to environmental stressors are not randomly distributed but are hypothesized to exhibit spatial clustering in relation to environmental exposures, landscape configuration, and mobility-based interactions. I propose the Eco-Affective Health Mapping (EAHM) framework, which integrates four spatial layers: environmental exposures, landscape configuration, person–place interaction, and affective indicators, together with a companion composite metric, the Eco-Affective Load Index (EALI), for which I provide a formal multi-domain specification and a purely illustrative, non-empirical worked example. EAHM is presented here as a theoretical and methodological proposal rather than as a validated instrument: no primary environmental, mobility, or affective data were collected or analyzed for this article, and the framework’s constituent relationships require prospective empirical testing, for which I outline a companion measurement strategy grounded in the Eco-Affective Health Assessment Protocol (EAHAP). By conceptualizing climate-related emotional responses as candidate measurable public health signals, EAHM is intended to support a future shift toward prevention-oriented, population-level mental health strategies aligned with planetary health and sustainable development agendas. Full article

Motorcycle noise along leisure routes constitutes a distinct environmental health burden poorly captured by standard road traffic noise indicators. This study derives source-specific exposure–response functions and examines non-acoustic predictors of residential motorcycle noise annoyance. A mixed-methods socio-acoustic design was applied in five study areas in Baden-Wuerttemberg, Germany. A community survey (N = 493) assessed long-term annoyance (12-month recall); a smartphone-based experience-sampling study (MotoApp; N = 213; ten days in summer 2022) collected hourly ratings. Acoustic measurements provided vehicle-specific L_Aeq,1h, L_AFmax,1h, and N₆₀; exposure–response functions were estimated using logistic regression and generalised estimating equations. In the long-term survey, 46.5% were highly annoyed by motorcycle noise, compared with 18.4% for passenger cars. Motorcycle exposure–response curves were markedly shifted; the 25–highly-annoyed threshold was reached approximately 16 dB lower in L_Aeq,1h than for passenger cars on weekends. Negative attitudes towards motorcycle riders, low coping capacity, and noise sensitivity were significant independent predictors. Source-specific assessment is necessary for motorcycle leisure routes. The 25–highly-annoyed criterion maps to approximately 52 dB L_Aeq,1h and 78 dB L_AFmax,1h on weekends, and to 25 N₆₀ events per hour—substantially below current road traffic guideline values—providing actionable thresholds for noise action planning. Full article

Conventional DC control and protection (C&P) systems rely on single-modal electrical data and are susceptible to false tripping and missed detection under complex operating conditions or novel fault types. In high-voltage direct current (HVDC) transmission, multi-modal data encompass time-series electrical quantities, unstructured transient waveforms, condition monitoring measurements, and environmental variables, each reflecting the system operating state from a distinct dimension with significant inter-modal complementarity. Nevertheless, fusing these heterogeneous modalities poses three key challenges: feature conflicts arising from data heterogeneity, difficulty embedding domain-specific C&P knowledge into data-driven models, and degraded model robustness under data noise and missing data conditions. This paper proposes a three-layer hierarchical fusion model that integrates multimodal data preprocessing, a C&P phase-aware rule-guided feature weighting strategy, and a dual-path decision mechanism. Experiments conducted on a dataset covering normal operation, typical fault, and complex operating scenarios demonstrate that the proposed model achieves an overall fault identification accuracy of 96.2%, which is 13.9 and 6.5 percentage points higher than a single-modal baseline and a generic multimodal model, respectively. The average decision latency of 7.2 ms satisfies the millisecond-level real-time requirement of industrial C&P systems, confirming the engineering applicability of the proposed approach. Full article

Respiratory sound monitoring is a promising non-invasive tool for precision pig farming, but practical evidence from calibrated room-level deployment under degraded air-quality conditions remains limited. This study reports a 28-day exploratory room-level case study in which 52 growing pigs were housed in two rooms: one standard-ventilation room and one ventilation-restricted room, and monitored with one microphone per room emphasizing mixed room-level soundscape monitoring rather than individual pig cough counts or replicated treatment inference. Because the design lacked independent room-level replication, all room contrasts and p-values were interpreted as exploratory descriptive screening summaries rather than causal treatment effects. Airflow verification, playback calibration at multiple pen positions, and background-noise spectral analysis were performed to address measurement bias. Signal inspection showed that biologically relevant vocal energy was retained after 16 kHz resampling, while class imbalance was handled by inverse-frequency weighting and macro-F1-based model selection. The Audio Spectrogram Transformer (AST) pipeline was subjected to five-fold group-blocked cross-validation, and temporal validation. The model achieved a test macro-F1 of 0.937, five-fold macro-F1 of 0.928 ± 0.019, and three-day deployment validation macro-F1 of 0.914. In this two-room dataset, the ventilation-restricted room displayed higher room-level cough-like detections, aggressive vocalizations, normal vocalizations, lower silence, reduced growth, and poorer air quality. Cough-like detections showed recurring clock-time clustering, with the most sustained elevation during 19:00–22:00 and a smaller peak around 10:00 with the highest occurrences at 20.00 (2.84 room-level cough-like detections standardized to group size). Audio-only early-warning analysis flagged deteriorated air-quality windows with AUROC = 0.91 and AUPRC = 0.88 and provided a median 34 min lead time before environmental threshold exceedance, highlighting practical utility as an early inspection cue for farmers before air-quality deterioration becomes more pronounced. Cough-like events descriptively co-varied positively with NH₃, temperature, and CO₂. Overall, calibrated AST-based monitoring can summarize group-level acoustic changes associated with degraded room environments, while multi-room and multi-farm replication remains necessary for causal inference and generalization. Full article

Urban noise pollution is considered to be a significant challenge to neighborhood livability, particularly in cities with limited noise governance. This has resulted in raising the environmental noise as a critical health and livability concern. Existing approaches rely on decibel thresholds that fail to capture the spatial, social and perceptual complexity of noise conditions at the neighborhood scale. This study offers a multi-criteria soundscape framework integrating acoustic measurements and urban parameters (urban design, landscape, regulations and perception). The aim is to support the optimal location for noise planning. The framework was applied as a pilot study across five residential neighborhoods in Tripoli, Lebanon. The OpeNoise mobile application was used across four temporal sessions. Recordings were combined with qualitative resident interviews. Average Equivalent Continuous Sound level LAeq(t) values ranged from 60.3 to 76.8 dBA. While all neighborhoods exceeded the World Health Organization WHO guidelines, acoustic severity alone did not determine the best case for intervention. The neighborhood with the highest measured noise did not achieve the highest priority score. This demonstrates that decibel planning is insufficient. The alignment of acoustic severity, feasibility, and community demand is more effective for intervention. The framework’s criteria are based on observable urban indicators, suggesting applicability to similar urban contexts, further pending validation. Full article

Marine monitoring records collected from buoys and nearshore sensors are often affected by missing values, abrupt spikes, and short-term fluctuations. These errors are difficult to remove with a single detection or interpolation rule, especially when local anomalies and global outliers occur in the same sequence. This study develops a Multi-Strategy Fusion Architecture (MSFA) for cleaning marine environmental time-series data. In MSFA, DBSCAN is not applied directly to the raw observations; instead, the time index and measurement value are first normalized into a common feature space, where local density anomalies can be detected more consistently. IQR screening is then used to identify global extreme values. After abnormal positions are marked, the repair result is estimated from two complementary sources: linear interpolation, which follows local temporal change, and a moving average based only on neighboring valid observations, which reduces random noise. Their contributions are adjusted according to local reliability rather than fixed manually. Because initial repair may still leave small residual errors, we further use a Combined Residual Metric (CRM) with a median/MAD-based threshold to recheck the repaired sequence and update the abnormal-position set when necessary. Experiments on the 2020 Dongying offshore buoy dataset and a self-collected nearshore dataset show that MSFA achieves AUROC/AUPRC/NRMSE values of 0.896/0.855/0.066 and 0.986/0.915/0.0653, respectively. Compared with DBSCAN+LOF, DBSCAN+Transformer, and IQR+Sigmoid, MSFA improves AUROC and AUPRC by about 12–25% on average and reduces NRMSE by more than 40%. These results indicate that the proposed method can improve the usability of noisy and incomplete marine monitoring data while keeping the cleaning process interpretable. Full article

Traditional interferometric optical hydrophones typically employ narrow-linewidth lasers. Although their long coherence length facilitates interference, it also makes the system highly susceptible to spurious interference and phase noise. Low-coherence sources can effectively suppress such parasitic noise; however, their extremely short coherence length imposes stringent stability requirements on the optical path difference (OPD). In underwater environments, ambient disturbances easily cause OPD drift beyond the coherence length, resulting in loss of interference. To address this issue, we propose and experimentally demonstrate a closed-loop hydrophone system that combines low-coherence interferometry with dynamic OPD compensation using a programmable fiber delay line (FDL). Numerical simulations and experiments confirm that the closed-loop compensation algorithm maintains the OPD within the coherence length under environmental perturbations, thereby ensuring long-term stable interference. The prototype achieves a sensitivity of −117.82 dB re rad/µPa at 171 Hz, a noise-equivalent sound pressure spectral density of 8.69 dB re 1 µPa/√Hz, a measured peak phase amplitude of 130 rad, a minimum detectable phase of 14 µrad, and a corresponding logarithmic dynamic range of 139 dB. These results verify the feasibility of integrating low-coherence interferometry with active OPD compensation for low-frequency underwater acoustic detection, under laboratory quasi-static environmental conditions, the proposed closed-loop compensation maintained stable interference throughout a continuous 1.5-h experiment, presenting a differentiated and worthy-of-further-engineering-exploration technical path for high-sensitivity and low-noise optical hydrophones. Full article

Urban environments expose individuals to multiple stressors, including air pollution and noise, which significantly impact health by causing cardiovascular and respiratory diseases and sleep disruption. Effective monitoring of these stressors through intelligent sensing technologies can support the mitigation of long-term deterioration in both physical and mental health. In this context, this pilot study presents a multimodal approach that integrates environmental sensing and physiological monitoring to assess stress responses of the human body to urban conditions. Indoor and outdoor air quality were measured using smart sensor nodes that captured particulate matter (PM₁, PM_2.5, PM₄, PM₁₀), air temperature and relative humidity. The physiological response to urban noise exposure was evaluated using electrodermal activity (EDA) and heart rate variability (HRV) acquired via a wearable biomedical device, while sound pressure levels (dBA) were measured using a professional sound level meter. Preliminary results indicate that indoor particulate matter concentrations greatly exceeded outdoor levels, despite outdoor sensors being deployed in a high-traffic urban environment. Physiological analysis revealed increased tonic electrodermal activity under noise exposure, indicating increased sympathetic activation. Complementary HRV analysis showed elevated heart rate (HR), reduced parasympathetic activity, and increased sympathetic dominance under high-noise conditions, confirming a measurable physiological stress response to urban environmental exposure. Full article