Search Results (407)

Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009–2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R² = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m⁻² d⁻¹, and Rn = 167.54 W m⁻². These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location systems (RTLS) with long-range (LoRa) wireless communication and ultra-wideband (UWB) positioning. The system comprises Arduino nano microcontrollers, organic light-emitting diode (OLED) displays, and piezo buzzers to detect and signal proximity breaches between workers and equipment. Using an action research approach, three pilot case studies were conducted in a simulated tunnel environment to test the system’s effectiveness in both static and dynamic risk scenarios. The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. These findings confirm the system’s capacity to enhance situational awareness and reduce reliance on manual safety protocols. The study contributes to the tunnel safety literature by demonstrating the feasibility of low-cost, real-time monitoring solutions that simultaneously track labour and machinery. The proposed RTLS framework offers practical value for safety managers and informs future research into automated safety systems in complex construction environments. Full article

Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution of land subsidence from 2018 to 2024. A total of 207 Sentinel-1 SAR images were first processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to generate high-resolution surface deformation time series. Subsequently, the seasonal-trend decomposition using the LOESS (STL) model was applied to extract annual cyclic deformation components from the InSAR-derived time series. To quantitatively assess the delayed response of land subsidence to groundwater level changes and subsurface strain evolution, time-lagged cross-correlation (TLCC) analysis was performed between surface deformation and both groundwater level data and distributed fiber-optic strain measurements within the 5–50 m depth interval. The strain data was collected using a borehole-based automated distributed fiber-optic sensing system. The results indicate that land subsidence is primarily concentrated in the urban core, with annual cyclic amplitudes ranging from 10 to 18 mm and peak values reaching 22 mm. The timing of surface rebound shows spatial variability, typically occurring in mid-February in residential areas and mid-May in agricultural zones. The analysis reveals that surface deformation lags behind groundwater fluctuations by approximately 2 to 3 months, depending on local hydrogeological conditions, while subsurface strain changes generally lead surface subsidence by about 3 months. These findings demonstrate the strong predictive potential of distributed fiber-optic sensing in capturing precursory deformation signals and underscore the importance of integrating InSAR, hydrological, and geotechnical data for advancing the understanding of subsidence mechanisms and improving monitoring and mitigation efforts. Full article

Background/Objectives: The combination drug sulfamethoxazole/trimethoprim (ST) is a broad-spectrum antibiotic used against various infections; however, it is associated with several serious adverse events. The ST package inserts contain warnings about these adverse events. However, warnings vary internationally, and specific measures to address ST-related adverse events are unclear. Therefore, we aimed to comprehensively evaluate ST-related adverse events using the Japanese Adverse Drug Event Report (JADER) database and analyze the onset time for each event. Methods: Adverse events due to ST were analyzed using the JADER database between April 2004 and June 2023. The reported odds ratio and 95% confidence interval (95% confidence interval [CI]) were calculated, with a signal detected if the 95% CI lower limit exceeded 1. The Weibull distribution was used to characterize the onset time of adverse events with detected signals. Results: The total number of cases in the JADER database during the study period was 862,952, and the number of adverse events involving ST as a suspected drug was 4203. Adverse events associated with ST include hyperkalemia, syndrome of inappropriate antidiuretic hormone secretion, hematopoietic cytopenia, acute renal failure, hypoglycemia, disseminated intravascular coagulation syndrome, hepatic disorder, and the Stevens–Johnson syndrome/toxic epidermal necrolysis. Conclusions: Weibull analysis indicated an early failure-type onset time for all adverse events, suggesting the need for intensive adverse event monitoring of ST, especially in the first month of use. These findings may support revising drug package inserts in Japan to better reflect the identified risks. Full article

Rapid identification of vessel height within the navigable space beneath bridges is crucial for ensuring bridge safety. To prevent bridge collisions caused by vessels exceeding their height limits, this article introduces a real-time warning framework for excessive vessel height based on video spatial transformation. The specific contributions include the following: (1) A spatial transformation-based method for locating vessel coordinates in the channel using buoys as control points, employing laser scanning to obtain their world coordinates from a broad channel range, and mapping the pixel coordinates of the buoys from side channel images to the world coordinates of the channel space, thus achieving pixel-level positioning of the vessel’s waterline intersection in the channel. (2) For video images, a key point recognition network for vessels based on attention mechanisms is developed to obtain pixel coordinates of the vessel’s waterline and top, and to capture the posture and position of multiple vessels in real time. (3) Analyzing the posture of vessels traveling in various directions within the channel, the method accounts for the pixel distance of spatial transformation control points and vessel height to determine vessel positioning coordinates, solve for the vessel’s height above water, and combine with real-time waterline height to enable over-height vessel collision warnings for downstream channel bridges. The method has been deployed in actual navigational scenarios beneath bridges, with the average error in vessel height estimation controlled within 10 cm and an error rate below 0.8%. The proposed approach enables real-time automatic estimation of vessel height in terms of computational speed, making it more suitable for practical engineering applications that demand both real-time performance and system stability. The system exhibits outstanding performance in terms of accuracy, stability, and engineering applicability, providing essential technical support for intelligent bridge safety management. Full article

The application of deep learning to seismic electric signal (SES) anomaly detection remains underexplored in geophysics. This study introduces the integration of a 1D convolutional neural network (1DCNN) with a self-attention mechanism to automate SES analysis in a station in a certain place in China. Utilizing physics-informed data augmentation, our framework adapts to real-world interference scenarios, including subway operations and tidal fluctuations. The model achieves an F1-score of 0.9797 on a 7-year dataset, demonstrating superior robustness and precision compared to traditional manual interpretation. This work establishes a practical deep learning solution for real-time geoelectric anomaly monitoring, offering a transformative tool for earthquake early warning systems. Full article

Aiming at the issues that the field mill type electric field sensor lacks an accurate and complete mathematical model, and its signal is weak and contains a large amount of harmonic noise, on the basis of establishing the mathematical model of the sensor’s induction electrode, the finite element method was adopted to analyze the influence laws of parameters such as the thickness of the shielding electrode and the distance between the induction electrode and the shielding electrode on the sensor sensitivity. On this basis, the above parameters were optimized. A signal processing circuit incorporating a pre-integral transformation circuit, a differential amplification circuit, and a bias circuit was investigated, and a completed mathematical model of the input and output of the field mill type electric field sensor was established. An improved harmonic detection method combining fast Fourier transform and back propagation neural network (FFT-BP) was proposed, the learning rate, momentum factor, and excitation function jointly participated in the adjustment of the network, and the iterative search range of the algorithm was limited by the threshold interval, further improving the accuracy and rapidity of the sensor measurement. Experimental results indicate that within the simulated electric field intensity range of 0–20 kV/m in the laboratory, the measurement resolution of this system can reach 18.7 V/m, and the measurement linearity is more than 99%. The designed system is capable of measuring the atmospheric electric field intensity in real time, providing necessary data support for lightning monitoring and early warning. Full article

The increasing deployment of wind energy systems, particularly offshore wind farms, necessitates advanced monitoring and maintenance strategies to ensure optimal performance and minimize downtime. Supervisory Control And Data Acquisition (SCADA) systems have become indispensable tools for monitoring the operational health of wind turbines, generating vast quantities of time series data from various sensors. Anomaly detection techniques applied to this data offer the potential to proactively identify deviations from normal behavior, providing early warning signals of potential component failures. Traditional model-based approaches for fault detection often struggle to capture the complexity and non-linear dynamics of wind turbine systems. This has led to a growing interest in data-driven methods, particularly those leveraging machine learning and deep learning, to address anomaly detection in wind energy applications. This study focuses on the development and application of a semi-supervised, multivariate anomaly detection model for horizontal axis wind turbines. The core of this study lies in Bidirectional Long Short-Term Memory (BI-LSTM) networks, specifically a BI-LSTM autoencoder architecture, to analyze time series data from a SCADA system and automatically detect anomalous behavior that could indicate potential component failures. Moreover, the approach is reinforced by the integration of the Isolation Forest algorithm, which operates in an unsupervised manner to further refine normal behavior by identifying and excluding additional anomalous points in the training set, beyond those already labeled by the data provider. The research utilizes a real-world dataset provided by EDP Renewables, encompassing two years of comprehensive SCADA records collected from a single offshore wind turbine operating in the Gulf of Guinea. Furthermore, the dataset contains the logs of failure events and recorded alarms triggered by the SCADA system across a wide range of subsystems. The paper proposes a multi-modal anomaly detection framework orchestrating an unsupervised module (i.e., decision tree method) with a supervised one (i.e., BI-LSTM AE). The results highlight the efficacy of the BI-LSTM autoencoder in accurately identifying anomalies within the SCADA data that exhibit strong temporal correlation with logged warnings and the actual failure events. The model’s performance is rigorously evaluated using standard machine learning metrics, including precision, recall, F1 Score, and accuracy, all of which demonstrate favorable results. Further analysis is conducted using Cumulative Sum (CUSUM) control charts to gain a deeper understanding of the identified anomalies’ behavior, particularly their persistence and timing leading up to the failures. Full article

Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage architecture consisting of watertight instrument housing, a communication circuit, and a buoy to realize high-capacity real-time data transmissions. The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. Through verification in a water tank and sea trials, the system successfully measured seismic wave signals. An improved ALE-LOFAR (Adaptive Line Enhancer–Low-Frequency Analysis) joint framework, combined with DEMON (Demodulation of Envelope Modulation) demodulation technology, was proposed to conduct the spectral feature analysis of ship seismic wave signals, yielding the low-frequency signal characteristics of vessels. This scheme provides an important method for the covert monitoring of shallow-sea targets, providing early warnings of illegal fishing and ensuring underwater security. Full article

The structural integrity of cement concrete pavements, paramount for ensuring traffic safety and operational efficiency, faces mounting challenges from the escalating burden of heavy-duty vehicular traffic. Precise characterisation of pavement dynamic responses under such conditions proves indispensable for implementing effective structural health monitoring and early warning system deployment. This investigation examines the triaxial dynamic response characteristics of cement concrete pavement subjected to low-speed, heavy-duty vehicular excitations, employing data acquired through in situ field measurements. A monitoring system incorporating embedded triaxial MEMS accelerometers was developed to capture vibration signals directly within the pavement structure. Raw data underwent preprocessing utilising a smoothing wavelet transform technique to attenuate noise, followed by empirical mode decomposition (EMD) and short-time energy (STE) analysis to scrutinise the time–frequency and energetic properties of triaxial vibration signals. The findings demonstrate that heavy, slow-moving vehicles generate substantial triaxial vibrations, with the vertical (Z-axis) response exhibiting the greatest amplitude and encompassing higher dominant frequency components compared to the horizontal (X and Y) axes. EMD successfully decomposed the complex signals into discrete intrinsic mode functions (IMFs), identifying high-frequency components (IMF1–IMF3) associated with transient vehicular impacts, mid-frequency components (IMF4–IMF6) presumably linked to structural and vehicle dynamics, and low-frequency components (IMF7–IMF9) representing system trends or ambient noise. The STE analysis of the selected IMFs elucidated the transient nature of axle loading, revealing pronounced, localised energy peaks. These findings furnish a comprehensive understanding of the dynamic behaviour of cement concrete pavements under heavy vehicle loads and establish a robust methodological framework for pavement performance assessment and refined axle load identification. Full article

Coal mine disaster safety monitoring often employs microseismic technology for its high sensitivity and real-time capability. However, nonlinear, non-stationary, and multi-scale signals limit traditional time series models (e.g., ARMA, ARIMA). This paper proposes a hybrid deep learning model—VMD-SE-CEEMDAN-BO-CNNGRU—integrating variational mode decomposition, sample entropy, CEEMDAN, Bayesian optimization, and a CNN-GRU architecture. Microseismic data from the 08 working face in D mine (Weibei mining area) were used to predict daily maximum energy, average energy, and frequency. The model achieved high predictive performance with R² values of 0.93, 0.89, and 0.88, significantly outperforming baseline models lacking modal decomposition. Comparative experiments verified the superiority of the VMD-first, SE-reconstruction, and CEEMDAN-second decomposition strategy, yielding up to 13% greater accuracy than reverse-order schemes. The model maintained R² above 0.80 on another dataset from the 03 working face in W mine (Binchang mining area), demonstrating robust generalization. Although performance declined during fault disturbances, accuracy for average energy and frequency rebounded post-disturbance, indicating strong adaptability. Overall, the VSCB-CNNGRU model enhances both accuracy and stability in microseismic prediction, supporting dynamic risk assessment and early warning in coal mining. Full article

This study reframes closure intention in small- and medium-sized enterprises (SMEs) as an ex ante diagnostic signal rather than a post-mortem symptom of failure. The survey evidence from 385 Ecuadorian SMEs was analyzed in two stages; confirmatory factor analysis validated the scales capturing environmental pessimism and personal pressures, and a structural equation model confirmed that both latent constructs directly heighten exit propensity. A binomial logistic regression model correctly classified 71% of the cases and explained 30% of variance. Five variables proved decisive: low-level liquidity (OR = 0.84), a high debt-to-equity ratio (1.41), weak profitability (0.14), negative environmental perceptions (1.72), and a shorter operating tenure (0.91); the sector and the firm size were non-significant. The combined CFA-SEM-logit sequence yields practical early warning thresholds—debt-to-equity ratio > 1.4, current ratio < 1.0, and ROA < 0.15—that lenders, advisers, and entrepreneurs can embed in dashboards or credit screens. Recognizing closure intention as a rational, strategic step challenges the stigma surrounding exit and links financial distress and the strategic exit theory. Policymakers can use the findings to pair debt relief and liquidity programs with cognitive bias training that helps owners interpret risk signals realistically. For scholars, the results highlight closure intention as a dynamic learning process, especially pertinent in emerging economies characterized by informality and institutional fragility. Full article

Rapid and accurate detection of primary waves (P-waves) using high-rate Global Navigation Satellite System (GNSS) data is essential for earthquake monitoring and tsunami early warning systems, where traditional seismic methods are less effective in noisy environments. We applied a wavelet-based method using a Mexican hat wavelet and dynamic threshold to thoroughly analyze the three-component displacement waveforms of the 2009 Padang, 2012 Simeulue, and 2018 Palu Indonesian earthquakes. Data from the Sumatran GPS Array and Indonesian Continuously Operating Reference Stations were analyzed to determine accurate displacements and P-waves. Validation with Indonesian geophysical agency seismic records indicated reliable detection of the horizontal component, with a time delay of less than 90 s, whereas the vertical component detection was inconsistent, owing to noise. Spectrogram analysis revealed P-wave energy in the pseudo-frequency range of 0.02–0.5 Hz and confirmed the method’s sensitivity to low-frequency signals. This approach illustrates the utility of GNSS data as a complement to seismic networks for the rapid characterization of earthquakes in complex tectonic regions. Improving the vertical component noise suppression might further help secure their utility in real-time early warning systems. Full article

The Flexible Combined Imager (FCI) instrument aboard the Meteosat Third Generation (MTG-I) geostationary satellite, launched in December 2022 and operational since September 2024, by providing shortwave infrared (SWIR), medium infrared (MIR) and thermal infrared (TIR) data, with an image refreshing time of 10 min and a spatial resolution ranging between 500 m in the high-resolution (HR) and 1–2 km in the normal-resolution (NR) mode, may represent a very promising instrument for monitoring thermal volcanic activity from space, also in operational contexts. In this work, we assess this potential by investigating the recent Mount Etna (Italy, Sicily) eruption of February–March 2025 through the analysis of daytime and night-time SWIR observations in the NR mode. The time series of a normalized hotspot index retrieved over Mt. Etna indicates that the effusive eruption started on 8 February at 13:40 UTC (14:40 LT), i.e., before information from independent sources. This observation is corroborated by the analysis of the MIR signal performed using an adapted Robust Satellite Technique (RST) approach, also revealing the occurrence of less intense thermal activity over the Mt. Etna area a few hours before (10.50 UTC) the possible start of lava effusion. By analyzing changes in total SWIR radiance (TSR), calculated starting from hot pixels detected using the preliminary NHI algorithm configuration tailored to FCI data, we inferred information about variations in thermal volcanic activity. The results show that the Mt. Etna eruption was particularly intense during 17–19 February, when the radiative power was estimated to be around 1–3 GW from other sensors. These outcomes, which are consistent with Multispectral Instrument (MSI) and Operational Land Imager (OLI) observations at a higher spatial resolution, providing accurate information about areas inundated by the lava, demonstrate that the FCI may provide a relevant contribution to the near-real-time monitoring of Mt. Etna activity. The usage of FCI data, in the HR mode, may further improve the timely identification of high-temperature features in the framework of early warning contexts, devoted to mitigating the social, environmental and economic impacts of effusive eruptions, especially over less monitored volcanic areas. Full article