Search Results (692)

To improve the accuracy of second-order cell-centered finite volume method in near-boundary regions for solving the two-dimensional shallow water equations, a numerical scheme with globally second-order accuracy was proposed. Having the primary objective to overcome the challenge of accuracy degradation in near-boundary regions and to develop a robust numerical framework combining high-order accuracy with strict conservation, the key research objectives had been as follows: Firstly, a physical variable reconstruction method combining a vertex-based nonlinear weighted reconstruction scheme and a monotonic upwind total variation diminishing scheme for conservation laws was proposed. While the overall computational efficiency was maintained, linear-exact reconstruction in near-boundary regions was achieved. The variable reconstruction in interior regions was integrated to achieve global second-order accuracy. Subsequently, a flux boundary condition treatment method based on uniform flow was proposed. Conservative allocation of hydraulic parameters was achieved, and flow stability in inflow regions was enhanced. Finally, a series of numerical test cases were provided to validate the performance of the proposed method in solving the shallow water equations in terms of high-order accuracy, exact conservation properties, and shock-capturing capabilities. The superiority of the method was further demonstrated under high-speed flow conditions. The high-precision numerical model developed in this study holds significant value for enhancing the predictive capability of simulations for natural disasters such as flood propagation and tsunami warning. Its robust boundary treatment methods also provide a reliable tool for simulating free-surface flows in complex environments, offering broad prospects for engineering applications. Full article

Forest fires in Yanyuan County, a typical dry-hot valley region, pose serious threats to ecological security and public safety. Conventional fire warning methods rely heavily on manual surveys, making them time-consuming, labor-intensive, and prone to missing the critical window for effective intervention. This paper presents a 3D visualization decision support system for fire prevention, developed on a Web-GIS platform using the Cesium engine. The system integrates multi-source data, including a 12.5 m DEM, remote sensing imagery, and real-time video streams. It employs a YOLO11 model for automated fire and smoke detection, achieving a precision of 82.4%. Compared to conventional 2D systems, the platform enhances emergency response speed by 37% while significantly improving spatial awareness and operational coordination. This cross-platform tool facilitates sustainable forest management through efficient resource allocation and real-time monitoring, offering a scalable and practical solution for fire prevention in complex terrains. Full article

Geohazards pose significant dangers to human safety, infrastructures, and the environment, highlighting the need for advanced monitoring techniques for early damage detection and structure management. The distributed optical fiber sensors (DFOS) are strain, temperature, and vibration monitoring tools characterized by minimal intrusiveness, accuracy, ease of deployment, and the ability to perform measurements with high spatial resolution. Although these sensors rely on well-established measurement techniques, available for over 40 years, their diffusion within monitoring and early warning systems is still limited, and there is a certain mistrust towards them. In this regard, based on several case studies, the implementation of DFOS for early warning of various geotechnical hazards, such as landslides, earthquakes and subsidence, is discussed, providing a comparative analysis of the typical advantages and limitations of the different systems. The results show that real-time monitoring systems based on well-established distributed fiber-optic sensing techniques are now mature enough to enable reliable and long-term geotechnical applications, identifying a market segment that is only minimally saturated by using other monitoring techniques. More challenging remains the application of the technique for vibration detection that still requires improved interrogation technologies and standardized practices before it can be used in large-scale, real-time early warning systems. Full article

Accurate prediction of structural displacements in hydropower stations is essential for the safety and long-term stability of large-scale water-related infrastructure. To address this challenge, this study proposes an AI-assisted monitoring framework that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction with Gated Recurrent Units (GRUs) for temporal sequence modeling. The framework leverages long-sequence prototype monitoring data, including reservoir level, temperature, and displacement, to capture complex spatiotemporal interactions between environmental conditions and dam behavior. A parameter optimization strategy is further incorporated to refine the model’s architecture and hyperparameters. Experimental evaluations on real-world hydropower station datasets demonstrate that the proposed CNN–GRU model outperforms conventional statistical and machine learning methods, achieving an average determination coefficient of R² = 0.9582 with substantially reduced prediction errors (RMSE = 4.1121, MAE = 3.1786, MAPE = 3.1061). Both qualitative and quantitative analyses confirm that CNN–GRU not only provides stable predictions across multiple monitoring points but also effectively captures sudden deformation fluctuations. These results underscore the potential of the proposed AI-assisted framework as a robust and reliable tool for intelligent monitoring, safety assessment, and early warning in large-scale hydropower facilities. Full article

Oak forests provide critical ecosystem services, but are being increasingly exposed to climate variability, drought, and insect outbreaks that threaten their long-term resilience. This study aims to integrate structural canopy indicators with climate-derived indices to detect early-warning signals of decline in temperate oak stands. We monitored eight Forest Management Units in western Romania between 2017 and 2021, combining field-based assessments of crown morphology, vitality traits, defoliation, and epicormic shoot frequency with hydroclimatic indices such as the Forest Aridity Index. Results revealed strong spatial and temporal variability: several stands showed advanced canopy deterioration characterized by increased defoliation, dead branches, and epicormic resprouting, while others maintained stable conditions, suggesting resilience and suitability as reference sites. Insect defoliators, particularly Geometridae, contributed additional stress, but generally at subcritical levels. By synthesizing these metrics into conceptual models and a risk scorecard, we identified the causal pathways linking climatic anomalies and biotic stressors to structural decline. The findings demonstrate that combining structural and climatic indicators offers a transferable framework for forest health monitoring, providing robust early-warning tools to guide adaptive silviculture and resilience-based management. Beyond the Romanian context, this integrative approach supports sustainability goals by strengthening conservation strategies for temperate forests under global change. Full article

Sudden cardiac death (SCD) is a leading cause of mortality worldwide, with arrhythmia serving as a major precursor. Early and accurate prediction of SCD using non-invasive electrocardiogram (ECG) signals remains a critical clinical challenge, particularly due to the inherent asymmetric and non-stationary characteristics of ECG signals, which complicate feature extraction and model generalization. In this study, we propose a novel SCD prediction framework based on hierarchical feature fusion, designed to capture both non-stationary and asymmetrical patterns in ECG data across six distinct time intervals preceding the onset of ventricular fibrillation (VF). First, linear features are extracted from ECG signals using waveform detection methods; nonlinear features are derived from RR interval sequences via second-order detrended fluctuation analysis (DFA2); and multi-scale deep learning features are captured using a Temporal Convolutional Network-based sequence-to-vector (TCN-Seq2vec) model. These multi-scale deep learning features, along with linear and nonlinear features, are then hierarchically fused. Finally, two fully connected layers are employed as a classifier to estimate the probability of SCD occurrence. The proposed method is evaluated under an inter-patient paradigm using the Sudden Cardiac Death Holter (SCDH) Database and the Normal Sinus Rhythm (NSR) Database. This method achieves average prediction accuracies of 97.48% and 98.8% for the 60 and 30 min periods preceding SCD, respectively. The findings suggest that integrating traditional and deep learning features effectively enhances the discriminability of abnormal samples, thereby improving SCD prediction accuracy. Ablation studies confirm that multi-feature fusion significantly improves performance compared to single-modality models, and validation on the Creighton University Ventricular Tachyarrhythmia Database (CUDB) demonstrates strong generalization capability. This approach offers a reliable, long-horizon early warning tool for clinical SCD risk assessment. Full article

Background/Objective: The growing volume and complexity of cases presented to emergency departments underline the urgent need for effective clinical-risk-management strategies. Increasing demands for quality and safety in healthcare highlight the importance of predictive tools in supporting timely and informed clinical decision-making. This study aims to evaluate the performance and usefulness of predictive models for managing the clinical risk of people who visit the emergency department. Methods: A systematic review was conducted, including primary observational studies involving people aged 18 and over, who were not pregnant, and who had visited the emergency department; the intervention was clinical-risk management in emergency departments; the comparison was of early warning scores; and the outcomes were predictive models. Searches were performed on 10 November 2024 across eight electronic databases without date restrictions, and studies published in English, Portuguese, and Spanish were included in this study. Risk of bias was assessed using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies as well as the Prediction Model Risk-of-Bias Assessment Tool. The results were synthesized narratively and are summarized in a table. Results: Four studies were included, each including between 4388 and 448,972 participants. The predictive models identified included the Older Persons' Emergency Risk Assessment score; a new situation awareness model; machine learning and deep learning models; and the Vital-Sign Scoring system. The main outcomes evaluated were in-hospital mortality and clinical deterioration. Conclusions: Despite the limited number of studies, our results indicate that predictive models have potential for managing the clinical risk of emergency department patients, with the risk-of-bias study indicating low concern. We conclude that integrating predictive models with artificial intelligence can improve clinical decision-making and patient safety. Full article

Early warning of structural damage from induced seismic events requires rapid and reliable ground motion forecasting. This study presents a novel real-time framework that couples a deep learning approach with structural fragility assessment to generate immediate damage alerts following the onset of seismic shaking. Long Short-Term Memory (LSTM) neural networks are employed to predict full S-wave accelerograms from initial P-wave inputs, trained and tested on accelerometric records from induced seismicity scenarios. The predicted S-wave motion is then used as input for a suite of fragility curves in real time to estimate the probability of structural damage for masonry buildings typical in rural areas of geothermal platforms. The proposed method captures both the temporal evolution of shaking and the structural response potential, offering critical seconds of lead time for automated decision-making systems. Results demonstrate high predictive accuracy of the LSTM model and effective early classification of structural risk. This integrated system provides a practical tool for early warning or rapid response in regions experiencing anthropogenic seismicity, such as those affected by geothermal operations. Full article

In the context of deep foundation pit excavation, the settlement of surrounding buildings is a critical indicator for safety assessment and early warning. Due to the non-stationary and nonlinear characteristics of settlement data, traditional prediction approaches often fail to achieve satisfactory accuracy. To address this challenge, this study proposes a hybrid prediction model integrating the Grey Wolf Optimizer (GWO), Variational Mode Decomposition (VMD), and Long Short-Term Memory (LSTM) networks, referred to as the GWO-VMD-LSTM model. In the proposed framework, GWO is employed to optimize the key hyperparameters of VMD as well as LSTM, thereby ensuring robust decomposition and prediction performance. Experimental results based on settlement monitoring data from four typical points around the Yongning Hospital foundation pit in Taizhou, China, demonstrate that the proposed model achieves superior predictive accuracy compared with five benchmark models. Specifically, the GWO-VMD-LSTM model attained an average coefficient of determination (R²) of 0.951, mean squared error (MSE) of 0.002, root mean square error (RMSE) of 0.033 mm, mean absolute error (MAE) of 0.031 mm, and mean absolute percentage error (MAPE) of 1.324%, outperforming all alternatives. For instance, compared with the VMD-LSTM model, the proposed method improved R² by 26.56% and reduced MAPE by 45.87%. These findings confirm that the GWO-VMD-LSTM model not only enhances the accuracy and generalization of settlement prediction but also provides a reliable and practical tool for real-time monitoring and risk assessment of buildings adjacent to deep foundation pits in soft soil regions. Full article

In July 2024, bluetongue virus serotype 3 (BTV-3) was first detected in southern Belgium, marking the onset of a major epidemic wave. This study documents, for the first time in Belgium, the ability of BTV-3 to cross the placental barrier in cattle, causing abortions and congenital central nervous system malformations. Abortion cases from January to December 2024 were monitored through the national abortion protocol, which mandates reporting and laboratory investigation (i.e., the year of emergence and the three previous years as the baseline data set). Among 5,751 reported abortions, 903 foetuses were tested by PCR, revealing widespread BTV-3 circulation. The first malformed PCR-positive foetus was recorded in mid-August, four weeks after a sharp increase in abortion rates. Lesions such as hydranencephaly were confirmed in PCR-positive foetuses, with a malformation rate of 32.24% in affected herds from weeks 36 to 52 (i.e., 22 times higher than in previous years). Gestational stage analysis indicated that congenital lesions were most frequent following infection between 70 and 130 days of gestation. Based on the observed gross lesions and the timing of abortion, it was deduced that the earliest maternal infections likely occurred in February–March 2024, implying low-level winter BTV-3 circulation before the official detection of the epidemic wave. These findings highlight the epidemiological value of systematic abortion monitoring as an early warning system tool and highlight the inadequacy of relying solely on clinical surveillance in adult ruminants. The abrupt emergence of BTV-3 across the territory without a gradual spatial spread underscores the need for anticipatory control strategies. Strategic, multivalent vaccination campaigns and enhanced abortion surveillance are critical to mitigate similar reproductive and economic losses in future bluetongue outbreaks. Full article

Viral metagenomic next-generation sequencing (vmNGS) has transformed our capacity for the untargeted detection and characterisation of (re)emerging zoonotic viruses, surpassing the limitations of traditional targeted diagnostics. In this review, we critically evaluate the current landscape of vmNGS, highlighting its integration within the One Health paradigm and its application to the surveillance and discovery of (re)emerging viruses at the human–animal–environment interface. We provide a detailed overview of vmNGS workflows including sample selection, nucleic acid extraction, host depletion, virus enrichment, sequencing platforms, and bioinformatic pipelines, all tailored to maximise sensitivity and specificity for diverse sample types. Through selected case studies, including SARS-CoV-2, mpox, Zika virus, and a novel henipavirus, we illustrate the impact of vmNGS in outbreak detection, genomic surveillance, molecular epidemiology, and the development of diagnostics and vaccines. The review further examines the relative strengths and limitations of vmNGS in both passive and active surveillance, addressing barriers such as cost, infrastructure requirements, and the need for interdisciplinary collaboration. By integrating molecular, ecological, and public health perspectives, vmNGS stands as a central tool for early warning, comprehensive monitoring, and informed intervention against (re)emerging viral threats, underscoring its critical role in global pandemic preparedness and zoonotic disease control. Full article

Taste and odor (T&O) compounds in freshwater are frequently produced by certain cyanobacteria; however, their occurrence remains difficult to predict. This study examined the temporal and spatial variations in the mibC gene, which encodes a critical enzyme in the biosynthesis of 2-methylisoborneol (2-MIB), by analyzing environmental DNA (eDNA) and RNA (eRNA) in the North Han River, Republic of Korea, from July 2019 to October 2021. Surface water was sampled at twelve sites and analyzed for mibC DNA copy number, RNA expression, cyanobacterial cell density, and 2-MIB concentration using quantitative PCR (qPCR), microscopy, and gas chromatography–mass spectrometry (GC–MS). The mibC gene was present throughout the year, exhibiting peaks from late summer to early winter; higher concentrations typically initiated upstream and subsequently moved downstream. RNA expression was elevated from summer to autumn, rapidly declined following heavy rainfall, and reliably preceded increases in 2-MIB concentrations by 2–4 weeks. RNA levels were strongly correlated with 2-MIB concentrations (r = 0.879, p < 0.001) but showed only a moderate association with Pseudanabaena cell density, whereas DNA demonstrated weaker correlations. More than 95% of total 2-MIB was dissolved, limiting the ability to directly estimate concentrations from eRNA data alone. The results indicate that eRNA monitoring is an effective early warning tool for T&O events. In addition, combining eDNA and eRNA analyses enables a more accurate evaluation of T&O-producing cyanobacteria, presenting practical benefits for proactive management of drinking water. Full article

Vortex-induced vibration (VIV) is a kind of abnormal vibration which needs to be automatically identified and warned in real time to guarantee the operational safety of a bridge. However, the existing VIV identification methods only focus on identification and have limitations in visualizing identification results, which causes difficulty for bridge governors in other fields to quickly confirm the identification results. This paper proposes an automatic VIV identification, warning, and visualization method. First, a recurrence plot is introduced to analyze the signal to extract the characteristics of the vibration signal in a time domain. Then, a feature index defined as recurrence cycle smoothness is proposed to quantify the stability of the vibration signal, based on which the VIV can be automatically identified. An automatic VIV identification and multi-level warning process is finally established based on the severity of the vibration amplitude. The proposed method is validated through a suspension bridge with serious VIVs. The result indicates that the proposed method can automatically identify the VIV correctly without any manual intervention and can visualize the identification results using a graph, providing a good tool to quickly confirm the VIV identification results. The multi-level warning can successfully warn the serious VIV and provide possible early warning for large amplitude VIV. Full article

Open-pit mining often induces geological hazards such as slope instability, surface subsidence, and ground fissures. To support sustainable mine operations and safety, high-resolution monitoring and mechanism-based interpretation are essential tools for early warning, risk management, and compliant reclamation. This study focuses on the Baorixile open-pit coal mine in Inner Mongolia, China, where 48 Sentinel-1 images acquired between 3 March 2017 and 23 April 2021 were processed using the Small-Baseline Subset and Distributed-Scatterer Interferometric Synthetic Aperture Radar (SBAS-DS-InSAR) technique to obtain dense and reliable time-series deformation. Furthermore, a Trend–Periodic–Residual Subspace-Constrained Regression (TPRSCR) method was developed to decompose the deformation signals into long-term trends, seasonal and annual components, and residual anomalies. By introducing Distributed-Scatterer (DS) phase optimization, the monitoring density in low-coherence regions increased from 1055 to 338,555 points (approximately 321-fold increase). Deformation measurements at common points showed high consistency ($R^2$ = 0.97, regression slope = 0.88; mean rate difference = −0.093 mm/yr, standard deviation = 3.28 mm/yr), confirming the reliability of the results. Two major deformation zones were identified: one linked to ground compaction caused by transportation activities, and the other associated with minor subsidence from pre-mining site preparation. In addition, the deformation field exhibits a superimposed pattern of persistent subsidence and pronounced seasonality. TPRSCR results indicate that long-term trend rates range from −14.03 to 14.22 mm/yr, with a maximum periodic amplitude of 40 mm. Compared with the Seasonal-Trend decomposition using LOESS (STL), TPRSCR effectively suppressed “periodic leakage into trend” and reduced RMSEs of total, trend, and periodic components by 48.96%, 93.33%, and 89.71%, respectively. Correlation analysis with meteorological data revealed that periodic deformation is strongly controlled by precipitation and temperature, with an approximately 34-day lag relative to the temperature cycle. The proposed “monitoring–decomposition–interpretation” framework turns InSAR-derived deformation into sustainability indicators that enhance deformation characterization and guide early warning, targeted upkeep, climate-aware drainage, and reclamation. These metrics reduce downtime and resource-intensive repairs and inform integrated risk management in open-pit mining. Full article

Cyber coercion, where legitimate users are forced to perform actions under duress, poses a serious insider threat to modern organizations, especially to critical infrastructure. Traditional security controls and monitoring tools struggle to distinguish coerced actions from normal user actions. In this paper, we propose a cyber coercion detection system that analyzes a user’s activity using an integrated large language model (LLM) to evaluate contextual cues from user commands or actions and current policies and procedures. If the LLM indicates coercion, behavioral methods, such as keystroke dynamics and mouse usage patterns, and physiological signals such as heart rate are analyzed to detect stress or anomalies indicative of duress. Experimental results show that the LLM-assisted multimodal approach shows potential in detecting coercive activity with and without detected coercive communication, where multimodal biometrics assist the confidence of the LLM in cases in which it does not detect coercive communication. The proposed system may add a critical detection capability against coercion-based cyber-attacks, providing early warning signals that could inform defensive responses before damage occurs. Full article