Search Results (2,852)

The ongoing emergence of New Psychoactive Substances represents a growing threat to public health, as newly synthesized compounds continuously enter the illicit drug market, evading standard detection methods and challenging regulatory frameworks. Among New Psychoactive Substances, nitazenes are potent non-fentanyl opioids associated with severe cases of intoxication. This study evaluated the genotoxic potential of metodesnitazene and etodesnitazene in the human TK6 cell line. Cells were exposed to increasing concentrations of studied compounds, with and without S9 metabolic activation system. Preliminary assessments and micronuclei frequency analyses were performed by flow cytometry in at least three independent experiments. Metodesnitazene induced an increase in micronuclei frequency starting from 12.5 μM (p < 0.05), whereas etodesnitazene induced an effect only at 50 μM. Metabolic activation increases micronuclei formation at higher concentrations of metodesnitazene 25 μM, but did not substantially affect the response to etodesnitazene. Both compounds also induced intracellular reactive oxygen species production, measured through a chemiluminescent-based bioassay, suggesting oxidative stress as a potential contributing mechanism. These findings highlight the need for compound-specific toxicological profiling to better anticipate the acute and long-term risks associated with nitazene consumption. Full article

Lubricating oil monitoring provides continuous health information for early fault warning and maintenance decision-making in industrial gas turbines. However, real-world multi-sensor monitoring streams exhibit pronounced nonstationary thermodynamic drifts that often obscure subtle high-frequency residuals containing critical incipient degradation signatures. Prevailing data-driven monitoring models typically struggle to separate these macroscopic trends from stochastic wear-related fluctuations, and their restrictive distributional assumptions are often inadequate for the heteroscedastic and heavy-tailed nature of industrial residuals. To address these challenges, this study proposes ResAD-Net, a framework for early fault warning in nonstationary multi-sensor oil monitoring that combines trend–residual decoupling, trend-conditioned residual modeling, and residual-domain dependency learning. Specifically, a signal trend–residual decoupling strategy is adopted to separate slowly varying operational trends from stochastic residual fluctuations captured by the sensors, thereby exposing residual information that is more sensitive to incipient degradation. On this basis, a trend-conditioned diffusion model is introduced to characterize state-dependent, skewed residual distributions and generate residual sample ensembles for nonstationary monitoring. Meanwhile, a graph-based variational autoencoder is employed to learn latent intersensor dependency structures from the residual domain, providing diagnostic cues for temporal risk evolution analysis and sensor-level inspection. Experiments on a real-world industrial oil-monitoring record show that the proposed framework achieves an average F1-score of 0.985 with no observed false positives in the predefined pre-alarm reference interval of the finite test set. In addition to accurate anomaly detection, ResAD-Net captures early residual distributional shifts before clear macroscopic deviations emerge and provides diagnostic association cues for interpreting oil-monitoring changes around the system-level alarm. Full article

Greece and the eastern Mediterranean are among the regions that are most exposed to climate-driven natural hazards, with wildfires, floods, and extreme rainfall events consistently producing significant socioeconomic and environmental impacts. Although previous literature has addressed each hazard type individually, a systematic, comparative analysis of the atmospheric circulation regimes associated with all three hazard categories within a unified Lagrangian framework has not yet been conducted for Greece. In this study, a 96 h HYSPLIT back-trajectory analysis driven by ERA5 reanalysis data, combined with k-means clustering, is employed to characterise the air mass origins associated with extreme events in Greece from 2000 to 2020 at two atmospheric levels: 750 m and 3000 m above sea level. Wildfire events are predominantly linked to short-distance northeast airflow at 750 m, and are directly associated with the Etesian wind system and to a coherent northwest-west Mediterranean signal at 3000 m, reflecting the influence of the summer blocking anticyclone over Europe. Conversely, flood events are dominated by northerly flow at 750 m, driven by the eastern flank of Mediterranean depressions. These results indicate that flooding in Greece is primarily conditioned by surface cyclogenesis, regardless of the upper-level flow geometry. Extreme rainfall events exhibit the most complex structure, with a dominant upper-level cluster that describes a recurving trajectory consistent with cut-off low dynamics. Cross-hazard comparisons demonstrate that similar near-surface trajectory patterns may arise from different atmospheric drivers, underscoring the necessity of integrating Lagrangian trajectory classification with additional context, such as thermodynamic and seasonal, to enable robust multi-hazard attribution and enhance early warning capabilities in the eastern Mediterranean. Full article

To solve the low-precision prediction problem of noisy non-stationary goaf subsidence sequences, this study aims to establish a high-accuracy hybrid prediction model for mining surface deformation monitoring. The Global Navigation Satellite System (GNSS) monitoring data of surface subsidence in goaf areas exhibits non-stationary and noisy characteristics, which limits the accuracy of traditional prediction models. In this paper, a hybrid prediction model, namely the Improved Radial Movement Optimization–Variational Mode Decomposition–Gated Recurrent Unit (IRMO-VMD-GRU) model, is proposed. The IRMO algorithm is employed to globally optimize the key parameters of VMD, achieving adaptive and stable decomposition of the settlement sequences. The obtained Intrinsic Mode Function (IMF) sub-sequences are input into the GRU network for independent training and prediction, followed by superposition and reconstruction. The model is validated using the GNSS monitoring data from three monitoring points at a coal mine in Shaanxi Province, China. The results show that the proposed model outperforms the comparison models in all four evaluation indicators, namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), with all R² values exceeding 0.8. The model demonstrates superior fitting performance, correlation, and generalization ability, which provides important practical technical support for goaf subsidence early warning, geological disaster prevention and engineering safety management in mining areas. Full article

Hantaviruses are emerging rodent-borne pathogens that pose increasing global public health concerns due to their association with hemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS), both of which can result in substantial morbidity and mortality. Environmental change, climate variability, urbanization, and land-use transformation are increasingly recognized as critical drivers of hantavirus emergence and transmission. This review summarizes current evidence regarding hantavirus virology, epidemiology, pathogenesis, clinical manifestations, diagnostics, surveillance systems, prevention strategies, and One Health preparedness approaches. Emphasis is placed on the influence of climate change and ecological disruption on rodent reservoir dynamics and spillover risk, as well as major surveillance and diagnostic gaps in tropical and Caribbean regions where hantavirus circulation may be underrecognized. Advances in molecular diagnostics, genomic surveillance, vaccine development, monoclonal antibody therapies, and climate-based early warning systems are also discussed. Existing evidence highlights the importance of integrated One Health surveillance systems that combine human, animal, and environmental monitoring to improve early detection and outbreak preparedness. Strengthening laboratory capacity, ecological surveillance, regional collaboration, and public health infrastructure will be essential for reducing the global burden of hantavirus infections and improving preparedness for future zoonotic disease threats. Full article

Drought remains a persistent challenge affecting agricultural and pastoral livelihoods, particularly in dryland mixed crop–livestock systems. Water Monitoring and Early Warning Systems (WM-EWS) have increasingly been promoted as tools for delivering climate information services and supporting drought-related decision-making. However, empirical understanding of how users perceive and engage with such systems in pastoral contexts remains limited. This study explores stakeholder perceptions regarding the usefulness and operational relevance of a WM-EWS implemented in the Borana zone of Ethiopia. A mixed-methods approach was employed, combining survey data from 71 purposively selected mixed stakeholders with qualitative insights obtained through focus group discussions and key informant interviews. Findings indicate that respondents widely reported using WM-EWS information for water-related decision-making and perceived the system as useful in supporting drought preparedness and adaptive responses. Participants associated WM-EWS use with perceived changes in areas such as livestock management, access to water-related information, and coordination among stakeholders. Respondents also reported adopting multiple coping strategies, including early livestock sales, strategic herd mobility, and engagement with external support mechanisms. Respondents perceived fewer conflicts over water resources and greater engagement from humanitarian actors following WM-EWS implementation. Overall, the study provides exploratory insights into stakeholder experiences, perceived usefulness, and operational relevance of user-centered WM-EWS in drought-prone pastoral systems. The findings contribute to understanding how pastoral communities engage with climate information services while highlighting the need for future research using objective and longitudinal approaches to assess system effectiveness more rigorously. Full article

To address the problems of false negatives, false positives, and delayed response in traditional fire detection systems, this paper proposes a warehouse fire detection scheme based on multi-sensor information fusion. By constructing a ZigBee wireless sensor network and integrating temperature, CO concentration and smoke sensors, fire simulation data are collected in the warehouse. At the data processing level, an improved Grubbs criterion is innovatively adopted to eliminate outliers, and the median is used instead of the average to effectively suppress the same-side shielding effect. At the feature layer fusion stage, a BP neural network model optimized by the cosine decreasing inertia weight particle swarm optimization algorithm (CIW-PSO) is designed. By dynamically adjusting the learning factors (c₁, c₂) and inertia weight (w), the convergence speed and global optimization ability are significantly improved. At the decision-making level, a fuzzy logic reasoning mechanism is introduced to integrate multi-parameter membership functions, thereby reducing the probability of misjudgment. Field tests have verified that the system can achieve early fire warning in a 50 m × 100 m warehouse environment, with a false alarm rate reduced by 42% compared to a single sensor and a response time shortened by 35%, providing an efficient and reliable intelligent solution for warehouse fire safety. Full article

Arboviral diseases are emerging as important public health threats in Saudi Arabia, driven by rapid urbanization, climate variability, the expansion of Aedes aegypti populations, international travel, and large-scale religious mass gatherings. Dengue virus remains the most established arboviral infection in the Kingdom, particularly in the southwestern regions such as Jazan and the western urban centers of Makkah and Jeddah, where ecological and climatic conditions are conducive to sustained vector survival and transmission. This review synthesizes current evidence on the epidemiology, vector ecology, climatic determinants, diagnostics, and prevention strategies of arboviral diseases in Saudi Arabia. Particular attention is paid to the impacts of rising temperatures, changes in rainfall patterns, urban heat island effects, population mobility, and cross-border movement on vector expansion and disease emergence. The review also identifies gaps in surveillance, diagnostics, insecticide resistance monitoring, and integrated vector management programs. Emerging preparedness strategies include climate-informed early warning systems, Geographic Information System-based risk mapping, multiplex molecular diagnostics, genomic surveillance, and community-based vector control. The review emphasizes the importance of implementing a One Health approach that combines data on humans, the environment, entomology, and climate. Currently, sustained endemic transmission of chikungunya and Zika viruses has not been conclusively demonstrated in Saudi Arabia, but increased environmental suitability and connectivity with other areas highlight the need for proactive surveillance and preparedness. Full article

To support refined water quality evaluation and short-term early warning in multi-section river systems, this study developed three percentile-based composite indices: the Water Quality Index (WQI), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Entropy Weight Method (EWM). Monthly multi-parameter monitoring data from 2019 to 2025 were used, covering ten river sections (P1–P5, M1–M5). The three indices were compared in terms of statistical distribution, methodological consistency, and anomaly response. An integrated assessment–prediction framework was further established. Within this framework, a one-step prediction scheme was applied to evaluate four models: Long Short-Term Memory networks (LSTM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). The results show that WQI scores were generally high and fluctuated within a narrow range. A clear “ceiling effect” was observed in the moderate-to-high grade intervals. WQI also showed weak consistency with TOPSIS and EWM (r ≈ 0.29–0.32). In contrast, TOPSIS and EWM were more sensitive to water quality fluctuations and extreme risks, and were moderately correlated with each other (r ≈ 0.53). Using TOPSIS < 50 as the threshold, 49 severe anomalous events were identified. These events were mainly clustered in February–April 2020, April–July 2023, and June–September 2025, with sections P4, M1, and M2 acting as high-incidence sites. In several typical events, WQI values remained high, indicating that reliance on WQI alone may delay early warning. Prediction results further reveal that the choice of index strongly affects sequence predictability. Taking XGBoost as the reference, the median validation R² followed a stable gradient: WQI (0.807) > TOPSIS (0.723) > EWM (0.594). XGBoost yielded positive R² values across all indices and sections. It also achieved the most robust overall performance and the strongest cross-site, cross-index generalization capability. Full article

To address the challenges of quantifying expert experience, handling missing data, and managing class imbalance in evaluating the autonomous controllability of power equipment, this paper proposes a quantitative evaluation method that integrates expert prior rules with machine learning. First, building upon a five-dimensional evaluation indicator system, expert decision logic—including dimension-average threshold judgments, multi-dimensional weakness-based cumulative downgrading mechanisms, and key sub-item interaction rules—is formalized into a 15-dimensional rule prior feature vector, which is concatenated with the original 21-dimensional raw indicators to construct a RAW + RULE augmented feature space. Second, a KNN algorithm is employed for missing value imputation, while cost-sensitive learning combined with the SMOTE is adopted in a dual-path parallel scheme to address class imbalance. Six machine learning models are evaluated and compared via 30 repeated stratified cross-validations on a real-world dataset of 97 high-voltage bushing suppliers. Experimental results show that, on complete datasets, the RAW + RULE configuration with the Random Forest model achieves a mean test accuracy of 0.936 and a Kappa of 0.938, substantially outperforming the pure raw-feature model (accuracy 0.769, Kappa 0.766). Under weighted random missingness ranging from 10% to 50%, the RAW + RULE configuration demonstrates superior robustness, with ensemble tree models maintaining mean accuracies of 0.614–0.636 even at a 50% missing rate. This study provides a practically deployable technical solution and methodological reference for the quantitative assessment of autonomous controllability levels and early security warning in the power equipment supply chain. Full article

Climate hazards increasingly unfold as information crises alongside physical impacts, producing rapid shifts in what people search for and discuss online. This case study demonstrates how AI-supported analysis of online data can complement conventional disaster intelligence by providing a scalable social sensing layer for climate hazard resilience in Galicia. It integrates Google Trends as a proxy for changing public attention and information demand, and YouTube videos and comment threads to capture public sensemaking and resilience-relevant signals. Monthly Google Trends series were used for eight hazards, with floods showing the highest mean interest, followed by wildfires and heatwaves. For the three highest-salience hazards, the study analyzed YouTube comments using gpt-5-mini to extract sentiment, emotions, topics, institutional trust cues, collective efficacy cues, calls to action, impacts, vulnerable groups, and coping actions. The corpus included 184 heatwave comments, 20,427 wildfire comments, and 4882 flood comments. Across hazards, discourse is predominantly negative but differs in structure. Heatwave threads skew toward mockery and normalization, wildfire threads center on anger, governance and low institutional trust, and flood threads combine solidarity with demands for localized warnings and guidance. The study translates comment-level signals into traceable policy recommendations emphasizing actionable risk communication, early warning and response capacity, and trust-building practices. The study concludes with an operational pipeline concept for continuous monitoring and dashboard-based decision support, while emphasizing limitations related to Google Trends sampling and normalization, platform and API biases, and model-mediated uncertainty. Full article

This study presents an integrated framework for enhancing the safety and operational efficiency of robotic arms in laparoscopic surgery by addressing minimum distance estimation between multi-arm manipulators and the associated collision-aware warning. By combining analytical modeling, real time simulation, and machine learning, the framework offers a robust solution for ensuring safe robotic operations. An analytical model was developed to estimate the minimum distances between robotic arms based on their joint configurations, offering theoretical calculations that serve as both a validation tool and a benchmark. To complement this, a 3D simulation environment was created to model two 7 DOF Kinova robotic arms (Kinova Inc., Boisbriand, QC, Canada), generating a diverse dataset of configurations for distance estimation and collision warning. Using these insights, a deep residual neural network model was trained with joint configurations as inputs. On the held out validation set, the model achieves $R^2=0.940$, RMSE $=42.0$ mm, MAE $=28.7$ mm, and a near zero mean bias, demonstrating strong predictive accuracy and consistent generalization across the workspace. The framework is intended as an early collision warning layer, where a warning is triggered when the predicted inter-arm distance falls below a 0.2 m threshold, which corresponds to a surface to surface clearance of approximately 50 mm given the Kinova Gen3 (Kinova Inc., Boisbriand, QC, Canada) cross sectional radius. This work demonstrates the effectiveness of combining analytical modeling with machine learning to enhance the precision and reliability of multi-arm robotic systems. Full article

The accurate location of leakage points is a key part of underground gas prevention. To solve the problem of low positioning accuracy for gas extraction pipeline leakage, the gas extraction pipeline leakage experimental system was built, and the multi-source collaborative leakage localization method based on Stacking learning was proposed. The results showed that the Stacking–LSSVM–Elman–DBN (S-L-E-D) model with pressure–flow collaborative input achieved the best localization performance, with an accuracy of 0.932, Root Mean Square Error (RMSE) of 0.053, Mean Absolute Percentage Error (MAPE) of 0.082, Theil Inequality Coefficient (TIC) of 0.056, and a distance error below 1 m. Compared with a single-parameter input, the collaborative pressure–flow input improved the localization accuracy by more than 10%, while the RMSE and MAPE decreased by 39.0% and 37.4%, respectively. Under monitoring point fault conditions, the localization accuracies of monitoring points 1, 4, and 5 were 0.884, 0.891, and 0.881, respectively, while the dual-fault condition of monitoring points 1 and 4 still maintained an accuracy of 0.861. The study provides a feasible multi-source collaborative learning framework for leakage localization in gas extraction pipelines and offers a methodological reference for improving leakage monitoring and early warning. Full article

Rockfall from slope unstable rock masses is a typical geological hazard induced by brittle failure, with abrupt occurrence, limited macroscopic deformation before failure, and a short warning lead time. Conventional static analysis methods are useful for design-stage stability checks, but they cannot continuously capture structural-plane damage or update the stability state in real time. Dynamic evaluation based on structural dynamics links measurable parameters such as natural frequency, damping ratio, mode shape, vibration trajectory, wave velocity, and energy dissipation to the degradation of structural planes. This review synthesizes the dynamic behavior mechanism, parameter system, theoretical models, sensing technologies, and engineering applications for slope unstable rock masses. Different from previous reviews that mainly summarize rockfall monitoring or conventional slope stability analysis, this paper organizes the literature by failure mode, monitoring scale, model assumptions, field validation, uncertainty sources, and engineering applicability. The single-degree-of-freedom models for sliding-, toppling-, and falling-type rock masses, multi-block chain-collapse models, and data-physics dual-driven surrogate models are compared critically. Contact monitoring based on MEMS sensors, non-contact LDV monitoring, acoustic emission, microseismic monitoring, coda wave interferometry, and cloud-edge early-warning architectures are further reviewed. Key challenges include field-scale validation under heterogeneous and anisotropic geological conditions, environmental compensation, robust threshold calibration, and probabilistic linkage between dynamic indicators and failure probability. The review provides guidance for selecting dynamic evaluation models, designing field monitoring systems, and developing full-life-cycle digital-twin platforms for rockfall risk mitigation. Full article

With the continuous growth of urban building density and elevator deployment, the reliability, maintenance, and degradation risk warning of elevator systems have attracted increasing attention. Conventional monitoring methods based on fixed thresholds or rule logic are easy to implement, but they often fail to identify progressive degradation and are sensitive to complex operating conditions and measurement noise. This paper proposes a physics-informed neural network (PINN)-based method for elevator health monitoring and early warning. First, multi-sensor data are processed through time alignment and feature reconstruction, and a dual-path acceleration estimation method is introduced to improve the stability of dynamic state calculation. Second, a simplified traction elevator dynamic model considering load variation, motor drive, and mechanical resistance is embedded into PINN training to identify hidden parameters. Electrical and dynamic residual indicators are then constructed to characterise system condition from different physical perspectives. Finally, a time-accumulated risk model combining anomaly magnitude and persistence duration is developed to detect progressive degradation trends. Results show stable parameter convergence and effective condition assessment. The proposed approach detects degradation trends earlier than conventional threshold-based monitoring methods and reduces false alarms caused by transient disturbances. It provides an interpretable and practical solution for predictive maintenance and intelligent operation of elevator systems. Full article