Search Results (1,065)

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article

The increasing prevalence of insecticide resistance in Aedes aegypti threatens the effectiveness of chemical vector control and highlights the need for alternative approaches targeting mosquito behavior. This study evaluated the oviposition deterrent and larvicidal activity of Salvia munzii essential oil against insecticide-susceptible (New Orleans; NO) and insecticide-resistant (Escobedo) Ae. aegypti strains. The essential oil, dominated by camphor (29.6%), 1,8-cineole (20.8%), and limonene (16.7%), was assessed through laboratory and semi-field bioassays. Larvicidal activity yielded LC₅₀ values of 184.38 µg mL⁻¹ for the susceptible strain and 305.04 µg mL⁻¹ for the resistant strain, with a resistance ratio of 1.65, indicating susceptibility. Oviposition deterrence was quantified using the Oviposition Activity Index (OAI), and median repellent concentrations (RC₅₀) were estimated. Under laboratory conditions, RC₅₀ values were 1.65 µg mL⁻¹ for the NO strain and 1.73 µg mL⁻¹ for the Escobedo strain. Under semi-field conditions, the RC₅₀ for the Escobedo strain decreased to 0.62 µg mL⁻¹. Deterrent activity increased with concentration and persisted for up to 40 days, particularly at higher doses. These results demonstrate that S. munzii essential oil exhibits both larvicidal and oviposition deterrent activity against Ae. aegypti, including a pyrethroid-resistant population, under laboratory and semi-field conditions. The findings support further evaluation of S. munzii essential oil as a potential complementary tool for integrated vector management strategies. Full article

Bench slopes in open-pit mines are highly susceptible to progressive deformation and instability due to the coupled effects of excavation disturbance, rock mass weathering, and extreme rainfall, posing significant challenges to rapid risk assessment and engineering decision-making. To address the limitations of conventional methods in efficiency and adaptability under complex multi-factor conditions, this study proposes a hybrid simulation–artificial intelligence framework for rapid slope stability assessment and bench face angle optimization. Multi-scenario numerical simulations were conducted by integrating geological investigation data, laboratory and in situ mechanical parameters, and extreme rainfall conditions to characterize slope deformation and failure mechanisms and generate a dataset for machine learning model training. Machine learning models were trained using slope height, bench face angle, unit weight, cohesion, and friction angle as inputs, and safety factors under natural and extreme rainfall conditions as outputs, with hyperparameters optimized by Bayesian optimization. The results indicate that highly weathered rock masses dominate shallow deformation and act as critical weak zones, while extreme rainfall significantly accelerates instability evolution and reduces slope safety factors. Among the RF, SVR, and ELM models, the Bayesian-optimized support vector regression (BO-SVR) exhibits the best predictive performance (R² > 0.98). SHapley Additive exPlanations (SHAP) analysis reveals that slope height and shear strength parameters are the dominant controlling factors, whereas unit weight has a relatively limited influence. Validation using real landslide cases shows good agreement with numerical simulations, confirming the reliability of the proposed framework. The developed approach enables rapid risk evaluation and supports bench face angle optimization, providing an effective tool for intelligent slope management in open-pit mining. Full article

Objectives: Myelodysplastic syndromes (MDSs) are a heterogeneous group of clonal hematopoietic disorders characterized by ineffective hematopoiesis, genomic instability, and a high risk of progression to acute myeloid leukemia. Oxidative stress (OS) has emerged as a central factor in MDS pathophysiology, contributing to DNA damage, altered cellular signaling, and disease progression. Recent advances in artificial intelligence (AI) and machine learning (ML) offer a transformative approach for integrating multidimensional datasets including oxidative stress markers, hematologic parameters, and molecular profiles to enhance diagnosis, prognostication, and therapeutic monitoring in MDS. Methods: A comprehensive literature search was conducted in PubMed and Scopus, using the keywords “OS biomarkers,” “AI,” and “MDS’’. Results: Modified redox biomarkers can be correlated with oxidative imbalance and disease progression. ML models such as neural networks, decision trees, and support vector machines effectively capture complex relationships among redox biomarkers, enhancing risk stratification and prediction of treatment response. AI-driven proteomic analyses further revealed OS-related protein signatures linked to MDS pathophysiology. Overall, AI and ML enable the transformation of multidimensional OS data into clinically actionable tools for personalized management in MDS. Conclusions: Integrating biomarker research with AI-based analytics holds promise for advancing personalized diagnostics, prognostication, and therapeutic strategies in MDS, paving the way toward precision medicine. Full article

Soil total nitrogen (STN) is a key indicator of soil fertility and plays a fundamental role in agricultural productivity and sustainable land management. However, achieving an accurate and spatially continuous estimate of STN at the field scale remains challenging due to inherent soil variability and the constraints of conventional sampling methods. In this study, we employed unmanned aerial vehicle (UAV)-based hyperspectral imagery to estimate STN by integrating spectral preprocessing, feature selection, and machine learning techniques. Multiple feature selection methods, including Pearson correlation analysis, variable importance in projection (VIP), and competitive adaptive reweighted sampling (CARS), were evaluated to identify the most informative spectral bands. Several regression models—support vector regression with radial basis function kernel (SVR-RBF), random forest (RF), Extra Trees, PCA-SVR-RBF, and XGBoost—were compared for STN prediction. Among these, the VIP-PCA-SVR-RBF model yielded the best performance, achieving a test R² of approximately 0.77 and an RMSE of 0.45 g kg⁻¹. The integration of VIP-based feature selection with PCA dimensionality reduction significantly enhanced predictive accuracy and generalization capability compared to the other models tested. Spatial prediction maps derived from the optimal model revealed considerable heterogeneity in STN distribution across the study area. These results underscore the potential of UAV hyperspectral remote sensing for high-resolution mapping of soil nitrogen and offer a promising framework for precision nutrient management in agriculture. Full article

This paper is the empirical validation companion to our prior methodology paper introducing the Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D) methodology, conducted through multi-vector adversarial testing on physical NVIDIA Jetson Orin Nano hardware. AZTRM-D unifies DevSecOps automation, the NIST Risk Management Framework, and Zero Trust architecture with AI orchestration via Cybectr Sentinel, featuring six AI subsystems with formal specifications. Testing spanned three progressive hardening stages across seven attack categories under a blind three-tester protocol with inter-rater agreement analysis. Factory-default devices were fully compromised in under five minutes. After full hardening, zero successful breaches were recorded across any tested vector. The CI/CD pipeline achieved a vulnerability detection rate of 96.8% (Wilson 95% CI: [0.891, 0.991]). Sentinel delivered 94.1% precision, 91.8% recall, and 4.2 min average detection time within 12−18% CPU overhead on edge hardware. A 14-capability comparative analysis against five established frameworks found seven capabilities unique to AZTRM-D. The 93.7% adversarial detection rate is reported against DiCE-generated counterfactual inputs and is bounded by the black-box threat model used in evaluation; gradient-based white-box attack evaluation is documented as a scoped Stage 4 future-work item. All three testers are affiliated with Cybectr LLC, the developer of AZTRM-D and Cybectr Sentinel; this conflict of interest is the most significant limitation of the present work, and independent third-party laboratory validation is the highest-priority Stage 4 deliverable. Full article

Plant viral diseases threaten the tomato agricultural industry. A smart decentralized diagnostic network was realized across the main Sicilian tomato-producing provinces for real-time detection/monitoring of Begomovirus solanumdelhiense (tomato leaf curl New Delhi virus—ToLCNDV), transmitted by Bemisia tabaci, Tobamovirus fructirugosum (tomato brown rugose fruit virus—ToBRFV), Orthotospovirus tomatomaculae (tomato spotted wilt virus—TSWV) and Amalgavirus lycopersici (southern tomato virus—STV). The network deployed smart portable thermocyclers and ready-to-use molecular diagnostic kits (real-time RT-LAMP, RT-qPCR). Data were remotely analyzed and in situ application of the developed kits was evaluated. Results revealed widespread STV infection (>70%) across all provinces, a variable ToBRFV presence with higher incidence in Ragusa (65%) and Siracusa (55.6%) provinces, ToLCNDV mainly concentrated in Siracusa (61.4%) and Trapani (60.2%) provinces, and localized TSWV outbreaks. ToLCNDV detection in Bemisia tabaci MED specimens confirmed the vector’s role in field transmission (up to 100% incidence). Performance comparison between laboratory and point-of-care conditions showed comparable accuracy, specificity, robustness, and rapid, cost-effective virus detection/monitoring. This diagnostic network enhances early diagnosis and timely phytosanitary interventions in tomato crops. The system supports integrated management strategies by reducing diagnostic delays and improving outbreak containment, control measures application and agroecosystem stability. Full article

Soybeans are a strategic commodity in global agricultural trade, and disruptions to their pricing system have direct implications for food security and trade patterns. This paper examines how major external shocks, particularly the Sino–U.S. trade wars, reshaped the dynamic connectedness and risk transmission structure of the global soybean price system. Using daily data from 2015–2025 for five key benchmarks, Chicago Board of Trade (CBOT) soybean futures, Dalian Commodity Exchange (DCE) No. 1 soybean futures, and cost-and-freight (CNF) prices for U.S. Gulf, Brazil, and Argentina shipments to China, we apply the time-varying parameter vector autoregression Diebold–Yilmaz connectedness model (TVP-VAR-DY) and the time-varying parameter vector autoregression Baruník–Křehlík frequency connectedness model (TVP-VAR-BK) models to quantify time-varying spillovers across short-, medium-, and long-run horizons. The results indicate that the global soybean market is highly integrated, while systemic risk transmission is predominantly short-run and declines sharply at longer horizons. CBOT futures remain the principal source of spillovers, especially in the short term, yet their net influence weakens noticeably after the 2018 trade-friction episode and declines further following the 2025 episode, particularly with respect to South American CNF benchmarks. Frequency-specific evidence suggests that trade-policy escalations are increasingly priced as structural shocks, strengthening medium- and long-horizon connectedness, while DCE’s outward spillovers rise markedly around 2025, consistent with the emergence of a more regionalized pricing architecture centered on Chinese demand. Within South America, Brazil leads short-run price formation, whereas longer-horizon dynamics are more exposed to Argentine policy risk spillovers. These findings provide new evidence on supply-chain reconfiguration and benchmark rebalancing in global soybean pricing and offer policy implications for strengthening China’s pricing capacity and enhancing multi-horizon supply-chain risk management. Full article

Pear fire blight, caused by the bacterium Erwinia amylovora, is a destructive disease affecting Rosaceae plants. Although insect transmission is well-documented, most studies have focused on pollinators, with limited attention to psyllids. Chinese pear psyllid (Cacopsylla chinensis) is a major piercing–sucking pest of pear trees, yet its role in the transmission of E. amylovora remains unclear. Here, we investigated the distribution of E. amylovora in and on C. chinensis and the synergistic damage (i.e., C. chinensis creates invasion wounds and nutrient-rich niches for E. amylovora via piercing–sucking feeding, while the pathogen enhances the vector’s fitness to promote disease spread). Field and laboratory assays confirmed severe synergistic symptoms. E. amylovora was isolated from all life stages and body parts of C. chinensis, with significantly higher pathogen loads and virulence in internally harbored strains compared to external ones. Specifically, E. amylovora loads in nymphs were significantly higher than those in adults, and strains from the digestive system and female reproductive organs caused a 3- to 9-fold higher disease index on pear leaves at 7 days post-inoculation compared to body surface isolates. This study provides evidence that C. chinensis acts as a crucial vector for E. amylovora in Xinjiang, laying a theoretical basis for the precise integrated management of this pest–disease complex. Full article

This study proposes a strategy for managing wind-turbine energy systems through the utilization of a high-gain observer in a sensorless backstepping control method applied to brushless double-fed reluctance generators (BDFRG). The paper initially introduces a vector control technique for brushless doubly fed reluctance generators, followed by the integration of the backstepping control method and the high-gain observer strategy within the overall system. Moreover, the research investigates the “maximum torque per inverter ampere” strategy, which enables the brushless doubly fed reluctance generators to achieve full magnetization by the primary winding, resulting in a reduction in the power factor. The stability of the system is established through the application of the Lyapunov theory. The simulation outcomes validate the efficacy and importance of this approach. Full article

Landslide susceptibility assessment (LSA) is crucial for regional landslide risk evaluation and mitigation strategy formulation. Previous studies mostly adopted single-scale features, while landslide formation is influenced by multi-scale factors, making multi-scale information extraction more appropriate for assessment. This study proposes a deep learning framework integrating multi-scale and attention modules for object-based LSA. A multi-scale network extracts geo-environmental features at different scales, which are input into attention networks using multi-head attention and Squeeze-and-Excitation, termed MSMHA and MSSE, respectively, to enhance relevant features and suppress irrelevant ones. Finally, features are fused for classification and prediction. In a case study in Hong Kong, CNN-based and ML-based methods were compared using 9,814 landslides and 11 influencing factors. Results show the proposed MSMHA (area under the curve, AUC 0.91) and MSSE (AUC 0.90) outperform conventional methods (e.g., random forest with AUC 0.86; multi-layer perceptron and support vector machine with AUC 0.85; DenseNet with AUC 0.86; CNN with AUC 0.88; VGG with AUC 0.87; GoogLeNet and ResNet with AUC 0.81). CNN-based methods outperformed ML-based ones, indicating that incorporating neighborhood information improves model performance. The rationality of the susceptibility map generated by MSMHA was verified via comparative analysis. Results confirm that the proposed multi-scale and attention-integrated framework outperforms traditional single-scale methods consistently. Equally importantly, the case study provides advanced CNN-based landslide susceptibility maps for Hong Kong, which can serve as a critical reference for regional landslide risk management and the formulation of targeted mitigation strategies. Full article

To overcome the limitations of traditional yield estimation methods—which are often subjective, costly, and difficult to implement at scale—this study developed a high-precision, interpretable model for predicting walnut yield by integrating multi-source remote sensing technology with interpretable machine learning. To provide a theoretical foundation for precise water and fertilizer management as well as intelligent production in walnut orchards. By employing interpretable machine learning and a multi-stage integration strategy, the model achieves not only high-precision yield estimation but also elucidates the influence pathways of water–fertilizer coupling on yield formation at a mechanistic level. This advancement offers reliable technical support and a decision-making framework for the precise management of orchards. This study focused on the Xinjiang ‘Wen 185’ walnut, employing field experiments with varying water and fertilizer gradients. A UAV equipped with a multispectral sensor was utilized to capture canopy images, from which vegetation indices and texture features were extracted. This process resulted in a comprehensive dataset that integrated remotely sensed features with management practices. Various machine learning algorithms, including random forest, support vector machine, partial least squares regression, and ridge regression, were applied. An innovative stacked integration model for growth stages was proposed, and the SHAP framework was incorporated to analyze feature contributions and enhance model interpretability. In this study, texture features—particularly those derived from the red-edge band—showed higher predictive importance than traditional vegetation indices. This suggests that they may be more sensitive to canopy structural heterogeneity under the tested conditions. Among the models, random forest showed numerically higher values in terms of R² and RPD compared to the other individual models under the present dataset, achieving a validation R² of 0.670 and an RPD of 1.836. The proposed growth stage stacking ensemble (GSSE) model further enhanced prediction accuracy, achieving validation R² of 0.789, an RMSE of 0.494, and an RPD of 2.296. Additionally, the results revealed that texture may have a potential ability to captured canopy heterogeneity as the primary mechanism underlying yield variation, and the integration of multi-stage spectral information was associated with higher estimation accuracy in this dataset in improving estimation accuracy, with the oil conversion stage contributing up to 60% to the final prediction. Full article

The generation of reliable and up-to-date national land cover information is essential for environmental management, climate action, and territorial planning. In Colombia, the CORINE Land Cover Colombia (CLCC) framework has been the official reference for land cover monitoring since 2000, traditionally updated through expert-based Computer-Assisted PhotoInterpretation (CAPI) at a 1:100,000 scale. However, increasing demands for higher spatial resolution and more frequent temporal updates have made process optimization necessary, driving the incorporation of cloud-based processing and artificial intelligence (AI), including machine learning and deep learning algorithms. This study presents a semi-automated methodology for generating a new generation of harmonized CLCC-compatible raster land cover maps at a 1:50,000 scale—offering four times greater spatial detail than the official vector product—with the capacity for semi-automated annual updates. The approach combines legend harmonization from 55 to 23 classes, historical CORINE Land Cover (CLC) polygon-guided sample generation, spectral stability analysis, and regionalized classification across 190 homogeneous subregions, supported by a reproducible cloud-based architecture. National land cover maps were produced for 2020, 2022, and 2024 with thematic accuracies above 80% and Kappa coefficients up to 0.87, alongside change maps for the 2022–2024 period capturing key dynamics in agricultural frontier expansion, wetland variability, and urban expansion. The resulting products also provide structured inputs for expert-based CAPI workflows, supporting the continuous updating of the official 1:100,000 CLCC map. The results demonstrate the operational capacity of integrating AI, cloud computing, and expert knowledge to strengthen Colombia’s national land cover monitoring system. Full article

Sepsis remains a leading cause of mortality worldwide and is increasingly recognized as a syndrome of dynamic immune dysregulation rather than a uniform inflammatory condition. The traditional paradigm of sequential hyperinflammation followed by immunosuppression has been replaced by a more complex view in which these processes coexist and evolve over time, contributing to marked interindividual variability in clinical outcomes. Despite advances in supportive care, current diagnostic and therapeutic approaches are still largely non-specific and fail to account for this biological heterogeneity. Extracellular vesicles (EVs) have emerged as key mediators of intercellular communication and potential integrators of immune activity in sepsis. These nanosized particles carry proteins, nucleic acids, lipids, and metabolites that reflect the functional state of their cells of origin and actively participate in immune regulation. Experimental and clinical evidence indicate that EVs exert context-dependent effects, contributing both to the propagation of inflammatory processes and the establishment of immunosuppressive states through the transfer of regulatory signals. Beyond their mechanistic role, EVs represent a promising platform for immune monitoring. Their cell-specific and dynamic molecular signatures have been associated with disease severity, organ dysfunction, and clinical trajectories, suggesting their role as biomarkers for patient stratification. In parallel, engineered and stem cell-derived EVs are being explored as therapeutic vectors capable of modulating immune responses and restoring immune homeostasis. In this review, we examine current concepts of immune dysregulation in sepsis and discuss how EVs may serve as both mediators and decoders of immune heterogeneity. We propose that EV-based approaches could bridge the gap between high-dimensional immunological profiling and precision immunotherapy, enabling more adaptive and individualized management of septic patients. Full article

Mass trapping of adult mosquitoes is increasingly promoted as an environmentally friendly alternative to insecticide-based vector control, yet quantitative evidence for its long-term population-level effects remains limited. We analyzed adult Aedes mosquito Biogents trap data from four tropical islands (three in the Maldives, one in the Philippines) where mass trapping was implemented at different trap densities. Using equilibrium-constrained population models, we describe how adult Aedes populations differ across trap densities, with outcomes ranging from partial suppression to near-zero levels at higher trap densities. At low to intermediate densities (4–6 traps·ha⁻¹), populations stabilized at non-zero equilibrium levels, whereas operational elimination was consistently observed at densities ≥ 10 traps ha⁻¹. A descriptive curve is shown to illustrate the decline in equilibrium abundance with increasing trap density, while a conceptual sigmoid model is used to illustrate how a transition in the recruitment–removal balance may occur under theoretical conditions. Limited larval source management was implemented on two islands, but elimination was also observed in the absence of larval interventions, indicating that sustained adult removal appears to have been the dominant driver of suppression. These findings indicate that mass trapping, when deployed at sufficiently high densities, is associated with rapid declines to near-zero population levels and may serve as an effective component of integrated vector management, particularly in geographically bounded settings or as a rapid-response intervention during outbreaks of arboviral diseases. Full article