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Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm
Efficacy of remimazolam in preventing postoperative nausea and vomiting: a systematic review and meta-analysis
Shielding failure analysis of extra high voltage unconventional transmission lines with increased power delivery capability
Formulation of a dynamic convective adjustment time-scale in the CESM1.2 and its influence on the Indian summer monsoon simulations
Explainable ML modeling of saltwater intrusion control with underground barriers in coastal sloping aquifers
Abstract Reliable modeling of saltwater intrusion (SWI) into freshwater aquifers is essential for the sustainable management of coastal groundwater resources and the protection of water quality. This study evaluates the performance of four Bayesian-optimized gradient boosting models in predicting the SWI wedge length ratio (L/L a ) in coastal sloping aquifers with underground barriers. A dataset of 456 samples was generated through numerical simulations using SEAWAT, incorporating key variables such as bed slope, hydraulic gradient, relative density, relative hydraulic conductivity, barrier wall depth ratio, and distance ratio. The dataset was divided into 70% for training and 30% for testing. Model performance was assessed using both visual and quantitative metrics. Among the models, Light Gradient Boosting (LGB) achieved the highest predictive accuracy, with RMSE values of 0.016 and 0.037 for the training and testing sets, respectively, and the highest coefficient of determination (R²). Stochastic Gradient Boosting (SGB) followed closely, while Categorical Gradient Boosting (CGB) and eXtreme Gradient Boosting (XGB) showed slightly higher error rates. SHapley Additive exPlanations (SHAP) analysis identified relative barrier wall distance and bed slope as the most influential features affecting model predictions. To support practical application, an interactive graphical user interface (GUI) was developed, allowing users to input key variables and easily estimate L/L a values. Finally, the best-performing model was validated against the Akrotiri coastal aquifer in Cyprus, a realistic benchmark case derived from numerical simulations. The model’s predictions showed strong agreement with reference results, achieving an RMSE of 0.04, thereby confirming its practical applicability. This study highlights the potential of interpretable, optimized ML models to enhance SWI prediction and support informed decision-making in coastal aquifer management.
Contrasting niche dynamics in the invasion processes of two congeneric dinoflagellates
Abstract Niche-based models are essential for predicting invasion risks. Although most invasive species tend to conserve their ecological niches after introduction, some challenge this assumption by expanding or contracting their niches, yet such patterns remain underexplored in microorganisms. Since larger niche shifts can reduce the predictive performance of these models, this study examines whether the climatic niches of the invasive dinoflagellates Ceratium hirundinella (Müller, 1841) and C. furcoides (Langhans, 1925) have shifted following their invasion from native European to non-native American ranges, where they have caused significant impacts on biodiversity and water quality. Though both species are native to temperate European lakes, their colonization patterns in the Americas differ, and the drivers of their spread remain unclear. In this study, niche conservatism was analyzed using five niche dynamic metrics for both species. The current distribution of C. hirundinella primarily in subtropical and temperate non-native areas aligns with its native climate (i.e., higher niche stability), suggesting preadaptation. Meanwhile, for C. furcoides, a niche shift—indicated by maximum expansion and unfilling—suggests a much higher potential for rapid spread across both tropical and subtropical climates. These findings show distinct climatic responses of congeneric species in non-native ranges, emphasizing the need to move beyond native environmental predictors when assessing invasion risk. Future research should explore niche shifts over time and whether invasions begin in ecologically matched habitats (as expected for C. hirundinella) or are driven by propagule pressure and human activity despite niche mismatches (as in C. furcoides).
SARS-CoV-2 seroprevalence and COVID-19 vaccination coverage in two states of Nigeria from a population based household survey
Diabetic retinopathy classification using a multi-attention residual refinement architecture
Self-splicing RNA circularization facilitated by intact group I and II introns
Gastrointestinal neuroprosthesis for motility and metabolic neuromodulation
F-actin disassembly by the oxidoreductase MICAL1 promotes mechano-dependent VWF-GPIbα interaction in platelets
Abstract Mechano-dependent interactions are key to thrombus formation and hemostasis, enabling stable platelet adhesion to injured vessels. The interaction between von Willebrand factor (VWF) and the platelet receptor GPIb-IX-V is central to this process. While GPIbα connects to the actin cytoskeleton, whether actin dynamics are important for GPIbα function under hemodynamic, high shear conditions remains largely unknown. Here, we show that actin disassembly is critical for proper VWF-GPIbα binding under shear. Mechanistically, we identify the oxidoreductase MICAL1 as a shear-activated regulator that promotes local F-actin disassembly around the GPIb-IX-V complex. This enables its translocation to lipid rafts and reinforces VWF binding. MICAL1-deficient platelets display impaired adhesion, increased deformability under shear, and defective thrombus formation in vivo. Thus, MICAL1 drives shear-dependent actin remodeling that supports GPIb-IX-V mechanotransduction and platelet function. These findings uncover a role for actin oxidation in platelet adhesion, providing a connection between cytoskeletal redox control and platelet function during thrombus formation.
Synthesis of piperazine-based benzimidazole derivatives as potent urease inhibitors and molecular docking studies
The ocular shape and retinal structure in children with a history of treated retinopathy of prematurity
A dilemma study of the traffic flow system emerged due to the lane-change by follower’s tailgating effect
Research on temporal-spatial distribution differences and formation mechanisms of NPP in the Lanzhou section of the yellow river mainstream
Natural language processing reveals network structure of pain communication in social media using discrete mathematical analysis
Associations of maternal neighborhood and trauma-related stressors with mitochondrial DNA copy number and telomere length in maternal and cord blood
The impact of predation pressure, natural light, and species-specific factors on the prevalence and intensity of nocturnal singing by diurnal birds
Vehicle-to-everything decision optimization and cloud control based on deep reinforcement learning
Abstract To address the challenges of decision optimization and road segment hazard assessment within complex traffic environments, and to enhance the safety and responsiveness of autonomous driving, a Vehicle-to-Everything (V2X) decision framework is proposed. This framework is structured into three modules: vehicle perception, decision-making, and execution. The vehicle perception module integrates sensor fusion techniques to capture real-time environmental data, employing deep neural networks to extract essential information. In the decision-making module, deep reinforcement learning algorithms are applied to optimize decision processes by maximizing expected rewards. Meanwhile, the road segment hazard classification module, utilizing both historical traffic data and real-time perception information, adopts a hazard evaluation model to classify road conditions automatically, providing real-time feedback to guide vehicle decision-making. Furthermore, an autonomous driving cloud control platform is designed, augmenting decision-making capabilities through centralized computing resources, enabling large-scale data analysis, and facilitating collaborative optimization. Experimental evaluations conducted within simulation environments and utilizing the KITTI dataset demonstrate that the proposed V2X decision optimization method substantially outperforms conventional decision algorithms. Vehicle decision accuracy increased by 9.0%, rising from 89.2 to 98.2%. Additionally, the response time of the cloud control system decreased from 178 ms to 127 ms, marking a reduction of 28.7%, which significantly enhances decision efficiency and real-time performance. The introduction of the road segment hazard classification model also results in a hazard assessment accuracy of 99.5%, maintaining over 95% accuracy even in high-density traffic and complex road conditions, thus illustrating strong adaptability. The results highlight the effectiveness of the proposed V2X decision optimization framework and cloud control platform in enhancing the decision quality and safety of autonomous driving systems.