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Application of swarm-based deep neural networks and ensemble models for reconstruction of specific conductance data
Knowledge, awareness, and attitude of physical therapists on Alzheimer’s disease in Syria
Irisin regulates lipid metabolism and ferroptosis in ovarian cancer cells by modulating the ALOX5-5-HETE-PD-L1 axis
PGSFormer: traffic flow prediction based on joint optimization of progressive graph convolutional networks with subseries transformer
Pore-micro fracture structure, porosity and gas- bearing property of deep shale under lithofacies-formation pressure coupling
Utilizing optical coherence tomography and machine learning to identify vision abnormalities in pediatric neurofibromatosis type 1 patients
The role of toll-like receptor 7 signaling in mice infected by severe fever with thrombocytopenia syndrome virus
Statistical downscaling reproduces high-resolution ocean transport for particle tracking in the Bering Sea
Abstract Understanding ocean transport is critical for applications ranging from fisheries to chemical plume tracking and carbon dioxide removal modeling. However, available hydrodynamic data often lack the spatial resolution needed for effective transport simulations. We apply statistical downscaling to coarse-resolution ocean reanalysis and atmospheric wind data, reconstructing fine-scale fields validated against high-resolution dynamic models in the Bering Sea. This enables the prediction of transport patterns without the need to run high resolution physics simulations, saving computational costs and time. We examined five years of high-resolution, statistically downscaled ocean currents and surface winds and found that the correlation of ocean current and wind components with GLORYS and ERA5 reanalysis models were r = 0.87 and r = 0.98. The Liu-mean skill score was 0.75 for ocean current velocity. Okubo–Weiss analyses showed comparable vorticity and shear between downscaled and dynamical models. The Finite-time Layupanov Exponent analysis showed consistent Lagrangian Cohesive Structures across datasets. Multi-year particle tracking using both downscaled and reanalysis forcing showed consistent relative separation distances with mean Bhattacharyya coefficient of 0.720 ± 0.133. The demonstrated parity in dispersal patterns indicates statistically downscaled approaches can substitute dynamical models for large-scale applications. Future work should validate these results across diverse oceanographic regimes and incorporate biogeochemical feedback mechanisms.
Tephra-mediated manganese cycling shapes coral responses to coastal sedimentation
Abstract Terrestrial runoff from tropical volcanic islands impacts coral reefs by increasing turbidity and sedimentation. During explosive volcanic eruptions, large amounts of fragmented volcanogenic rock (tephra) are deposited, exacerbating sediment runoff for long periods of time. Nevertheless, tephra is an important, yet underestimated, source of the essential trace metal manganese (Mn), which promotes coral photosynthesis. Here, we show Mn leached from pristine and remobilised tephra increases resilience to sedimentation stress. Using coral culture experiments, microcolonies of Stylophora pistillata exposed to four tephra samples all showed rapid and sustained increases in photosynthetic efficiency (Φ PSII , rETR, P n and P gross ), even under reduced light conditions. Photosynthetic efficiency is logarithmically correlated to seawater Mn concentration, with large increases at values < 3 µg Mn L − 1 , and negligible changes at values > 10 µg Mn L − 1 . Tephra exposure has a crucial role in coastal Mn cycling and potentially benefits stressed corals following environmental disturbances.
Multimodal road perception with illumination adaptation in autonomous vehicles
Higher lymph node burden predicts greater chemotherapy benefit in resected pancreatic ductal adenocarcinoma: evidence from 22,045 patients
An IoT-based smart emotion recognition system by using internal body parameters
Abstract Emotion recognition using physiological signals has gained significant attention in recent years due to its potential applications in mental health monitoring, human–computer interaction, and stress management. This study focuses on recognizing six emotional states neutral, happy, sad, fear, anger, and surprise using internal body parameters such as blood pressure, oxygen saturation, blood glucose, heart rate, and body temperature. Leveraging an Internet of Things (IoT) enabled framework, real-time data was collected from participants. An exhaustive experimental assessment has been performed on 11 different classification algorithms of the machine learning platform. Among the algorithms, the Random Forest algorithm performed better than all other algorithms with 90.56% accuracy and 93.34% F1-score. Moreover, the precision and recall of the proposed system are extremely high. Model Robustness and generalization performances were evaluated by conducting internal as well as external validation. On conducting internal validation through k-fold cross-checking, the accuracy increased to 93.18%, clearly validating the consistency in the performance of the model. Further, the external validation was conducted by using the conventional DEAP emotional tasks, showing a collective accuracy of about 94% along with very good max and weighted average precision, recall, and F1-score values for all classes of emotions. This clearly validates the efficacy of the chosen physiological features as well as the correctness of the devised approach. The findings indicate that physiological signals, combined with IoT and machine learning, provide an effective framework for emotion recognition. This research contributes to the development of real-time, non-invasive emotion recognition systems, with promising applications in healthcare, wearable devices, and personalized user experiences. Future work will explore the integration of additional physiological parameters and advanced deep-learning models for enhanced accuracy and scalability, and usage in advanced technology.
Integrative bioinformatics analyses of mitochondrial dysfunction-related genes in human non-obstructive azoospermia
Phosphoglycerate Kinase Can Adopt Topologically Misfolded Forms That Are More Stable Than Its Native State
Multi-omics and palynology of selected Philippine forest honey
Experimental investigation and optimization of mechanical and tribological performances of bio-based sustainable hybrid composites incorporating Nano-SiO₂ fillers
Machine learning for prompt estimation of macroseismic intensity from seismometric data in Italy
Abstract After an earthquake, it is crucial to rapidly and accurately estimate macroseismic intensity to guide rescue operations and assess potential damage. The Mercalli-Cancani-Sieberg intensity scale is used to qualitatively assess the ground shaking based on observed effects. This study develops a Machine Learning framework, leveraging the Random Forest algorithm, to estimate macroseismic intensity using early available seismic data. Data from different sources are used for model training: seismic data from the Italian instrumental monitoring networks of Istituto Nazionale di Geofisica e Vulcanologia and Protezione Civile , as well as macroseismic intensity data from both the online macroseismic questionnaire and the on-site surveys by field experts. In order to explain the predictive mechanism of the Random Forest algorithm, this study makes use of surrogate decision trees, providing an interpretative key for the informed decision-making process during seismic events. These models provide insights into the relationships between covariates and predicted intensities, enabling the discussion of model complexity, predictive capability, and explainability. Furthermore, the uncertainty associated with the predictions of the surrogate trees is assessed. When compared with other models for estimating intensity based on ground motion peaks or source parameters, the Random Forest model achieved better predictive performance.