Search Results (4,880)

This paper investigates modulation recognition technology for high-order modulated signals. Addressing the issue that existing deep learning-based modulation recognition methods struggle to effectively capture the features of long sequence signals in high-order modulation, we propose a ConvMamba model that integrates convolutional neural networks (CNNs) with the Mamba2 architecture. By employing a selective state-space model, the ConvMamba effectively captures the temporal dependencies in long sequence signals. It also combines the local feature extraction capability of CNNs with a soft-thresholding denoising module, forming a hybrid structure that possesses both global modeling and noise resistance capabilities. The evaluation results on the Sig53 dataset, which contains a rich variety of high-order modulations, demonstrate that compared to traditional CNN- or Transformer-based architectures, ConvMamba achieves a better balance between computational efficiency and recognition accuracy. Compared to Transformer models with similar performance, ConvMamba reduces computational complexity by over 60%. Compared to CNN models with comparable computational resource consumption, ConvMamba significantly improves recognition accuracy. Therefore, ConvMamba shows a distinct advantage in processing high-order modulated signals with long sequences. Full article

Squall lines are some of the most common types of mesoscale cloud systems in tropical and subtropical regions. Thunderstorms associated with these systems are among the major causes of weather-related disasters and socio-economic losses in many regions across the world. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating squall line features over the South African Highveld region. Two squall line cases were selected based on the availability of South African Weather Service (SAWS) weather radar data: 21 October 2017 (early austral summer) and 31 January–1 February 2018 (late austral summer). The European Centre for Medium-Range Weather Forecasts ERA5 datasets were used as observational proxies to analyze squall line features and compare them with WRF simulations. Mid-tropospheric perturbations were observed along westerly waves in both cases. These perturbations were coupled with surface troughs over central interior together with the high-pressure systems to the south and southeast of the country creating strong pressure gradients over the plateau, which also transports relative humidity onshore and extending to the Highveld region. The 2018 case also had a zonal structured ridging High, which was responsible for driving moisture from the southwest Indian Ocean towards the eastern parts of South Africa. Both ERA5 and WRF captured onshore near surface (800 hPa) winds and high-moisture contents over the eastern parts of the Highveld. A well-defined dryline was observed and well simulated for the 2017 event, while both ERA5 and WRF did not show any dryline for the 2018 case that was triggered by orography. While WRF successfully reproduced the synoptic-scale processes of these extreme weather events, the simulated rainfall over the area of interest exhibited a broader spatial distribution, with large-scale precipitation overestimated and convective rainfall underestimated. Our study shows that models are able to capture these systems but with some shortcomings, highlighting the need for further improvement in forecasts. Full article

Electrochemical reduction processes enable the CO to be converted into a useful chemical fuel. Our study employs density functional theory calculations to analyze the (110) facets of the transition metal carbide surfaces for CO capture, incorporating the Mars–van Krevelen (MvK) mechanism. All the possible adsorption sites on the surface, including carbon, metal, and bridge sites, were fully investigated. The findings indicate that the carbon site is more active relative to the other adsorption sites examined. The CO hydrogenation paths have been comprehensively investigated on all the surfaces, and the free energy diagrams have been constructed towards the product. The results conclude that the TiC is the most promising candidate for the formation of methane, exhibiting an onset potential of −0.44 V. The predicted onset potential for CrC, MoC, NbC, VC, WC, ZrC, and HfC are −0.86, −0.61, −0.61, −0.93, −0.87, −0.61, and −0.81 V, respectively. Our calculated results demonstrate that MvK is selectively relevant to methane synthesis. Additionally, we investigated the stability of these surfaces against decomposition and conversion to pure metals concerning thermodynamics and kinetics. It was found that these carbides could remain stable under ambient conditions. The exergonic adsorption of hydrogen on carbon sites, requiring smaller potential values for product formation, and stability against decomposition indicate that these surfaces are highly suitable for CO reduction reactions using the MvK mechanism. Full article

Objective: To evaluate the feasibility and effectiveness of wrist-worn wearable devices combined with machine learning (ML) approaches for detecting a diverse array of seizure types beyond generalized tonic–clonic (GTC), including focal, generalized, and subclinical seizures. Materials and Methods: Twenty-eight patients undergoing inpatient video-EEG monitoring at Mayo Clinic were concurrently monitored using Empatica E4 wrist-worn devices. These devices captured accelerometry, blood volume pulse, electrodermal activity, skin temperature, and heart rate. Seizures were annotated by neurologists. The data were preprocessed to experiment with various segment lengths (10 s and 60 s) and multiple feature sets. Three ML strategies, XGBoost, deep learning models (LSTM, CNN, Transformer), and ROCKET, were evaluated using leave-one-patient-out cross-validation. Performance was assessed using area under the receiver operating characteristic curve (AUROC), seizure-wise recall (SW-Recall), and false alarms per hour (FA/h). Results: Detection performance varied by seizure type and model. GTC seizures were detected most reliably (AUROC = 0.86, SW-Recall = 0.81, FA/h = 3.03). Hyperkinetic and tonic seizures showed high SW-Recall but also high FA/h. Subclinical and aware-dyscognitive seizures exhibited the lowest SW-Recall and highest FA/h. MultiROCKET and XGBoost performed best overall, though no single model was optimal for all seizure types. Longer segments (60 s) generally reduced FA/h. Feature set effectiveness varied, with multi-biosignal sets improving performance across seizure types. Conclusions: Wrist-worn wearables combined with ML can extend seizure detection beyond GTC seizures, though performance remains limited for non-motor types. Optimizing model selection, feature sets, and segment lengths, and minimizing false alarms, are key to clinical utility and real-world adoption. Full article

Coastal lagoons provide vital ecological functions, supporting diverse flora and fauna while being highly sensitive to environmental changes. In the southern Baltic Sea, the Puck Lagoon is a hydrologically distinct subregion of the Gulf of Gdańsk characterized by variable exchange of water with the outer bay and substantial freshwater inflows. Its benthic communities are particularly sensitive to salinity, yet the processes shaping this parameter remain insufficiently understood. In situ measurements in summer 2020 revealed relatively high salinity in the lagoon (up to 7.7 PSU) compared to the adjacent outer bay (7.2–7.4 PSU), with localized reductions near the Kuźnica Passage and the Reda River mouth. As a first step toward explaining the hydrodynamic processes responsible for these anomalies, we applied a high-resolution, two-dimensional model focused on three fundamental physical drivers: river inflows, open-boundary exchange, and wind forcing. These processes represent the primary controls on salinity in shallow lagoons and provide a basis for evaluating additional mechanisms. The model reproduced observed patterns with a mean absolute error of 0.15 PSU, confirming that this selective framework captures the key features of salinity variability and establishes a baseline for future three-dimensional modeling that will incorporate further processes such as vertical mixing, precipitation, and evaporation. Full article

Fuel Moisture Content (FMC) is a critical indicator for assessing forest fire risk and formulating early warning strategies, as its spatiotemporal dynamics directly influence the accuracy of fire danger rating. To improve the accuracy of forest FMC estimation, this study proposes an innovative deep learning method integrating multi-source remote sensing data. By combining the global feature extraction capability of the Transformer architecture with the local temporal modeling advantages of Gated Recurrent Units (GRU) (referred to as the Transformer-GRU model), a high-precision FMC estimation framework is established. The study focuses on forested areas in California, USA, utilizing ground-measured FMC data alongside multi-source remote sensing datasets from MODIS, Sentinel-1, and Sentinel-2. A systematic comparison was conducted among Transformer-GRU model, standalone Transformer models, single GRU models, and two classical machine learning models (Random Forest, RF, and Support Vector Regression, SVR). Additionally, forward feature selection was employed to evaluate the performance of different models and feature combinations. The results demonstrate that (1) All models effectively utilize the derived features from multi-source remote sensing data, confirming the significant enhancement of multi-source data fusion for forest FMC estimation; (2) The Transformer-GRU model outperforms other models in capturing the nonlinear relationship between FMC and remote sensing data, achieving superior estimation accuracy (R² = 0.79, MAE = 8.70%, RMSE = 11.44%, rRMSE = 12.60%); (3) The spatiotemporal distribution patterns of forest FMC in California generated by the Transformer-GRU model align well with regional geographic characteristics and climatic variability, while exhibiting a strong relationship with historical wildfire occurrences. The proposed Transformer-GRU model provides a novel approach for high-precision FMC estimation, offering reliable technical support for dynamic forest fire risk early warning and resource management. Full article

A systematic fuzzy multi-criteria decision making (MCDM) framework is proposed to prioritize near-Earth asteroids (NEAs) for a boulder capture mission, addressing the requirement for rigorous prioritization of asteroid candidates under conditions of data uncertainty. Twenty-eight NEA candidates were first selected through filtering based on physical and orbital properties. Then, objective fuzzy weighting MCDM methods (statistical variance, CRITIC, and MEREC) were applied to determine the importance of criteria such as capture cost, synodic period, rotation rate, orbit determination accuracy, and similarity to other candidates. Subsequent fuzzy ranking MCDM techniques (WASPAS, TOPSIS, MARCOS) generated nine prioritization schemes whose coherence was assessed via correlation analysis. An innovative sensitivity analysis employing Dirichlet-distributed random sampling around reference weights quantified ranking robustness. All methodologies combinations consistently identified the same top four asteroids, with 2013 NJ ranked first in every scenario, and stability metrics confirmed resilience to plausible weight variations. The modular MCDM methodology proposed provides mission planners with a reliable, adaptable decision support tool for asteroid selection, demonstrably narrowing broad candidate pools to robust targets while accommodating future data updates. Full article

The photocatalytic reduction of CO₂ into high-value chemicals utilizing solar energy represents a sustainable approach to mitigating greenhouse gas emissions and advancing renewable chemical production. Recently, copper-based metal–organic frameworks (Cu-MOFs) have been extensively researched for their potential in photocatalytic CO₂ reduction, due to their high affinity for capturing CO₂, the presence of unsaturated Cu sites, and their advantageous photochemical properties. In this review, we first provide an overview of Cu active sites in the secondary building units (SBUs) of MOFs, focusing on their selective adsorption of CO₂ gas and analyzing the mechanisms of the multi-electron transfer processes involved in Cu-based photocatalytic reduction of CO₂. Ultimately, this article outlines the existing obstacles and suggests potential avenues for future research. Full article

The integration of renewable energy and power-to-gas (P2G) technology into park-level integrated energy systems (PIES) offers a sustainable pathway for low-carbon development. This paper presents a low-carbon economic dispatch model for PIES that incorporates uncertainties in renewable energy generation and load demand. A novel two-stage P2G, replacing traditional devices with electrolysers (EL), methane reactors (MR), and hydrogen fuel cells (HFC), enhances energy efficiency and facilitates the utilisation of captured carbon. Furthermore, adjustable thermoelectric ratios in combined heat and power (CHP) and HFC improve both economic and environmental performance. A ladder-type carbon trading and green certificate trading mechanism is introduced to effectively manage carbon emissions. To address the uncertainties in supply and demand, the study applies information gap decision theory (IGDT) and develops a robust risk-averse model. The results from various operating scenarios reveal the following key findings: (1) the integration of CCT with the two-stage P2G system increases renewable energy consumption and reduces carbon emissions by 5.8%; (2) adjustable thermoelectric ratios in CHP and HFC allow for flexible adjustment of output power in response to load requirements, thereby reducing costs while simultaneously lowering carbon emissions; (3) the incorporation of ladder-type carbon trading and green certificate trading reduces the total cost by 7.8%; (4) in the IGDT-based robust model, there is a positive correlation between total cost, uncertainty degree, and the cost deviation coefficient. The appropriate selection of the cost deviation coefficient is crucial for balancing system economics with the associated risk of uncertainty. Full article

Engineering phase-selective gel composites presents a promising route to enhance both CO₂ adsorption and conversion efficiency in photocatalytic systems. In this work, Cu₉S₅/TiO₂ gel composites were synthesized via a hydrazine-hydrate-assisted hydrothermal method, using TiO₂ derived from a microwave-assisted sol–gel process. The resulting materials exhibit a porous gel-derived morphology with highly dispersed Cu₉S₅ nanocrystals, as confirmed by XRD, TEM, and XPS analyses. These structural features promote abundant surface-active sites and interfacial contact, enabling efficient CO₂ adsorption. Among all samples, the optimized 0.36Cu₉S₅/TiO₂ composite achieved a methane production rate of 34 μmol·g⁻¹·h⁻¹, with 64.76% CH₄ selectivity and 88.02% electron-based selectivity, significantly outperforming Cu₉S₈/TiO₂ synthesized without hydrazine hydrate. This enhancement is attributed to the dual role of hydrazine: facilitating phase transformation from Cu₉S₈ to Cu₉S₅ and modulating the interfacial electronic environment to favor CO₂ capture and activation. DFT calculations reveal that Cu₉S₅/TiO₂ effectively lowers the energy barriers of critical intermediates (*COOH, *CO, and *CHO), enhancing both CO₂ adsorption strength and subsequent conversion to methane. This work demonstrates a gel-derived composite strategy that couples efficient CO₂ adsorption with selective photocatalytic reduction, offering new design principles for adsorption–conversion hybrid materials. Full article

This study aimed to compare the changes in brain functional connectivity between states of stress induction and recovery in mentally stable, healthy individuals to investigate the effects of stress on brain networks. We selected a stable group comprising 20 healthy adults with Perceived Stress Scale scores of 0–13 points and a mean age of 24.4 ± 4.3 years. We used the Montreal Imaging Stress Task to induce stress and captured images of the brain using a 3T magnetic resonance imaging scanner. We analyzed the region of interest (ROI)-to-ROI connectivity and compared the differences in functional connectivity between the stress and recovery phases. In the stress state, we observed increased connectivity between the dorsal attention and sensorimotor networks and between the visual and default mode networks. In the recovery state, the default mode network became reactivated, and connectivity supporting self-referential thinking and stability was observed. The connectivities observed only in the recovery phase were Language.pSTG (R)—DefaultMode.LP (R) and DefaultMode.LP (R)—Visual.Lateral (R). Our findings provide important basic data for the development of stress management and recovery strategies. By assessing healthy individuals, our findings provide new perspectives on stress resilience in the brain. Full article

Background/Objectives: Quantitative approaches to talent identification in youth soccer often rely on either closed-skill assessments or small-sided games, but each carries inherent uncertainties that can reduce selection accuracy. Effective talent selection requires integrating both sources of data while accounting for their limitations. This study aimed to develop and validate a framework that combines closed-skill tests with competitive 1v1 game outcomes to optimize early-stage player selection. Methods: We assessed the dribbling and sprinting performances of 30 Brazilian youth players and used 1308 individual 1v1 bouts (70–90 bouts/individual) to estimate competitive abilities using a Bayesian ordinal regression model. Based on our empirical results, we then ran simulations to determine how many players should be selected when the aim is to reduce a player pool of 100 individuals so that the ‘true’ top 10 performers are reliably included and to determine how the weighting between data from closed-skill tests and games should change with increasing match observations. Results: Dribbling speed was a strong predictor of 1v1 success (β = –0.76, 95% CI: [–1.16, –0.40]), while sprint speed (β = 0.01, 95% CI: [–0.36, 0.40]) showed no significant association with 1v1 success. Simulations revealed that 26.0 ± 2.5 players were needed after five 1v1 contests per player to capture the true top 10% and then decreased to 18.0 ± 1.5 players after 20 contests. Optimal weighting shifted from a greater reliance on dribbling-based data (α > 0.80 at Game 0) to more match-based data after 10–20 contests per player (α = 0.16 at Game 20), but utilizing both sources of data improved selection accuracies and efficiencies. Conclusions: This study provides an uncertainty-aware protocol for talent identification that optimizes the integration of data from closed-skill tests and in-game performances within a dynamic selection framework that enhances precision and forms the basis for efficient early-stage scouting of large cohorts of players. Full article

AI-integrated robotics in Industry 5.0 demands advanced manufacturing systems capable of autonomously interpreting complex geometries and dynamically adjusting machining strategies in real time—particularly when dealing with aerospace components featuring large-curvature surfaces. Large-curvature aerospace components present significant challenges for precision drilling due to surface-normal deviations caused by curvature, roughness, and thin-wall deformation. This study presents a robotic drilling system that integrates adaptive PCA-based surface normal estimation with in-process pre-drilling correction and post-drilling verification. This system integrates a 660 nm wavelength linear laser projector and a 1.3-megapixel industrial camera arranged at a fixed 30° angle, which project and capture structured-light fringes. Based on triangulation, high-resolution point clouds are reconstructed for precise surface analysis. By adaptively selecting localized point-cloud regions during machining, the proposed algorithm converts raw measurements into precise normal vectors, thereby achieving an accurate solution of the normal direction of the surface of large curvature parts. Experimental validation on a 400 mm-diameter cylinder shows that using point clouds within a 100 mm radius yields deviations within an acceptable range of theoretical normals, demonstrating both high precision and reliability. Moreover, experiments on cylindrical aerospace-grade specimens demonstrate normal direction accuracy ≤ 0.2° and hole position error ≤ 0.25 mm, maintained across varying curvature radii and roughness levels. The research will make up for the shortcomings of existing manual drilling methods, improve the accuracy of hole-making positions, and meet the high fatigue service needs of aerospace and other industries. This system is significant in promoting the development of industrial automation and improving the productivity of enterprises by improving drilling precision and repeatability, enabling reliable assembly of high-curvature aerospace structures within stringent tolerance requirements. Full article

For unmanned ground vehicles in squad mission support systems (SMSS-UGVs), tracking the entire squad as a group, rather than focusing on individual members, can effectively mitigate issues such as target loss caused by occlusion and environmental interference. However, most existing group target tracking methods are designed for extended targets, which typically assume a rigid and unchanging shape. In contrast, pedestrian groups in SMSS-UGV scenarios exhibit inconsistent motions among members, resulting in continuous changes in the overall group shape. To address this challenge, this paper proposes a group target tracking method specifically tailored for SMSS-UGVs in pedestrian tracking scenarios. We introduce a tracking framework that incorporates a data selection mechanism based solely on positional information, enabling robust handling of dynamic group composition through adaptive shape modeling. Furthermore, a novel group target tracking method based on multi-ellipse shape modeling (ME-CGT-UGV) is presented, which effectively captures complex and evolving group formations. The experimental results show that the proposed method reduces orientation error by 86.13% compared to single-target tracking and by 54.79% compared to shapeless modeling methods. It also maintains strong performance under challenging conditions, including occlusions, environmental disturbances, sharp turns, and formation changes. These findings indicate that the proposed approach significantly enhances the effectiveness and operational reliability of SMSS-UGVs in real-world applications. Full article

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of the dynamical system governing the subject vehicle’s motion. Unlike conventional machine learning models that typically seek a single generalized representation across all drivers, our framework develops individualized models that explicitly preserve driver heterogeneity. Using both synthetic data from multiple traffic models and real-world naturalistic driving datasets, we demonstrate that DMDc accurately captures nonlinear vehicle interactions and achieves high-fidelity short-term predictions. Analysis of the estimated system matrices reveals that DMDc naturally approximates kinematic relationships, further reinforcing its interpretability. Importantly, this is the first study to apply DMDc to model and predict car-following behavior using real-world driving data. The proposed framework offers a computationally efficient and interpretable tool for traffic behavior analysis, with potential applications in adaptive traffic control, autonomous vehicle planning, and human-driver modeling. Full article