Search Results (46,975)

Atmospheric turbulence is a key multi-scale motion affecting numerical weather prediction, aviation safety, and atmospheric mass-energy exchange. Thorpe analysis is a classic method for turbulence retrieval, but traditional observations are limited by low spatiotemporal resolution, the absence of a stratospheric turbulence inversion framework, and insufficient cross-layer comparisons between northern and southern China, restricting the understanding of turbulence modulation mechanisms. Based on the domestic round-trip intelligent sounding system, atmospheric observations at 12 stations in Guangdong and Inner Mongolia from December 2022 to March 2023 are collected, with ascending-phase profiles used in the turbulence analysis and synergistically analyzed with ERA5 reanalysis data, which provide the background wind fields for westerly jet identification and precipitation data for environmental modulation assessment. Results show that turbulence in the study area shows significant layered differentiation and a latitudinal contrast between the southern and northern stations: the troposphere is the main turbulent layer, southern tropospheric turbulence is mainly associated with solar radiation, and northern tropospheric turbulence is mainly related to large-scale dynamic processes; stratospheric turbulence occurs sporadically only at northern stations under westerly jet-induced wind shear and is nearly absent in the south. Precipitation modulates southern tropospheric turbulence, while the westerly jet acts as a key dynamic factor for northern stratospheric turbulence. By integrating in situ radiosonde profiling with reanalysis data, this work supports refined turbulence detection and parameterization model optimization, and provides a scientific basis for weather forecast improvement and aviation route planning. Full article

Obesity, diabetes, and dyslipidemia form an interconnected metabolic disease cluster driven by insulin resistance and chronic inflammation. This study evaluated the clinical efficacy and multi-target mechanisms of Coptidis Rhizoma for these metabolic disorders through an integrated systematic review, meta-analysis of randomized controlled trials (RCTs), network pharmacology, and molecular docking. Meta-analysis of 19 RCTs (n = 1718) demonstrated that Coptidis-containing formulations, primarily as adjunctive therapy, significantly improved primary clinical endpoints across all three metabolic conditions: body mass index (BMI: SMD = −0.72, 95% CI [−1.00, −0.44], p < 0.00001), glycated hemoglobin (HbA1c: SMD = −0.77, 95% CI [−1.07, −0.47], p < 0.00001), and low-density lipoprotein cholesterol (LDL-C: SMD = −1.10, 95% CI [−1.57, −0.62], p < 0.00001). Network analysis revealed shared targets enriched in lipid metabolism, inflammatory signaling, and PI3K-Akt/MAPK cascades. Molecular docking demonstrated favorable structural compatibility and predicted docking scores (ranging from −7.1 to −9.5 kcal/mol) of berberine toward key hub proteins, including PIK3CA, JAK2, MAOA, and PARP1. Consequently, Coptidis Rhizoma provides tangible clinical benefits across interconnected metabolic outcomes via the multi-target regulation of inflammatory and insulin signaling pathways. Clinically, Coptidis-containing formulations show promise as a complementary adjunct to conventional metabolic therapies, though future large-scale, standardized RCTs remain necessary to confirm long-term safety and optimize dosing regimens. Full article

Emergency Medical Services (EMS) personnel require exceptional spatial awareness and rapid decision-making within the confined environment of an ambulance. While Virtual Reality (VR) offers a safe alternative to traditional training, the lack of high-fidelity, regionally accurate, and VR-optimized 3D ambulance models limits its application. This study presents a scenario-driven methodology for developing a VR-ready 3D ambulance environment prototype tailored for Emergency Medical Technician (EMT) training. Utilizing reality-capture techniques, terrestrial laser scanning was performed to accurately document the interior of a standard Hungarian ambulance simulator. The resulting point cloud underwent systematic processing, manual retopology, PBR shading, and the implementation of a custom dual-rigging animation system to optimize complex mechanical movements—such as stretcher operations—for standalone VR platforms. The workflow successfully reduced the vertex count to 25,373 while maintaining millimeter-level spatial fidelity. Technical evaluation confirmed that geometrical, functional, and material objectives were fulfilled, whereas pedagogical implementation remains incomplete. Structural accuracy and animation readiness were verified through preliminary inspection within Blender’s VR viewport inspector. However, interactive game-engine integration remains future work, and educational effectiveness has not yet been tested with EMT learners. Overall, this workflow delivers a 3D asset foundation that establishes the necessary technical basis for subsequent software implementation and clinical evaluation. Full article

Industrial structure policies for specialty agriculture aim to optimize production, product, and industrial structures in China, yet their effect on land use performance through local government competitive actions remains underexplored. Using a balanced panel dataset covering 292 prefecture-level and above cities in China from 2010 to 2023, this study treats China’s Agricultural Supply-Side Structural Reform (ASSR) as a quasi-natural experiment and employs a generalized difference-in-differences model to examine the effects and mechanisms of industrial structure policies on land use performance in specialty agriculture. The results indicate that industrial structure policies significantly improve land use performance in specialty agriculture, with local government competitive actions in product quality improvement, market channel development, and industrial integration serving as three important transmission mechanisms. In addition, the policy effect is particularly pronounced in non-provincial capital cities, non-major grain-producing areas, and regions southeast of China’s Hu Line, and stronger market integration further amplifies this effect. The study contributes a multidimensional land use performance framework and a local government competition channel for understanding how macro-level industrial policies translate into land use performance, with implications for differentiated land governance in developing economies. Full article

Early-exit deep neural networks (DNNs) can reduce edge-inference latency, but abrupt variations in wireless and computing resources can destabilize split-inference policies. This paper proposes a meta-learning-driven adaptive control framework for joint backbone splitting and early-exit routing in MobileViT. The framework formulates multi-exit splitting as a constrained Markov decision process (CMDP) and introduces splitting-aware multi-dimensional adaptive proximal policy optimization (SMAPPO). SMAPPO combines nonlinear quality-of-service (QoS) penalties with topology-aware action masking, while cross-environment meta-initialization supports edge-local adaptation after resource disturbances. Under the stated simulation assumptions, SMAPPO reached the highest performance-index plateau among six methods in a representative 500-episode stationary trace and achieved the lowest normalized total cost across three latency–energy preference settings. Across ten seeds and nine stationary or disturbed scenarios, online SMAPPO achieved a 77.20% measured accuracy and 22.40 mJ of system energy. With an adaptation horizon of $K=14$, SMAPPO yielded a post-disturbance mean latency of 37.68 ms, a QoS-violation rate of 2.24%, and an on-time completion rate of 98.69%. These results indicate that combining meta-initialization, nonlinear constraint shaping, and topology-aware action masking improves stationary optimization and disturbance recovery within the controlled simulator. Full article

Carbon-based materials exhibit diverse structural responses to irradiation owing to their distinct dimensionality, degree of graphitization, surface chemistry, and pore architecture. Although irradiation has traditionally been regarded as a source of structural damage, increasing evidence demonstrates that controlled irradiation can be deliberately utilized to tailor defects, surfaces, interfaces, and pore structures, thereby enabling desirable functional properties. This review summarizes recent progress in the irradiation-induced structural evolution and functional applications of four representative carbon-based materials, including graphene-based materials, carbon nanotubes, carbon fibers, and activated carbon/biochar. Particular attention is given to the characteristic irradiation responses of different carbon architectures. In graphene, irradiation predominantly induces vacancies, reconstructed defects, and surface functionalization, providing active sites for environmental remediation. Carbon nanotubes additionally undergo inter-tube cross-linking and welding, enabling enhanced mechanical performance and tunable electronic properties. For carbon fibers, irradiation mainly regulates surface chemistry and fiber matrix interactions, facilitating interface engineering in high-performance composites. In activated carbon and biochar, irradiation modifies pore accessibility, structural disorder, and surface functional groups, thereby influencing adsorption and electrochemical performance. These distinct responses demonstrate that irradiation can evolve from a conventional damage process into a controllable materials-engineering strategy when appropriate irradiation conditions are employed. Finally, current challenges associated with optimal irradiation conditions, quantitative defect identification, and irradiation structure–property relationships are discussed. Based on these distinct responses, we propose an architecture-dependent irradiation–structure–function (A-ISF) framework that links the initial carbon architecture and irradiation conditions to dominant energy-deposition mechanisms, structural evolution pathways, property modulation, and ultimately functional applications. Within this framework, irradiation engineering is interpreted as a competition between beneficial structural modification and excessive radiation damage, giving rise to an application-dependent optimal irradiation window. Full article

To address the inefficiencies of two-stage sweet potato harvesting in southern China, an integrated machine for synchronous haulm shredding and tuber excavation was developed. The equipment features a front-mounted, reverse-rotating crushing knife roller and a rear-mounted, adjustable grate-type digging shovel. The performance of the prototype was systematically evaluated through two-stage field trials in clay loam soil. First, an orthogonal test was employed to assess the haulm shredding quality. The results indicated that the knife roller speed significantly increased the qualified rate of crushed stems and leaves, whereas the forward speed exerted a negative effect. Additionally, the blade-to-ridge clearance primarily dictated the ridge-top stubble length. Second, a quadratic orthogonal rotational composite design was utilized to optimize the integrated harvesting parameters. The analysis demonstrated that shovel inclination significantly enhanced the tuber exposure rate, while both clearance and inclination exhibited quadratic nonlinear effects on the tuber damage rate. Multi-objective optimization established the optimal operational parameters as a blade-to-ridge clearance of 66.6 mm and a shovel inclination of 34.0°. Field validations under these settings achieved a tuber exposure rate of 83.7% and a damage rate of 4.3%, confirming the high reliability of the predictive models. The integrated equipment effectively shortens the harvesting cycle and demonstrates robust adaptability to clayey moist soils, thereby advancing the mechanization of sweet potato production. Full article

Background/Objectives: Hybrid EEG-fNIRS emotion recognition aims to accurately identify an individual’s emotional state by analyzing neurophysiological signals and constitutes an important research direction in affective brain–computer interfaces and human–computer interaction. In recent years, EEG-fNIRS emotion recognition has advanced from handcrafted feature extraction and shallow fusion to deep learning and graph-based modeling. However, most existing methods rely on predefined fixed frequency-band partitioning and second-order graph structures that only support pairwise connections, making it difficult to accommodate inter-subject frequency variability and to characterize high-order brain network relationships such as multi-channel synergistic activation within a frequency band and cross-frequency coupling. Methods: To address these issues, this paper proposes an EEG-fNIRS emotion recognition framework based on a Spatial-Frequency Hypergraph Neural Network (SF-HGNN). First, a Dynamic Frequency Band Decomposition module is designed to achieve adaptive optimization of the EEG and fNIRS frequency bands; second, a Multi-scale Temporal Convolution module extracts temporal features at different time scales; third, a Spatial-Frequency Adaptive Hypergraph Convolution module is constructed to model intra-band cross-channel spatial synergy and channel-wise cross-frequency coupling; and finally, a Cross-Modal Attention Fusion mechanism achieves high-order interaction between the complementary information of the two modalities. The proposed method was validated on the public ENTER dataset comprising 50 participants and four emotion categories (sadness, happiness, fear, and calm). Results: Experimental results show that SF-HGNN achieves accuracies of 82.94% and 68.85% in subject-dependent and subject-independent experiments, respectively; ablation studies and visualization analyses further verify the effectiveness of each module and the interpretability of the model. Conclusions: Future work will focus on validation with larger-scale data and improving cross-subject domain generalization. Full article

As of 2020, liver cancer has emerged as the fifth most common cancer and the third leading cause of cancer death worldwide, following lung and colorectal cancers. The most prevalent type is hepatocellular carcinoma (HCC), originating from hepatocytes. Despite advancements, liver cancer treatment outcomes remain poor due to metastasis and recurrence. Existing anticancer drugs often exhibit narrow therapeutic windows and limited selectivity for cancer cells. Manilkara zapota (L.) P. Royen has attracted significant scientific attention because of its diverse bioactive constituents and potential therapeutic properties. Of particular interest in this review, we explored the molecular connectivity of oxidative stress-induced liver cancer. We discussed the underlying mechanisms of Manilkara zapota (L.) P. Royen involved in cancer and inflammation. The phytochemical constituents were also highlighted in this study. The phytochemicals reported from Manilkara zapota (L.) P. Royen included flavonoids, tannins, saponins, and phenolic compounds. These compounds demonstrated potential anticancer activities through mechanisms such as induction of apoptosis, inhibition of cell proliferation, modulation of oxidative stress, and regulation of PI3K/Akt and NF-κB signaling pathways. Further investigations are required to clarify the benefit–risk profile of Manilkara zapota (L.) P. Royen through large-scale clinical trials. Collectively, the current evidence suggests that this plant may offer a promising strategy for cancer management, provided that such interventions are optimized to minimize adverse effects. Full article

Accurate short-term traffic forecasting and efficient dynamic route guidance are pivotal for mitigating urban congestion. However, accurately capturing complex temporal dependencies and nonlinear variations in traffic flow remains challenging, while conventional path-planning algorithms may also suffer from local-optimum problems. To address these challenges, this study proposes a hybrid framework integrating an optimized prediction model with an enhanced ant colony optimization algorithm. First, a GA-BiGRU-ATT model is developed for traffic state prediction. By combining a bidirectional gated recurrent unit (BiGRU), a temporal attention mechanism, and genetic algorithm-based hyperparameter optimization, the model captures contextual temporal dependencies within the observed historical input window and emphasizes critical time-step features. Under the evaluated model configurations, the proposed approach achieved traffic-state classification accuracies of 90.28% and 87.50% on Segments 1 and 2, respectively. Second, based on the predicted traffic states, an Improved Ant Colony Algorithm (IACA) incorporating traffic-state feedback is proposed to mitigate slow convergence and local-optimum entrapment in conventional ACO. Within the ant-colony-based comparison, the IACA reduced the average computational time by 49.57% relative to the conventional ACA. Furthermore, under the evaluated SUMO evening-peak scenario, periodic dynamic guidance reduced the average travel time of the selected guided vehicles by 12.06% and increased their average speed by 27.07%. These results demonstrate the potential of prediction-guided dynamic rerouting under the evaluated simulation conditions. Full article

The existing research on digital–intelligent innovation collaboration networks has not effectively connected the evolution of real collaborative relationships with governance methods, and governance models often struggle to accommodate both empirically identified evolutionary mechanisms and sequential decision-making capability. Using Chinese digital–intelligent innovation co-patent data from 2016 to 2025, this study develops a machine learning analytical framework centered on relationship evolution and collaboration resilience governance and employs OSR-DDQN to optimize dynamic intervention strategies under limited budgets. The results show that state transitions in digital–intelligent innovation collaboration exhibit pronounced path specificity, with the innovation capability, relational capital, and technological complementarity displaying differentiated conditional associations across transition paths. Further simulation experiments show that, across different budget conditions, OSR-DDQN improves the mean reward by 11.56% to 12.83% relative to the best-performing standard deep Q-network. Within the specified governance environment, once the budget reaches a minimally sufficient level, further performance gains arise primarily from optimizing the action timing, intervention targets, and stopping decisions rather than from simply increasing governance resources. Full article

Timely vehicular sensing is important for traffic monitoring, cooperative driving, and road-safety management. High mobility, time-varying wireless conditions, and limited edge resources nevertheless make information freshness difficult to maintain. This paper studies age of information (AoI) minimization in a three-layer UAV-assisted edge network comprising vehicle devices (VDs), unmanned aerial vehicles (UAVs), and a cloud center (CC). VDs periodically generate sensor-data packets, UAVs provide mobile edge processing and data-relaying services, and the CC coordinates system-wide resource allocation. The joint optimization of sensor-data transmission, UAV movement, packet processing, computation offloading, and bandwidth allocation is formulated within a cooperative multi-agent framework. To solve this problem, we propose a collaborative heterogeneous federated actor–critic (CHFAC) framework. Its significance-aware federated learning mechanism evaluates local model updates according to update significance, alignment with the global learning direction, and training stability and uses the resulting contribution scores for non-uniform agent selection and contribution-weighted aggregation. In the considered simulation setting, evaluation over 1000 test episodes yields an average AoI of $7.45\pm 1.65$ and a worst-case AoI of $38.72\pm 24.06$. The average AoI is $79.0\%$, $63.9\%$, and $29.2\%$ lower than that obtained by the implemented HF-MARL, H-MAAC, and non-federated baselines, respectively. These results demonstrate the effectiveness of CHFAC for freshness-aware vehicular sensing in dynamic UAV-assisted edge environments. Full article

The widespread adoption of autonomous vehicle technologies requires accurate predictions of coordinated multi-agent motion. While predicting such motion under arbitrary control mechanisms is generally intractable, this paper focuses on certain classes of optimal control where the system dynamics reduce to Lie-Poisson equations. In this context, the goal of the paper is to learn the dynamics solely from data, without prior knowledge of the control Hamiltonian or the inter-agent interaction laws. The main achievement of this paper is the introduction of Control Optimal Lie-Poisson Neural Networks (CO-LPNets), which are built from a composition of Poisson maps. By design, CO-LPNets preserve the system’s Casimir invariants to machine precision. The paper also demonstrates the completeness of these neural networks and highlights their representational efficiency. CO-LPNets are applied to systems of interacting particles on the $\mathrm{SO}\left(3\right)$ and $\mathrm{SE}\left(3\right)$ Lie groups, modeling coupled rigid body rotations and the spatial navigation of unmanned vehicles, respectively. Numerical evaluations confirm that CO-LPNets accurately learn the global phase-space dynamics from sparse data, faithfully reproducing trajectories over hundreds of time steps. Furthermore, the paper demonstrates the robustness of the architecture against observational noise. Requiring minimal training data (∼200 points per dimension) and highly compact architectures (∼1000 parameters), CO-LPNets offer a highly efficient, structure-preserving solution well-suited for practical edge deployment in autonomous systems. Full article

To investigate the performance degradation patterns and underlying damage mechanisms of permeable concrete under freeze–thaw cycles in cold regions, permeable concrete with varying porosities was selected as the research subject. A total of 120 rapid low-temperature freeze–thaw cycles were conducted. The evolution of porosity, mass loss, skid resistance, permeability, and compressive strength was systematically analyzed. Exploratory numerical simulations, conducted under idealized assumptions, suggest that rising porosity may reduce effective thermal conductivity, extend phase-change duration, and amplify internal temperature gradients—trends that are consistent with the observed porosity-dependent frost damage but require experimental temperature validation for quantitative confirmation. With the increase in freeze–thaw cycles, mass loss and porosity continuously increase, while compressive strength and permeability gradually decrease. After 120 cycles, the mass loss of all specimen groups was below 1%, with compressive strength decreasing by 5.5% to 12.9%. Despite this, the specimens maintained good permeability and skid resistance. Numerical simulations indicate that permeable concrete exhibits a three-stage temperature response during both freezing and thawing processes. An increase in porosity reduces the material’s effective thermal conductivity, prolongs the phase transition duration, and intensifies the internal temperature gradient, thereby amplifying the thermo–mechanical coupling damage effects. Therefore, optimizing the pore structure is crucial for improving the long-term service performance of permeable pavements in cold regions. Full article

This article addresses real-time local conflict resolution for a self-planning UAV operating in a three-dimensional dynamic environment with surrounding UAVs. To this end, we develop an SBA–DWA hybrid planning framework in which an improved sticky bacteria algorithm (SBA) is embedded in the dynamic window approach (DWA) to enhance real-time velocity selection for the self-planning UAV. First, a chemotaxis operator with projection is developed to strictly constrain bacterial positions within the convex dynamic window. Furthermore, an anisotropic Gaussian adhesion potential field is proposed to adaptively guide the current population search using historical optimal velocity commands, achieving cross-step memory transfer. Then, a dynamic pruning mechanism is designed to ensure that historical memory does not lead UAVs into infeasible or hazardous regions. The proposed scheme guarantees that the single-step planning latency satisfies stringent real-time requirements. Comparative simulation results demonstrate that the proposed method reduces path length by approximately 30% and planning time by approximately 31% compared with the standard DWA, while achieving a larger minimum inter-vehicle clearance in dense dynamic scenarios. Full article