Search Results (10,296)

Underwater imaging is affected by spatially varying blur caused by water flow turbulence, light scattering, and camera motion, resulting in severe visual quality loss and diminished performance in downstream vision tasks. Although numerous underwater image enhancement methods have been proposed, the issue of addressing non-uniform blur under realistic underwater conditions remains largely underexplored. To bridge this gap, we propose PMSPNet, a Progressive Multi-Scale Perception Network, designed to handle underwater non-uniform blur. The network integrates a Hybrid Interaction Attention Module to enable precise modeling of feature ambiguity directions and regional disparities. In addition, a Progressive Motion-Aware Perception Branch is employed to capture spatial orientation variations in blurred regions, progressively refining the localization of blur-related features. A Progressive Feature Feedback Block is incorporated to enhance reconstruction quality by leveraging iterative feature feedback across scales. To facilitate robust evaluation, we construct the Non-uniform Underwater Blur Benchmark, which comprises diverse real-world blur patterns. Extensive experiments on multiple real-world underwater datasets demonstrate that PMSPNet consistently surpasses state-of-the-art methods, achieving on average 25.51 dB PSNR and an inference speed of 0.01 s, which provides high-quality visual perception and downstream application input from underwater sensors for underwater robots, marine ecological monitoring, and inspection tasks. Full article

Cyclic solvent injection (CSI) with CO₂ is a promising non-thermal enhanced oil recovery (EOR) method for heavy oil reservoirs that also supports CO₂ sequestration. However, its effectiveness is limited by short foamy oil flow durations and low CO₂ utilization. This study explores how waterflooding and nanoparticle-assisted flooding can enhance CO₂-CSI performance through experimental and numerical approaches. Three sandpack experiments were conducted: (1) a baseline CO₂-CSI process, (2) a waterflood-assisted CSI process, and (3) a hybrid sequence integrating CSI, waterflooding, and nanoparticle flooding. The results show that waterflooding prior to CSI increased oil recovery from 30.9% to 38.9% under high-pressure conditions and from 26.9% to 28.8% under low pressure, while also extending production duration. When normalized to the oil saturation at the start of CSI, the Effective Recovery Index (ERI) increased significantly, confirming improved per-unit recovery efficiency, while nanoparticle flooding further contributed an additional 5.9% recovery by stabilizing CO₂ foam. The CO₂-CSI process achieved a maximum CO₂ sequestration rate of up to 5.8% per cycle, which exhibited a positive correlation with oil production. Numerical simulation achieved satisfactory history matching and captured key trends such as changes in relative permeability and gas saturation. Overall, the integrated CSI strategy achieved a total oil recovery factor of approximately 70% and improved CO₂ sequestration efficiency. This work demonstrates that combining waterflooding and nanoparticle injection with CO₂-CSI can enhance both oil recovery and CO₂ sequestration, offering a framework for optimizing low-carbon EOR processes. Full article

Studies highlight the importance of physical activity (PA) in relation to positive psychological functioning (PPF) among adults. Physical inactivity is strongly associated with lower levels of PPF, supporting the idea that lifestyle choices can be identified as a public health concern. There is growing evidence of the health benefits of regular PA. This study aims to analyze the contribution of PA to flow, PPF, and well-being. The sample consisted of 226 adults aged between 18 and 65 years (M = 41.23; SD = 12.50), mostly female (70.35%), with 56% reporting regular PA. Results revealed significant differences favoring active individuals over sedentary participants in all dimensions of flow, except for loss of self-consciousness. Regular PA was associated with higher levels of flow and psychological well-being. Both regular and intensive PA, as well as environmental mastery (EM), were key contributors to flow experiences, while self-acceptance and EM were central contributors to the Live Well Index. These findings support the association between PA and lower likelihood of sedentary lifestyles, emphasize its benefits for well-being, and highlight the association between PPF and active lifestyle patterns. Full article

The interaction between the left ventricle (LV) and aorta is critical for cardiovascular performance, particularly under pathophysiological conditions. However, how changes in LV mechanics, including preload and afterload, affect aortic function via LV–aorta interactions remains poorly understood due to the challenges associated with varying loading conditions in vivo. To overcome these limitations, the effects of varying LV preload or afterload on LV and aortic functions via LV–aorta interactions are quantified using ex vivo beating rat heart experiments in this study. In five healthy rat hearts under retrograde Langendorff and antegrade working heart perfusion, LV pressure, volume, aortic pressure, and aortic blood flow were measured. Key findings include the following: (1) under Langendorff perfusion, aortic flow increased linearly with LV developed pressure (DP), with a slope of 4.04 mmHg·min/mL; under working heart constant-pressure perfusion (2) a 12.4% increase in afterload decreased aortic flow by 58.8%, indicating that elevated aortic pressure significantly impedes aortic flow; (3) a 10.4% increase in preload enhanced aortic flow by 44.2%, driven primarily by an increase in LV DP that promoted forward flow. These results suggest that aortic pressure predominantly influences aortic flow under varying afterload conditions, whereas LV DP plays the dominant role in regulating aortic flow under different preload conditions. These findings demonstrate that the heart’s loading conditions strongly impact aortic blood flow. Specifically, elevated LV afterload can severely limit forward blood flow, while increased LV filling with increased LV preload can enhance blood flow, highlighting the importance of managing both afterload and preload in conditions such as hypertension and heart failure with preserved ejection fraction. This pilot study also established the feasibility of experimental platforms for coronary and ventricular function analysis. Full article

Sand disasters significantly restrict ecological restoration and the development of sustainable transport infrastructure in desert areas, and the impact of varying subgrade width and roughness caused by different types and uses of routes on the wind-sand environment is still unclear. To address this, four typical subgrade widths were studied, and wind tunnel experiments were carried out using models. Near the ground surface (at heights < 8.3 cm), a 3.5 cm wide subgrade had a greater effect on the windward wind speed compared with three other widths. The distance required for wind speed recovery on the leeward of the 3.5 cm wide subgrade was greater than that for the three other widths. The 3.5 cm wide subgrade had a larger effect range and extent on the leeward wind flow field compared with the three other widths. The distance needed for the leeward wind flow field to recover at the 3.5 cm wide subgrade was also greater than that for the three other widths. The sand transport rates for the 14, 26, and 41 cm wide subgrades were similar and showed a consistent trend. However, the sand transport rate for the 3.5 cm wide subgrade was more variable and was lower than that for the three other widths at near-ground surface heights but higher at intermediate heights. Width has a minor effect on the wind-sand environment around the subgrades compared to roughness. The research findings provide insights into the relationship between the subgrade width, roughness, and wind–sand environment, offering guidance for mitigating sand disasters along transportation routes. It provides theoretical support for optimizing transportation infrastructure design, promoting green and low-carbon construction, and promoting ecological restoration around the routes. Full article

Background: Vascular malformations are relatively common in children. Current therapeutic strategies include observation, medical therapy, sclerotherapy or embolization, laser therapy, cryoablation, and surgery, depending on the type and anatomical location of the malformation. Surgery is commonly limited to small and/or circumscribed lesions, to debulking in case of large volumes, or in drug-resistant cases. Sclerotherapy is a minimally invasive treatment generally used to treat dysplastic vasculature and to significantly improve patients’ symptoms. Herein, we describe our preliminary experience with bleomycin electrosclerotherapy (BEST) in the treatment of peripheral low-flow venous and lymphatic malformations in the pediatric population. Methods: We prospectively collected and analyzed data from patients who underwent BEST for peripheral low-flow vascular malformations (venous and lymphatic) and were treated at our institution from May 2022 onward. Results: Twelve patients (4 boys and 8 girls) with peripheral low-flow vascular malformations who underwent BEST were enrolled in this preliminary study. The median patient age at the first procedure was 81 months (IQR = 46–128). The most frequent anomaly was peripheral low-flow venous malformation. No relevant postoperative complications were encountered in any of the patients. All patients underwent a clinical evaluation of the malformation 1 month after the procedure. A clinical and ultrasonographic evaluation of the malformation was performed 2 months after the procedure to determine whether to repeat BEST. In cases of clinical resolution, a second ultrasonographic evaluation was performed 6 months after the procedure. Conclusions: BEST appears to be a promising and safe option for treating peripheral low-flow vascular malformations in children. Further studies with a greater number of patients and longer follow-up periods are needed to confirm our preliminary experience. Full article

In the course of digitalization, the importance of modeling and simulating real-world processes in a computer is rapidly increasing. Simulations are now in everyday use in many areas. For example, simulations are used to gain a better understanding of the real experiment, to plan new experiments, or to analyze existing experiments. Simulations are now also increasingly being used as an essential component of a real measurement, usually as part of an inverse problem. To ensure confidence in the results of such virtual measurements, traceability and methods for evaluating uncertainty are needed. In this paper, the challenges and benefits inherent to virtual metrology techniques are shown using three examples from different metrological fields: the virtual coordinate measuring machine, the tilted-wave interferometer, and the virtual flow meter. Full article

Tight oil is an important resource replacement in the petroleum industry, with the reserves of Type-II energy accounting for over 40%. However, these reservoirs have small pore throats and complex structures, and their wettability directly affects the EOR by affecting the occurrence of crude oil and multiphase flow mechanisms. In response to an unclear understanding of the evolution mechanism of wettability after energy replenishment in tight reservoirs with different reservoir formation conditions, the evolution law of wettability in different energy replenishment media for tight type-II reservoirs is evaluated by performing wettability experiments and nuclear magnetic resonance experiments, and the mechanism of differential changes in wettability after energy replenishment in different media is elucidated. The results show that the block with well-developed pores and good connectivity (Block: Z401) had the smallest in situ wetting angle, ranging from 27.1° to 30.4°, and that the interface effect had a small impact, resulting in a small change in the wetting angle after energy replenishment. The wetting angle of the developmental intersection block (Block: G93) is the highest, ranging from 36.6° to 46.4°. The connected pore and throats fully interact with the medium at the interface, resulting in a significant change in the wetting angle. In addition, after natural gas energy supplementation, the principle of similar solubility causes a significant change in the wetting angle of the pore throat interface after adsorption, with a maximum angle of 19.6°. The change in the wetting angle change of the CO₂ mixed-phase principle is in the middle, at about 13.6°, while the change in the wetting angle is minimal after N₂ replenishment, around 10°. The research results improve our understanding of the basic theory of tight oil supplementary energy development and have important practical significance. Full article

The strong coupling of the six-degree-of-freedom (6-DoF) electro-hydraulic Stewart parallel mechanism manifests as adjusting the elongation of one actuator potentially inducing motion in multiple degrees of freedom of the platform, i.e., a change in pose; this pose change leads to time-varying and unbalanced load forces (disturbance inputs) on the six hydraulic actuators; unbalanced load forces exacerbate the time-varying nature of the acceleration and velocity of the six hydraulic actuators, causing instantaneous changes in the pressure and flow rate of the electro-hydraulic system, thereby enhancing the pressure–flow nonlinearity of the hydraulic actuators. Considering the advantage of artificial intelligence in learning hidden patterns within complex environments (strong coupling and strong nonlinearity), this paper proposes a reinforcement learning motion control algorithm based on deep deterministic policy gradient (DDPG). Firstly, the static/dynamic coordinate system transformation matrix of the electro-hydraulic Stewart parallel mechanism is established, and the inverse kinematic model and inverse dynamic model are derived. Secondly, a DDPG algorithm framework incorporating an Actor–Critic network structure is constructed, designing the agent’s state observation space, action space, and a position-error-based reward function, while employing experience replay and target network mechanisms to optimize the training process. Finally, a simulation model is built on the MATLAB 2024b platform, applying variable-amplitude variable-frequency sinusoidal input signals to all 6 degrees of freedom for dynamic characteristic analysis and performance evaluation under the strong coupling and strong nonlinear operating conditions of the electro-hydraulic Stewart parallel mechanism; the DDPG agent dynamically adjusts the proportional, integral, and derivative gains of six PID controllers through interactive trial-and-error learning. Simulation results indicate that compared to the traditional PID control algorithm, the DDPG-PID control algorithm significantly improves the tracking accuracy of all six hydraulic cylinders, with the maximum position error reduced by over 40.00%, achieving high-precision tracking control of variable-amplitude variable-frequency trajectories in all 6 degrees of freedom for the electro-hydraulic Stewart parallel mechanism. Full article

Underground pressurized pipe leakage can induce sand fluidization, leading to ground collapses in urban areas. Additionally, the defluidization process is one of the main causes of sinkholes. In this study, a physical model test was conducted to examine sand bed fluidization and defluidization through a slot, which allowed precise control of the water flow rate in increments of 10 mL/s. The sand layer movement during the experiments was recorded, and the pressure field was accurately measured. The fluidization and defluidization processes were classified into five stages: fluidization static bed, internal fluidization, surface fluidization, internal defluidization, and defluidization static bed. Subsequently, the static bed stage included slow fluidization and fast fluidization, with the former driven by seepage and the latter involving densification and upward movement of sand particles above the orifice. Fluidization initiated at 240 mL/s when the sand particles near the orifice were compressed to approximately minimum porosity 0.37. The head losses comprised orifice head loss, seepage head loss, and vortex head loss, each exhibiting different variation patterns with the water flow rate. Hysteresis was observed in the cavity height curve, attributed to the arching effect. The findings of this study contribute to a more comprehensive understanding of effective strategies for preventing ground collapse. Full article

Advances in satellite miniaturisation have led to a steep rise in the number of Earth-observation platforms, turning the downlink of the resulting high-volume remote-sensing data into a critical bottleneck. Low-Earth-Orbit (LEO) communication constellations offer a high-throughput relay for these data, yet also introduce intricate scheduling requirements. We term the associated task the Remote Sensing Data Transmission in Communication Constellations (DTIC) problem, which comprises two sequential stages: inter-satellite routing, and satellite-to-ground delivery. This problem can be cast as a Hybrid Flow Shop Scheduling Problem (HFSP). Unlike the classical HFSP, every processor (e.g., ground antenna) in DTIC can simultaneously accommodate multiple jobs (data packets), subject to two-dimensional spatial constraints. This gives rise to a new variant that we call the Hybrid Flow Shop Problem with Two-Dimensional Processor Space (HFSP-2D). After an in-depth investigation of the characteristics of this HFSP-2D, we propose a constructive heuristic, denoted NEHedd-2D, and a Two-Stage Memetic Algorithm (TSMA) that integrates an Inter-Processor Job-Swapping (IPJS) operator and an Intra-Processor Job-Swapping (IAJS) operator. Computational experiments indicate that when TSMA is initialized with the solution produced by NEHedd-2D, the algorithm attains the optimal solutions for small-sized instances and consistently outperforms all benchmark algorithms across problems of every size. Full article

Sharing bottleneck bandwidth among TCP flows with diverse round-trip times (RTTs) remains a persistent challenge. This study investigates RTT unfairness and evaluates the behavior of two widely deployed congestion control algorithms, TCP Cubic and TCP BBR, under a variety of scenarios. The main objective is to better understand the underlying causes of RTT-based throughput disparity and to identify network configurations that promote fair bandwidth sharing. Using the Mininet emulation platform, extensive experiments were conducted to examine the effects of buffer size, sender distribution, and delay asymmetry on transmission performance metrics. The results show that while TCP BBR achieves high utilization with minimal buffering, its fairness depends on the interaction between RTT and buffer size. On the other hand, TCP Cubic achieves better fairness when moderate buffer sizes are provisioned and bandwidth imbalance is driven mostly by RTT ratio. These findings suggest that careful buffer sizing can reduce RTT unfairness and highlight the broader impact of queuing strategies on network performance. Full article

The present study investigates passive microdroplet generation in a T-junction microchannel using microscopic observations, microscale particle image velocimetry (Micro-PIV) visualization, and lattice Boltzmann simulations. The key flow regimes, i.e., dripping, threading, and parallel flow, are characterized by analyzing the balance between hydrodynamic forces and surface tension, revealing the critical role of the flow rate ratio of the continuous to dispersed fluids in regime transitions. Micro-PIV visualizes velocity fields and vortex structures during droplet formation, while a lattice Boltzmann model with wetting boundary conditions captures interface deformation and flow dynamics, showing good agreement with experiments in the dripping and threading regimes but discrepancies in the parallel flow regime due to neglected surface roughness. The present experimental results highlight non-monotonic trends in the maximum head interface and breakup positions of the dispersed fluid under various flow rates, reflecting the competition between the squeezing and shearing forces of the continuous fluid and the hydrodynamic and surface tension forces of the dispersed fluid. Quantitative analysis shows that the droplet size increases with the flow rate of continuous fluid but decreases with the flow rate of dispersed fluid, while generation frequency rises monotonically with the flow rate of dispersed fluid. The dimensionless droplet length correlates with the flow rate ratio, enabling tunable control over droplet size and flow regimes. This work enhances understanding of T-junction microdroplet generation mechanisms, offering insights for applications in precision biology, material fabrication, and drug delivery. Full article

Knowledge tracing, the computational modeling of student learning progression through sequential educational interactions, represents a critical component for adaptive learning systems and personalized education platforms. However, existing approaches face a fundamental trade-off between predictive accuracy and interpretability: deep sequence models excel at capturing complex temporal dependencies in student interaction data but lack transparency in their decision-making processes, while probabilistic graphical models provide interpretable causal relationships but struggle with the complexity of real-world educational sequences. We propose a hybrid architecture that integrates transformer-based sequence modeling with structured Bayesian causal networks to overcome this limitation. Our dual-pathway design employs a transformer encoder to capture complex temporal patterns in student interaction sequences, while a differentiable Bayesian network explicitly models prerequisite relationships between knowledge components. These pathways are unified through a cross-attention mechanism that enables bidirectional information flow between temporal representations and causal structures. We introduce a joint training objective that simultaneously optimizes sequence prediction accuracy and causal graph consistency, ensuring learned temporal patterns align with interpretable domain knowledge. The model undergoes pre-training on 3.2 million student–problem interactions from diverse MOOCs to establish foundational representations, followed by domain-specific fine-tuning. Comprehensive experiments across mathematics, computer science, and language learning demonstrate substantial improvements: 8.7% increase in AUC over state-of-the-art knowledge tracing models (0.847 vs. 0.779), 12.3% reduction in RMSE for performance prediction, and 89.2% accuracy in discovering expert-validated prerequisite relationships. The model achieves a 0.763 F1-score for early at-risk student identification, outperforming baselines by 15.4%. This work demonstrates that sophisticated temporal modeling and interpretable causal reasoning can be effectively unified for educational applications. Full article

This work aims at improving the accuracy of ensemble streamflow forecasts at short-to-medium ranges with the conditional bias-penalized regression (CBPR)-aided Meteorological Ensemble Forecast Processor (MEFP) and streamflow data assimilation (DA). To assess the potential impact of the CBPR-aided MEFP and streamflow DA, or CBPR-DA, 20-yr hindcast experiments were carried out using the Global Ensemble Forecast System version 12 reforecast dataset for 46 locations in the service areas of 11 River Forecast Centers of the US NWS. The results show that, relative to the current practice of using the MEFP and no DA, or MEFP-NoDA, CBPR-DA improves the accuracy of ensemble forecasts of 3-day flow over lead times of 0 to 3 days by over 40% for 4 RFCs and by over 20% for 9 of the 11 RFCs. The margin of improvement is larger where the predictability of precipitation is larger and the hydrologic memory is stronger. As the lead time increases, the margin of improvement decreases but still exceeds 10% for the prediction of 14-day flow over lead times of 0 to 14 days for all but 3 RFCs. Full article