Search Results (3,691)

High quality factor (Q factor) resonant metasurfaces enable efficient mid-infrared (MIR) upconversion, yet their narrow operating bandwidths severely limit practical broadband detection and imaging applications. Although high Q magnetic toroidal dipole (MTD) modes exhibit outstanding momentum space ($k$-space) stability in linear optics, their application in nonlinear processes has primarily been confined to degenerate second-harmonic generation (SHG), leaving complex non-degenerate processes such as sum-frequency generation (SFG) largely unexplored. Here, we propose a tunable MIR upconversion platform based on an all-dielectric gallium phosphide (GaP) dimer metasurface. Breaking the in-plane symmetry to trigger Brillouin zone folding excites robust MTD quasi-guided modes (MTD-QGM), tightly confining the locally enhanced optical fields within the highly nonlinear GaP nanostructure. Synchronizing this high Q resonance with a spatially overlapping pump mode yields an exceptional SFG conversion efficiency of $7.9\times {10}^{-4}$, successfully translating a 3101.8 nm MIR signal to the 903 nm near-infrared band. Crucially, the intrinsic $k$-space stability of the MTD-QGM enables continuous, broadband upconversion through simple angle tuning. This mechanism effectively overcomes the narrow-band limitations characteristic of typical symmetry-protected resonators, establishing a robust paradigm for room-temperature MIR detection. Full article

Objective: The effects of fatigue on swimming propulsion are unclear. This study examined upper-limb propulsive force and bilateral coordination during constant-speed front crawl performed until exhaustion. Methods: Twelve competitive swimmers completed a visually paced front-crawl trial performed at a constant speed (95% of maximal speed) until volitional exhaustion. Upper-limb propulsion (pressure-derived) was quantified using wearable differential-pressure mini-paddles synchronized with high-speed video. Propulsive force and impulse were analyzed at ten standardized time points (10–100% of test duration), distinguishing the early (entry–catch–pull) phase and the push phase of the stroke cycle. Results: Total overall propulsive impulse (time-integral of propulsive force) and mean propulsive force decreased significantly as early as 30–40% of test duration, with the largest reductions occurring during the push phase. Interestingly, push-phase impulse declined earlier in the non-dominant left arm (from 20% of test duration) compared to the dominant right arm (from 40%), whereas force generated during the early phase did not change. Peak propulsive force decreased at later stages, while intra-cycle timing indices (peak timing and force centroid) and inter-limb asymmetry remained unchanged. Stroke frequency increased from mid-test onward and was strongly negatively associated with stroke efficiency (r = −0.79). Stroke efficiency correlated positively with push-phase impulse and peak force. Conclusions: During constant-speed front crawl performed to exhaustion, propulsion progressively declines, primarily through reduced force and impulse during the push phase rather than changes in the early (entry–catch–pull) phase or temporal and asymmetry-related variables. Increased stroke frequency initially compensates for declining propulsion but ultimately fails to maintain the imposed swimming velocity. Full article

The high penetration of renewables introduces stability challenges to modern power grids due to their intermittency and lack of inertia. Unlike conventional grid-following controls, this paper proposes a Virtual Synchronous Machine (VSM) control strategy for a three-level neutral-point clamped (NPC) battery energy storage system (BESS), enabling autonomous voltage and frequency support. The VSM control comprises voltage reference generation and voltage tracking. A step-by-step derivation of inverter output impedance is also provided, along with an analysis of how key parameters affect it. Simulation results in MATLAB R2021a/Simulink demonstrate excellent dynamic performance and grid-supporting functionalities, validating the effectiveness of the proposed design and the accuracy of the impedance analysis. Full article

Recent advances in extended reality (XR) have enabled immersive virtual flight experiences for applications such as entertainment and teleoperation support. However, XR-based flight systems that rely primarily on audiovisual cues often fail to evoke a compelling sense of flight and embodied sensation. This study investigates how adaptive wind feedback enhances subjective flight perception in a virtual flight simulation system, Drone Rider. We implemented direction- and velocity-adaptive wind feedback that synchronizes airflow intensity and direction with the user’s motion in the virtual environment, focusing on perceptual effects in a controlled manner to identify key design factors, rather than reproducing aerodynamically accurate airflow. To explore flexible system configurations, two fan installation positions were compared: front-mounted and bottom-mounted. A questionnaire-based user study revealed that adaptive wind feedback significantly enhanced the sense of flight, self-location, and agency compared with the constant-wind and no-wind conditions. However, no significant differences were observed between velocity-adaptive wind and direction- and velocity-adaptive wind conditions. Furthermore, wind delivered from beneath the user yielded flight sensations comparable to those generated by front-mounted airflow. These findings suggest that temporal coupling between airflow intensity and visual motion plays a central role in XR flight perception and provide practical design insights for immersive and flexible XR-based flight simulation systems. Full article

This study experimentally investigates the dynamics of air bubble plumes in water under varying thermal and hydrodynamic conditions using a two-dimensional Particle Image Velocimetry (PIV) system. The experimental setup consists of a transparent acrylic tank equipped with a bubble generator, a controlled heating system, and a synchronized PIV arrangement to capture both bubble motion and the induced liquid flow field. Experiments were conducted over a range of water temperatures (21–60 °C), air flow rates, and water depths (200–600 mm) to systematically quantify their coupled influence on bubble plume behavior. The results demonstrate that bubble rising velocity (defined here as the mean vertical, buoyancy-driven component of bubble motion measured in the fully developed plume region) increases with water temperature, gas flow rate, and water depth. For a fixed gas flow rate and water depth, increasing the water temperature from 40 °C to 60 °C resulted in an approximately twofold increase in bubble rising velocity, primarily due to reduced liquid viscosity and enhanced buoyancy forces. Bubble velocity also increased with gas flow rate and water depth, reflecting stronger momentum input and extended acceleration distances within taller water columns. PIV-resolved velocity fields further reveal that the surrounding fluid velocity increases proportionally with bubble rising velocity and temperature, confirming a strong coupling between bubble motion and plume-induced circulation. The surrounding liquid velocity reached approximately 30–60% of the corresponding bubble rising velocity, depending on operating conditions. These findings provide quantitative experimental insight into the coupled effects of thermal conditions, gas injection rate, and liquid depth on bubble–liquid interactions. The results contribute valuable validation data for multiphase flow modeling and offer practical relevance for thermal–hydraulic, chemical, and environmental engineering applications involving bubble-driven transport processes. Full article

Proton exchange membrane fuel cells (PEMFCs) have considerable potential for frequency support in grid-forming applications. However, their transient dispatchable power is nonlinearly influenced by operating conditions, such as the oxygen excess ratio and stack temperature, thereby weakening frequency support performance by delaying power compensation during disturbances. To address this issue, a coordinated control strategy for inertia support and active power compensation is proposed that explicitly accounts for operating-state effects. Based on a dynamic PEMFC model, the effects of the oxygen excess ratio and stack temperature on transient output capability are analyzed, and a jointly corrected inertia coefficient is introduced into the virtual synchronous generator (VSG) rotor motion equation to achieve adaptive adjustment of virtual inertia under varying operating conditions. In addition, model predictive control (MPC) is incorporated into the VSG control framework, and a performance index is formulated using weighted quadratic terms of frequency variation and input power, thereby enabling the compensation power to be determined online and the PEMFC power reference to be updated accordingly. Simulation results show that the proposed strategy can effectively suppress frequency fluctuations under disturbance conditions. Compared with Conventional PI-VSG, the maximum frequency deviation and the peak rate of change of frequency (ROCOF) are reduced by 49.1% and 62.1%, respectively. Full article

To address unstable localization and sparse mapping for autonomous vehicles operating in GPS-denied and low-visibility environments, this paper proposes GTS-SLAM, a tightly coupled dense visual SLAM framework integrating Generalized Iterative Closest Point (GICP) and 3D Gaussian Splatting (3DGS). The system is designed for intelligent driving platforms such as underground mining vehicles, inspection robots, and tunnel autonomous navigation systems. The front-end performs covariance-aware point-cloud registration using GICP to achieve robust pose estimation under low texture, dust interference, and dynamic disturbances. The back-end employs probabilistic dense mapping based on 3DGS, combined with scale regularization, scale alignment, and keyframe factor-graph optimization, enabling synchronized optimization of localization and mapping. A Compact-3DGS compression strategy further reduces memory usage while maintaining real-time performance. Experiments on public datasets and real underground-like scenarios demonstrate centimeter-level trajectory accuracy, high-quality dense reconstruction, and real-time rendering. The system provides reliable perception capability for vehicle autonomous navigation, obstacle avoidance, and path planning in confined and weak-light environments. Overall, the proposed framework offers a deployable solution for autonomous driving and mobile robots requiring accurate localization and dense environmental understanding in challenging conditions. Full article

Circulating tumor cells (CTCs) are valuable liquid-biopsy biomarkers, yet their extreme rarity makes high-purity, high-throughput enrichment challenging. In spiral inertial microfluidics, high cell loading induces long-range hydrodynamic interactions that broaden the focused blood-cell stream; consequently, a subpopulation completes the ~0.5 and ~1.0 Dean-cycle migrations with a phase delay, compressing the CTC–blood cell gap and degrading purity. Here we propose a Dean-cycle phase-regulated double-spiral design informed by this phenomenon. This design aims to mitigate the stream-broadening effect by boosting the Dean number during the first half-cycle to promote synchronized blood-cell migration and shifting the CTC equilibrium position near one full cycle to further widen the CTC–blood cell separation. We implement this strategy in a second-generation double-spiral microfluidic chip (SDMC) and scale it to a four-channel parallel array (ASDMC). Under optimized conditions, ASDMC processes diluted whole blood (hematocrit = 4%) without the need for red blood cell (RBC) lysis or antibody labeling, achieving a sample throughput of 1200 μL·min⁻¹. Specifically, it exhibits a mean recovery rate of 98.8% across three spiked tumor cell lines (MCF-7, PC-9, and Mahlavu) and a mean white blood cell (WBC) depletion efficiency of 93.3%. In a pilot clinical testing of 20 patients (NSCLC and HCC), enriched fractions enabled immunofluorescence identification of CK⁺CD45⁻DAPI⁺ CTCs, with an exploratory trend of increasing CTC counts with advanced disease stage (4–34 cells·mL⁻¹). These results describe a scalable, label-free platform, and the observed purification performance aligns with our proposed mechanism: Dean-cycle phase regulation to mitigate blood-cell migration lag. Our findings support further technical validation and clinical assessment in larger cohorts. Full article

There are still challenges in speech emotion recognition, as the representation capability of single-modal information is limited, there are difficulties in capturing continuous emotional transitions in discrete emotion annotations, and the issues of modal structural differences and cross-sample alignment in multimodal fusion methods persist. To address these, this study undertakes work from both data and model perspectives. For data, a Chinese multimodal database STEM-E²VA was constructed, synchronously collecting four modalities of data: articulatory kinematics, acoustics, glottal signals, and videos. This covers seven discrete emotion categories and employs PAD continuous annotation. By integrating discrete and continuous dimensional annotations, it better represents the distinction between strong and weak emotions under the same discrete emotion label. Concurrently, to process the biases in PAD annotations, we employed the SCL-90 psychological questionnaire to analyze annotators’ cognitive and emotional perceptions, thereby ensuring data reliability. For model, this paper proposes a multimodal supervised contrastive fusion network incorporating PAD perception. It employs a PAD-enhanced hybrid contrastive loss function to optimize intra-model and inter-modal feature alignment. Utilizing a cross-attention mechanism combined with a GRU–Transformer network for temporal feature extraction, it achieves deep fusion of multimodal information, reducing inter-modal discrepancies and cross-class confusion. Experiments demonstrate that the proposed method achieves 85.47% accuracy in discrete sentiment recognition on STEM-E²VA, with a substantial reduction in RMSE for PAD dimension prediction. It also exhibits excellent generalization capability on IEMOCAP, providing a novel framework for integrating discrete and continuous sentiment representations. Full article

Digital Twin (DT) technology, which enables the simulation, evaluation, and optimization of physical entities through synchronized digital replicas, has attracted increasing attention in the context of wireless networks. Among the various components involved, the radio propagation environment is fundamental to communication performance, making its accurate digital replication a critical challenge. This paper focuses on constructing a high-precision radio propagation environment DT using deep learning (DL) methods. While data-driven DL has become a mainstream solution for signal propagation prediction in DTs, its performance depends heavily on the model’s ability to learn intrinsic propagation patterns from data. Owing to the complex interactions between wireless signals and environmental obstacles, conventional DL models often struggle to efficiently capture implicit propagation laws solely from raw data. To address this issue, we propose a general methodology for incorporating expert knowledge of radio propagation into DL frameworks. Building upon the widely adopted encoder–decoder architecture, the proposed approach explicitly integrates theoretical propagation knowledge to enhance learning efficiency and prediction accuracy. Ablation experiments demonstrate that the inclusion of expert knowledge significantly improves the performance of DL-based radio environment DTs. This work highlights the potential of knowledge–data dual-driven DL as a promising direction for advancing radio propagation environment DTs. Full article

This paper has the goal of exploring the potential of electromagnetic propulsion systems based on tethers to create artificial Sun-synchronous orbits for remote sensing satellites, as well as performing station-keeping maneuvers and de-orbiting of the satellite after the end of its useful life. To create artificial Sun-synchronous orbits, the force is applied to keep the longitude of the ascending node with the same angular velocity of the apparent motion of the Sun around the Earth, which is the definition of a Sun-synchronous orbit. These orbits are very important for remote sensing satellites, because in these orbits the satellite passes by a given point at the same time, helping in analyzing the data collected. The use of electrodynamic tethers can extend the regions of Sun-synchronous orbits, both in terms of inclination and semi-major axis. To perform the de-orbiting of the satellite, the same tether can apply a force in the opposite direction of the motion of the satellite, so reducing its energy and decreasing the semi-major axis until the satellite crashes into the atmosphere of the Earth. This is very important to avoid increasing the presence of space debris in space, a very serious problem nowadays. For the station-keeping maneuvers, we just need to use the appropriate control laws, from time to time, to correct any errors in the Keplerian elements. A significant advantage of employing an electrodynamic tether over traditional thrusters is that it does not require consumption of fuel. The study assumes that a current can flow in both directions through the tether, so interacting with the magnetic field of the Earth to create the Lorentz force. The possibility of using electrodynamic tethers with autonomous charge generation, to avoid dependence on plasma densities and other external factors, is considered. The results presented here help in space and planetary science, since they give more options for remote sensing satellites, which are a key element in planetary science. Full article

This study elucidates the mechanism enabling the low-voltage electrolysis of black liquor (BL) for integrated resource recovery. The process simultaneously generates protons at the anode via the oxidation of organics (OOR), which occurs at a lower potential than the oxygen evolution reaction (OER), and induces lignin precipitation. Concurrently, hydrogen and hydroxide ions are produced at the cathode through the hydrogen evolution reaction (HER). Driven by the electric field, sodium ions migrate from the anode to the cathode chamber, combining with hydroxide ions to form sodium hydroxide, thereby achieving the synchronous production of acid, alkali, hydrogen, and modified lignin in a single process. Using a platinum electrode, we conducted a mechanistic investigation through linear sweep voltammetry (LSV), cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and detailed product analysis. The results show that overall efficiency is controlled by competition at the anode between OOR and OER, which directly determines proton yield. A critical trade-off exists between anodic proton generation and cathodic alkali recovery, driven by the competitive migration of protons and sodium ions across the cation-exchange membrane. The proton yield was highly dependent on the initial BL composition, with a characteristic peak observed under specific conditions. Conversely, the sodium hydroxide recovery rate was maximized when the anolyte pH remained high, minimizing competitive proton migration. This work provides fundamental insights into the interfacial mechanisms of BL electrocatalytic, establishing it as a versatile electrochemical biorefinery platform for simultaneous proton and alkali production from a renewable waste stream, beyond its role as a hydrogen source and lignin recovery. Full article

Vegetation resilience plays a key role in ecosystem stability as climate change and human disturbance intensify. We quantified resilience via AR(1) from kNDVI data over mainland China (2000–2024), and assessed its spatiotemporal patterns, long-term causal drivers (Causal Forest), and breakpoint-related mechanism shifts (non-stationary causal networks). Resilience varied strongly across space, with higher AR(1) values concentrated in northern transition belts and inland regions. Breakpoints clustered in 2010–2018 and showed broad synchronicity nationwide. Long-term effects were dominated by environmental background states: mean variables generally outweighed variability (CV) and memory terms, suggesting that persistent climate–environment conditions primarily shaped resilience gradients. Temperature emerged as the strongest national-scale control and acted asymmetrically across metrics—TMX strongly suppressed resilience, whereas TMN tended to enhance it—while precipitation and CO₂ gained importance regionally. Driver networks reorganized markedly across breakpoints, exhibiting high edge turnover and heterogeneous lag shifts—pointing to stage-dependent restructuring that goes beyond changes in driver strength. This framework links net effects with mechanism reorganization to help diagnose vegetation resilience under non-stationary conditions. Full article

Among lower-body techniques in karate, the Mawashi Geri Jodan is regarded as the most frequently applied, technically sophisticated, and potentially hazardous skill. Yet, whether karate athletes of varying proficiency levels exhibit differential mastery of this technique remains empirically unexamined. This study aimed to reveal movement control strategies of elite athletes by comparing kinematic and surface electromyography (sEMG) characteristics of Mawashi Geri Jodan between elite and sub-elite female karate practitioners. A total of eight female karate athletes (4 elite, 4 sub-elite) were recruited. During the execution of the dominant-leg Mawashi Geri Jodan, they struck a karate punching bag positioned at head height, while kinematic and sEMG data were synchronously collected. Analyzed metrics included phase durations, center of mass (COM) displacement, joint angles/angular velocities, and integral electromyography (IEMG) with muscle work percentage of 8 lower limb muscles. Independent-sample t-tests were used for intergroup comparisons (α = 0.05). Compared with the sub-elite group, elite athletes completed the full Mawashi Geri Jodan in significantly less time (0.825 ± 0.07 s vs. 1.030 ± 0.05 s, p < 0.01) and exhibited a shorter core striking phase (p < 0.05). Kinematically, elite athletes showed smaller vertical COM displacement during the striking phase (p < 0.05) and greater hip joint range of motion (p < 0.05). sEMG data revealed significantly higher activation of lower limb prime movers (vastus lateralis, gastrocnemius) during the striking phase and greater rectus femoris contribution during the recovery phase in elite athletes. Elite female karate practitioners demonstrate superior movement efficiency, body stability, and neuromuscular coordination in Mawashi Geri Jodan. Technical training should prioritize hip joint flexibility and stability, synergistic explosive force generation of the lower limb kinetic chain during the striking phase, and active rectus femoris activation during the recovery phase to enhance execution precision and efficiency. Full article

In view of the bottleneck problems existing in the 3D ultrasonic testing of aircraft composite laminated structures—including heavy reliance on manual operation, resulting in low detection efficiency, and the inability of traditional robotic arms to adapt to the testing of complex curved surfaces due to their dependence on predefined fixed trajectories—this paper proposes an automated 3D ultrasonic testing method based on 3D vision guidance for robotic arms. Firstly, the proposed Yolo-Mask model is adopted to realize the visual recognition and segmentation of composite component regions, after which the segmentation results are mapped to the depth map and further converted into the surface point cloud of the material. Secondly, on the basis of point cloud preprocessing and trajectory point extraction, the automatic planning of the robotic arm’s scanning trajectory is achieved, which drives the robotic arm to perform precise motion and to synchronously collect spatial pose and ultrasonic testing data. Finally, 3D reconstruction is completed via a fusion algorithm, and 3D images of the material’s internal structures are generated. Experimental verification shows that the proposed method achieves a Segm-mAP of 97.4%, a detection speed of 11.7 fps, and a 3D imaging error of less than 0.1 mm, thereby realizing fully automated detection throughout the entire process. This research provides an effective solution for the non-destructive testing of aircraft composite structures. Full article