Search Results (456)

Generating realistic and controllable aerial images is important for building and evaluating remote sensing recognition systems, especially when real samples of rare aircraft types or dense airport layouts are limited. However, airplane synthesis remains challenging for generic generative models. Aircraft have rigid and symmetric structures, and airport scenes often contain many closely spaced instances; as a result, existing models tend to produce distorted wings and fuselages or merge adjacent airplanes into ambiguous shapes. To address these issues, we propose AirplaneGen, a skeleton-guided latent diffusion framework for multi-airplane remote sensing image generation. AirplaneGen represents each airplane with an editable eight-keypoint skeleton and uses skeleton-derived soft masks to separate instance-level refinement from background-context modeling during denoising. To support this task, we construct MARS20, a benchmark with 2778 high-resolution aerial scenes and 16,673 airplane instances annotated with skeletons, categories, and contextual descriptions. Experiments on MARS20 show that AirplaneGen improves image fidelity, geometric consistency, and instance separation over representative controllable generation methods. Full article

Junmu No.1 is a commercially important Chinese pig breed, yet stable in vitro models for investigating its muscle development mechanisms and genetic regulation remain lacking; this study aimed to establish an immortalized porcine satellite cell line from Junmu No.1 pigs to address this gap. Primary porcine satellite cells (PSCs) were isolated from a 2-day-old Junmu No.1 piglet and immortalized via lentiviral transduction using the pHAGE-EF1α-eGFP-SV40LT-BleoR vector. The resulting cell line (imPSC-JM) was characterized for morphology, satellite cell marker expression, karyotype stability, myogenic differentiation capacity, and long-term proliferative potential, and RNA sequencing combined with Gene Set Enrichment Analysis (GSEA) was performed to assess transcriptomic fidelity relative to primary PSCs. The imPSC-JM line retained characteristic spindle-shaped satellite cell morphology, consistently expressed PAX7, maintained a normal diploid karyotype (2n = 38, XY), and showed stable SV40 large T antigen expression, enabling sustained proliferation exceeding 100 cumulative population doublings while preserving myogenic differentiation and the formation of multinucleated myotubes expressing Desmin, MYHC, and DMD. Transcriptomic profiles were highly concordant with primary PSCs (Pearson r ≥ 0.95; R² = 0.9188; 83.8% of expressed genes unchanged), with key satellite-cell and myogenic regulator genes (PAX7, MYOD1, MYF5, MYOG, MYF6) unaltered, while GSEA revealed upregulation of autophagy and inflammatory signaling and downregulation of ribosome biogenesis. The imPSC-JM line thus provides a reliable experimental platform with high transcriptomic fidelity for studying muscle development and genetic regulation in Junmu No.1 pigs. Full article

Point cloud completion from partial observations remains challenging due to the trade-off between preserving global structural consistency and recovering fine-grained local details, especially under severe incompleteness. We propose a symmetry-guided progressive refinement network to address this problem by learning flexible structural correspondences and progressively refining incomplete shapes. First, a Symmetry Graph Inference Network (SymGraphNet) constructs a feature-space graph over sampled keypoints and predicts symmetry-guided structural counterparts for robust coarse shape recovery, without explicitly estimating a rigid symmetry plane or axis. Second, a confidence-weighted Cross-Aware Decoder adaptively fuses partial-observation features and symmetry-guided features to balance visible-region fidelity and missing-region completion. Third, a multi-stage residual refinement strategy progressively improves geometric fidelity, local continuity, and point distribution uniformity. Experiments on PCN, MVP, and KITTI datasets demonstrate consistent improvements over representative state-of-the-art methods under both synthetic and real-world incomplete point cloud settings. Full article

Stropharia rugosoannulata mycelium is a naturally fibrous and sustainable protein source for meat analogs; however, its weak gel-forming ability and poor extrudability limit its printability and structural stability. In this study, extrusion-based 3D-printable composite inks were developed using mechanically fragmented mycelium, pea protein isolate (PPI), and curdlan (CUR). The effects of mycelium and CUR concentrations on printability, rheological properties, water-holding capacity, water distribution, thermal properties, and texture were systematically evaluated. The results showed that mechanical fragmentation for 20 s effectively dispersed the mycelial aggregates while preserving the filamentous network. CUR markedly improved extrusion continuity, print accuracy, and shape fidelity after deposition. All inks exhibited shear-thinning behavior. Increasing CUR concentration enhanced apparent viscosity, storage modulus, thixotropic recovery, water-holding capacity, and thermal stability, while converting part of the immobilized water into bound water within the gel network. In addition, CUR strengthened hydrogen bonding in the composite inks. Texture profile analysis of heated meat analogs showed that hardness, springiness, cohesiveness, gumminess, chewiness, and resilience increased progressively with increasing CUR concentration. Among the tested formulations, the ink containing 50% mycelium, 5% PPI, and 6% CUR exhibited the best balance between printability, structural stability, and meat-like texture, showing the closest textural similarity to boiled chicken breast. These findings provide a practical strategy for fabricating mycelium-based meat analogs with improved printability and meat-like texture. Full article

This study presents a discrete–continuous flux-guided shape-refinement framework for freeform shell geometries under self-weight. The method evaluates the directional relation between a prescribed support-directed transmission field and the shell surface normal, identifies locally underperforming regions, applies top-down geometric updates, and reconstructs a continuous surface at each step. It is intended as a transparent intermediate stage between intuitive freeform design and high-fidelity structural verification. The framework is demonstrated on nine shell cases with different geometries, support conditions, height ranges, and surface irregularities. Across all the cases, the results show reduced normal-component misalignment and increased tangential alignment relative to the prescribed transmission field. A representative finite-element comparison provides case-specific supporting evidence that under a linear-elastic gravity-load model the refined geometry can reduce deformation and stress levels over large surface regions; however, it does not prove general structural optimality or fully membrane-dominated behavior. Geometric roughness remains a key limitation requiring explicit regularization in future work. The approach is positioned as a lightweight geometric pre-optimization tool for conceptual shell design, rather than as a substitute for equilibrium-based form-finding or detailed structural optimization. Full article

With the increasing prevalence of uncertainties and variability in modern energy systems, model predictive control (MPC) often faces the challenge of predictive model mismatch. This paper proposes a desired-dynamics-based predictive control (DDPC) framework, in which an inner shaping layer is introduced to transform the raw plant into a desired dynamic model for the outer MPC. A unified design methodology is developed, including equivalent-model construction, desired-dynamics selection, and two inner-layer realizations based on desired dynamic equation (DDE)-PID and active disturbance rejection control (ADRC). In this way, the prediction model used by MPC is no longer the original uncertain plant but an explicitly shaped equivalent model determined by inner-layer controller parameters. The proposed method is validated on linear and nonlinear benchmark plants, together with frequency-domain and Monte Carlo robustness analyses. Results show that DDPC improves disturbance-rejection ability and enhances robustness against model mismatch and parameter perturbations. Further evaluation on the superheated steam temperature loop of a high-fidelity 660 MW coal-fired boiler hardware-in-the-loop simulator shows that DDPC reduces the peak-to-peak temperature fluctuation from 22.88 °C to 11.39 °C in the deep peak shaving scenario, corresponding to a 50.2% reduction relative to standard MPC. Full article

Dynamic three-dimensional (3-D) reconstruction of small objects moving at high speed is fundamentally limited by the number of viewpoints that a fixed camera array can provide at any single time instant. When the camera count is insufficient, single-frame multi-view stereo produces incomplete or inaccurate geometry. This paper proposes a multi-frame temporal integration approach that overcomes this limitation by exploiting the rigid-body assumption: because a falling object maintains its shape across consecutive frames, images captured at different time instants can be combined into a single, viewpoint-enriched reconstruction. A three-layer circular array of 32 synchronized RGB cameras captures 1440 × 1080 images at 160 fps, and a free-fall-oriented algorithm automatically detects active frames, selects informative temporal windows, and feeds the accumulated multi-frame images into a structure-from-motion and multi-view stereo (SfM-MVS) pipeline, effectively multiplying the number of viewpoints without additional hardware. The algorithm simultaneously recovers the 6-DOF pose trajectory of each object from the SfM-estimated camera parameters. Progressive accumulation experiments on freely falling soybeans (approximately 9–10 mm diameter) show that a single 32-camera frame already achieves an F-score exceeding 0.97 at a 0.5 mm threshold against an industrial structured-light scanner reference, and that accumulating additional temporal frames reaches a stable convergence plateau with both objects reaching a plateau F-score of 0.984. Beyond approximately one to two accumulated frames, additional frames yield diminishing returns, confirming that a small number of temporal frames is sufficient for convergent sub-millimeter accuracy. Across 30 independent free-fall trials with three objects, the system achieves an overall mean error of $0.146\pm 0.033$ mm and an overall F-score of $0.980\pm 0.006$—a mean relative error of approximately 1.6% on 8–10 mm targets—and fine surface features such as structural cracks are resolved at a fidelity sufficient for visual defect identification. These results establish rigid-body multi-frame temporal integration as an effective strategy for high-throughput, non-contact 3-D inspection of small objects in motion. Full article

Pumping stations serve as the foundation platform for large-scale vertical fluid machinery, and their structural dynamics directly govern the vibration levels and long-term reliability of the installed pump units. In low-head vertical pumping stations, the interaction among the massive underwater substructure, flexible above-ground powerhouse, and surrounding backfill soil creates a complex dynamic system whose behavior remains insufficiently characterized. This study presents a comprehensive dynamic analysis of a large-scale vertical pumping station using a high-fidelity three-dimensional finite element model that incorporates the powerhouse superstructure, submerged concrete substructure, and backfill soil. Modal analysis under four boundary condition scenarios—varying in soil participation and interface contact conditions—systematically quantifies the influence of soil–structure interaction on natural frequencies and mode shapes. Resonance verification against three primary excitation sources—rotational frequency (4.917 Hz), blade passage frequency (24.583 Hz), and rotor–stator interaction frequency (196.667 Hz)—is extended from the first 50 modes to the 400th mode to assess potential high-order resonance risks. Results show that the roof slab, with its large span and low stiffness, exhibits the highest vibration susceptibility. For the rotational frequency, modes 4–12 fall below the 20% code-specified safety margin but rapidly exceed the threshold thereafter. For the blade passage frequency, the separation ratio decreases progressively with increasing mode order within the first 50 modes, and the extended analysis up to the 400th mode shows that the separation ratio remains well above 20% throughout modes 51–400. Consequently, no substantial resonance risk exists for the blade passage frequency within the entire computed range. The rotor–stator interaction frequency remains safely separated with margins exceeding 95%. These findings demonstrate the profound influence of soil–structure interaction and confirm that, despite a decreasing trend in frequency separation at higher orders, the blade passage frequency poses no substantial resonance risk up to the 400th mode. This work provides a rigorous analytical framework for vibration-informed design and optimization of pump foundation systems, with direct implications for the reliability and operational safety of large-scale vertical fluid machinery. Full article

Extrusion-based three-dimensional food printing requires inks that can be smoothly extruded while maintaining sufficient structural stability after deposition. In this study, gelatin and κ-carrageenan were first mixed and then subjected to post-mixing pH regulation before spray drying, producing composite powders with different structural states. These powders were incorporated into yellow peach pulp gels to prepare fruit-based printing inks, and their printing performance, extrusion behavior, mechanical properties, particle-size distribution, and microstructure were systematically evaluated. The results showed that the structural state formed during gelatin–κ-carrageenan powder preparation was closely associated with the extrusion stability and shape retention of the final inks. Among the tested formulations, the ink prepared with gelatin–κ-carrageenan powder pre-regulated to pH 4.0 exhibited the best overall printability. Although its pore-area fidelity was slightly lower than that of the sample pre-regulated to pH 3.5, it produced more stable multilayer cylinders and better-defined lattice structures. In addition, the pH 4.0 sample showed the lowest and most stable extrusion force and the highest Young’s modulus, indicating a favorable balance between extrusion flowability and post-deposition support. Microstructural observations and particle-size analysis suggested that pH regulation altered the aggregation state and local morphology of the gelatin–κ-carrageenan system. Samples prepared at higher pH values tended to form larger and less uniform aggregates, which was unfavorable for stable extrusion and shape retention. Overall, post-mixing pH regulation of gelatin–κ-carrageenan provides a practical strategy for improving the printing-related properties of fruit-based gel inks. Full article

This study presents a reinforcement learning (RL)-based framework for the aerodynamic optimization of the Lotte airship, combining mid-fidelity dynamic simulations with adaptive learning strategies. To address the complex nonlinear coupling between the hull shape and tail configuration, a staged, data-driven optimization strategy is developed. In the first stage, single-parameter RL experiments are conducted to independently analyze the aerodynamic sensitivity of key design variables. This conceptual stage isolates pure aerodynamic potential, focusing on the unconstrained optimization of the hull’s Bézier parameterized profile, alongside the individual sensitivities of empennage area, longitudinal shift, lift slope factor, and efficiency. These experiments yield a comprehensive sensitivity map, clarifying each parameter’s independent influence on the average lift-to-drag ratio ($\overline{L/D}$) of the airship. In the second stage, the obtained sensitivities are utilized to structure an integrated multi-parameter optimization scenario. Crucially, this unified environment integrates the hull and tail while enforcing rigorous longitudinal trim constraints via a dynamic bisection search. This forces the RL agent to balance system-level aerodynamic recovery against inevitable trim drag penalties. The proposed framework is implemented in MATLAB R2023b using the SILCROAD airship dynamics environment and trained by the Deep Deterministic Policy Gradient (DDPG) algorithm. Results demonstrate that the initial single-parameter sensitivity extraction not only accelerates algorithmic convergence but also significantly improves the interpretability and physical validity of the final trimmed full airship configuration. This hierarchical approach establishes a systematic path from isolated parameter understanding to holistic, physics-informed aerodynamic design, offering a transferable methodology for future autonomous airship optimization. Full article

To address the challenges of generating autonomous, high-quality control laws for high-performance Unmanned Aerial Vehicles (UAVs) performing long-horizon complex aerobatic maneuvers, specifically the difficulty of achieving energy-altitude closure, low-level control chattering, and low utilization of high-quality experience, this paper proposes an improved Soft Actor-Critic (SAC) algorithm incorporating a Quality-Aware Expert Pool (QAEP). Using the aerobatic loop maneuver as a representative scenario, this study explores the autonomous generation of control-surface manipulation policies comparable to those of skilled human pilots for agile fixed-wing UAVs. First, a singularity-free feature state representation and a rate-integrated action space are constructed. Combined with a symmetric shaping reward, these suppress control chattering at the physical level and achieve energy-altitude closure throughout the maneuver. Second, a dual-threshold expert pool driven by task reward and trajectory quality, together with a progressive mixed-sampling mechanism, is designed to effectively filter out low-quality samples and improve algorithmic convergence stability. Simulation experiments based on JSBSim with a high-fidelity F-16 model, which serves as a representative surrogate for a high-performance UAV, demonstrate that the proposed method generates maneuver strategies with manipulation quality comparable to that of skilled human pilots. The Dynamic Time Warping (DTW) similarity between the generated control commands and human expert demonstration data exceeds 0.97, the Manipulation Smoothness Index (MSI) is improved by 7.3%, and the loop completion rate under randomized initial conditions reaches 96.2%. These results suggest that the proposed framework enables human-like energy coordination and fine-grained control sequence generation in complex simulation environments, offering a promising approach to advancing maneuver intelligence and autonomous control capability in UAV systems. Full article

Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy because of its low radiation dose and on-board acquisition capability. However, CBCT images often suffer from scatter artifacts, increased noise, reduced soft-tissue contrast, and inaccurate Hounsfield Unit (HU) values, which limit their direct use for accurate dose calculation and quantitative analysis. To address this limitation, we propose a CBCT-to-CT synthesis framework based on 2.5D context encoding (concatenating five adjacent slices along the channel dimension) and latent-space variational diffusion. The proposed method combines a Vector Quantized Variational Autoencoder (VQ-VAE) and a U-shaped Vision Transformer (U-ViT)-based latent-space Variational Diffusion Model (VDM) to translate CBCT images into synthetic CT (sCT) images in a compressed latent space. To incorporate inter-slice anatomical context while preserving the computational efficiency of 2D processing, five adjacent CBCT slices are concatenated along the channel dimension and used as input. We evaluated the proposed method on the SynthRAD2025 paired CBCT-CT dataset covering head-and-neck, thoracic, and abdominal regions. Under the provided benchmark setting, quantitative evaluation on the validation set showed that the proposed 2.5D model improved peak signal-to-noise ratio (PSNR) from 25.39 dB to 27.44 dB (averaged across regions), structural similarity index measure (SSIM) from 0.813 to 0.846, reduced mean squared error (MSE) from 0.00313 to 0.00200, and lowered Fréchet inception distance (FID) from 1009.33 to 869.53 compared with the 2D baseline. Qualitative results also showed improved anatomical consistency and reduced artifact-related distortions. These findings suggest that neighboring-slice context can enhance HU fidelity and overall image quality in a computationally practical synthesis framework, supporting the usefulness of efficient AI-based cross-modality reconstruction for radiotherapy-related imaging workflows. Full article

Building healthy communities requires organizational arrangements that center on resident and community assets while using data to guide decisions. This study examines how the Evidence2Success framework was implemented in three communities, Kearns, UT, Mobile, AL, and Memphis, TN, to understand how citizen-led asset mapping, coalition processes, and funding strategies shape youth well-being efforts. Using an interpretive case-study design, we analyzed process-evaluation interviews, implementation milestones and benchmarks, strengths-and-concerns reports, and community case materials to trace how coalitions mobilized assets, reoriented institutional resources, and adapted evidence-based programs. The results show that broad, cross-sector Community Boards completed most implementation tasks, increased participation by people of color, and developed more inclusive decision-making structures that addressed historical inequities. Coalitions also strengthened data-use capacities, employing youth survey results and local qualitative input to select priorities, braid funding, and make culturally responsive adaptations while maintaining program fidelity. Overall, the findings suggest that when evidence-based planning frameworks are embedded within asset-based, resident-governed structures, communities can build sustainable organizational arrangements that support youth well-being and advance more equitable local systems. Full article

Children’s food choices are shaped early in life through cognitive, social, and environmental influences, yet relatively little is known about how school-based nutrition education supports these processes in routine settings. This study examined mechanisms potentially relevant to children’s food choices using a multi-source process evaluation of the municipal “I Know What I Eat” program implemented in Warsaw primary schools. A prospective observational implementation study was conducted in 81 public schools, covering 198 workshop cycles for students aged 8–9 years. Data were obtained from teacher-observers (n = 198), trained program implementers (n = 6), and implementation records. The evaluation focused on implementation quality, fidelity, acceptability, and mechanisms relevant to food-related decision-making. Quantitative data were analyzed using descriptive statistics, Kruskal–Wallis tests, and Spearman correlations; qualitative comments were examined using content analysis. The program was implemented with high quality and consistency, with mean ratings ranging from 4.88 to 4.96 on a five-point scale and no significant differences by implementer or class size. Qualitative findings indicated that experiential learning, practical food preparation, peer interaction, and active participation supported children’s engagement. These findings suggest that school-based nutrition education can create conditions relevant to food-related decision-making, although direct behavioral measures are needed. Full article

Integrating a heat exchanger (HEX) into the cooling duct of a high-power fuel-cell-based aircraft presents a critical trade-off between thermal performance and aerodynamic penalties. The present study addresses this challenge through the design and system-level analysis of a HEX integrated into the cooling duct. Developed as part of the Clean Aviation project FAME, the design features a rectangular inlet, a circular outlet, and a tilted HEX. The evaluation is performed using high-fidelity Large Eddy Simulations (LESs). The HEX is modeled with a porous media approach based on the Darcy–Forchheimer equation, while the simulations are carried out using a self-adapted version of the pisoFoam solver, termed pisoTempFoam, to account for heat transfer. The study reveals that while component-level design choices, such as a straight inlet and tilted HEX configuration, successfully mitigate local flow separation and duct-induced losses, a critical system-level performance issue emerges. The analysis demonstrates that the cooling duct design, when subjected to realistic operational conditions, generates the high pressure head to overcome the resistance of the HEX. The external aerodynamic analysis also indicates that the HEX resistance is a critical factor, and without overcoming it the system fails to capture the required air mass flow rate, compromising thermal management. The findings highlight the necessity to optimize the design, by an adapted duct shape or an auxiliary fan, to overcome the HEX-induced pressure drop. The porous media approach is thereby validated as an effective tool for rapid system-level design analysis, despite its inherent limitation in capturing detailed downstream turbulence. Full article