Search Results (2,443)

This paper presents Regret-Guided Heuristic Decentralized Prioritized Planning with Velocity Decomposition (RG-HDP-VD), a physics-aware cooperative trajectory planning framework for heterogeneous Unmanned Aerial Vehicles (UAVs) relief delivery in post-earthquake, non-convex canyon environments. RG-HDP-VD addresses two prevalent failure modes: energy-inefficient congestion caused by ignoring time-varying payload dynamics, and the collapse of feasible sets due to strict arrival windows in fixed-speed planning. We construct a mass-augmented energy topology and use a mass-augmented energy-aware A* search to extract baseline physical metrics—path length, total energy, and unit-distance energy—for each UAV. Regret-Guided (RG) arbitration then quantifies the relative energy cost of waiting versus detouring at conflicts and grants right-of-way to heavy-load, high-cost platforms. These priorities are embedded into Heuristic Decentralized Prioritized Planning (HDP), which maintains a global spatiotemporal occupancy map and serializes planning to eliminate deadlocks. To satisfy tight time windows, Velocity Decomposition (VD) maps 4D temporal constraints into a 3D path-length feasible interval and is realized via an improved VD-TSRRT* sampling-based planner. In high-fidelity simulations, RG-HDP-VD demonstrates superior scalability over conventional methods, maintaining high success rates (up to 100%) in saturated scenarios, while reducing average planning time by ~45% and total system energy by 6.7%. Finally, real-world flight demonstrations using a heterogeneous quadrotor team validate the framework’s practical feasibility and robust hardware execution. Full article

Frequency-modulated continuous-wave (FMCW) radars are widely used for range and velocity estimation. However, conventional velocity measurement techniques based on 2D-FFT processing require a large number of chirps and suffer from a maximum unambiguous velocity limitation, which restricts their applicability to high-speed targets. This study addresses these challenges by proposing a hybrid FMCW chirp waveform that employs bandwidth variation between consecutive chirps while maintaining a constant chirp duration. The proposed method enables separation of range- and Doppler-dependent frequency components using only two chirps; thus, it improves the maximum velocity constraint by keeping intermediate-frequency bandwidth and sampling requirements low. In addition, spatial angle estimation is performed using an amplitude-comparison monopulse antenna configuration, allowing single-snapshot angle measurement with low computational complexity. To enhance measurement robustness, extended and unscented Kalman filters are integrated for target tracking. Simulation results demonstrate that the proposed waveform achieves accurate velocity estimation for very high-speed targets and that the unscented Kalman filter consistently outperforms the extended Kalman filter in terms of convergence speed and robustness, particularly under poor initialization and strong nonlinearities. The results confirm that the proposed framework provides an efficient solution for tracking a single, fast-moving, isolated target in a homogeneous environment using FMCW radar systems at short and medium ranges. Full article

A cross-construction method is proposed to establish a wind turbine wake dataset with significantly reduced computational fluid dynamics (CFD) costs. This method involves adjusting one operating parameter, such as the tip speed ratio (TSR), while maintaining the others at their optimal values. This procedure is repeated across another parameter (inflow velocity) to generate a sparse but informative dataset. CFD simulations were performed using large eddy simulation (LES) coupled with an actuator line model (ALM) to generate data. A pre-training and fine-tuning network based on error classification (PFNEC) was developed, achieving high prediction accuracy with coefficients of determination of 0.9750 and 0.9851 for two validation conditions. Two models based on a softmax function and a residual block were designed, and they achieved the best performance, with coefficients of determination of 0.9921 and 0.9891 under different conditions. The Fourier embedding was applied to enhance input features of neural networks. Four samples added to the original dataset improved the prediction accuracy for extreme operating conditions, from coefficient of determination values of 0.7143 and 0.7034 to 0.9939 and 0.9886 with Fourier embedding. This cross-construction method can significantly reduce the cost of dataset establishment. The models exhibited reliable generalization and prediction accuracy. Full article

Lanternfishes (Myctophidae) dominate mesopelagic ecosystems and play a central role in pelagic food webs through their high biomass and diel vertical migration, yet detailed information on their age structure and growth dynamics remains limited in the Northwest Pacific Ocean. This study reconstructs age, growth patterns, and life-history strategies of D. gigas and D. perspicillatus using sagittal otolith microstructure analysis. Specimens were collected during oceanographic surveys conducted in 2023 and 2024, and individual ages were estimated by counting daily otolith growth increments. Somatic growth trajectories were evaluated using multiple nonlinear growth models, including the von Bertalanffy, Gompertz, and Logistic functions, and growth dynamics were further assessed through derivative-based growth speed analyses. The results reveal pronounced interspecific differences in growth strategy and longevity. D. perspicillatus exhibited rapid early somatic growth, a compressed age structure, and an early approach to asymptotic length, indicating a short-lived life-history strategy characterized by early growth deceleration and high population turnover. In contrast, D. gigas showed faster early growth, prolonged somatic development, greater inter-individual variability, and substantially larger maximum body size, reflecting delayed maturation and extended lifespan. Otolith microstructural zonation clearly corresponded to larval, juvenile, and adult growth phases in both species. The predominance of younger age classes in the catch and interannual differences in size structure were primarily attributed to ontogenetic habitat shifts, cohort composition, and sampling availability rather than intrinsic changes in growth dynamics. Full article

Introduction: Advanced-stage high-grade serous ovarian cancer (HGSC) is a disease that is difficult to manage due to its heterogeneous clinical behavior. No reliable prediction of response to chemotherapy is currently available and the overall survival rate remains poor. Herein, we sought to determine the molecular mechanisms by which microRNAs (miRNAs) confer chemoresistance in ovarian cancer and demonstrate the efficacy of targeting miRNAs to sensitize HGSC to cisplatin treatment. Methods: Next-generation miRNA sequencing was performed using microdissected HGSC specimens to identify an miRNA signature for intrinsic chemoresistance, and miR-625-3p was selected for further study. The effects of miR-625-3p on cisplatin sensitivity were evaluated using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assays and cell death enzyme-linked immunosorbent assay. Transcriptome profiling analysis, online prediction algorithms, and reporter assays were used to demonstrate SSX2IP as the direct gene target of miR-625-3p. Cell death enzyme-linked immunosorbent assays, mass spectrometry, and high-speed confocal microscopy were used to determine the roles of SSX2IP in mediating the effects of miR-625-3p in cisplatin sensitivity via the extracellular vesicle (EV) secretion of cisplatin. Results: An miRNA signature for intrinsic chemoresistance was identified. Amongst all the downregulated miRNAs in the chemo-refractory samples, only miR-625-3p was associated with poorer overall survival and progression-free survival rates. Further functional studies showed that the overexpression of miR-625-3p significantly decreased cisplatin resistance in ovarian cancer cells both in vitro and in vivo. SSX2IP (Synovial Sarcoma, X Breakpoint 2 Interacting Protein) was confirmed to be the direct gene target of miR-625-3p and its upregulation abrogated miR-625-3p-mediated cisplatin resistance by enhancing the EV export of cisplatin in ovarian cancer cells. Conclusions: These findings provide a new paradigm for intrinsic cisplatin resistance acquisition by HGSC cells, which will be crucial for developing new treatment strategies for ovarian cancer based on the upregulation of miR-625-3p or downregulation of SSX2IP to enhance cisplatin sensitivity and improve patient survival rates. Full article

In the aviation industry the so-called ballistic impact of small accidental or human-made sources on aircraft elements during their service life encompasses several scenarios of practical interest. The experimental assessment of ballistic impact requires dedicated infrastructures (such as the light-gas gun system utilized in this study) and exhibits intrinsic difficulties, mainly concerning the proper acceleration of a projectile and the accurate measurement by a high-speed camera of its (inlet and outlet) velocity. As a first objective, this study aimed at characterizing the dynamic response of fiber metal laminates, manufactured ad hoc by the authors with two different stacking sequences currently not available in commerce. The layups included aluminum 2024 T3 and aramid fiber-reinforced prepregs, leading through specific treatments to excellent specific properties. The collision of the laminate with a 25 g, 9 mm radius steel sphere, traveling at speeds ranging from 90 to 145 m/s, caused a variety of scenarios: partial or complete penetration, with the projectile passing through and continuing its trajectory, remaining stuck in the sample (embedment) or even being bounced back (ricochet). The experimental information led to the estimation, for each typology of sample, of a conventional ballistic limit according to the Lambert-Jonas approximation, as a second objective, these data were utilized to validate an accurate heterogeneous model of the samples developed in the ABAQUS^® platform, discretized by finite elements in explicit dynamics and including geometric nonlinearity and contact. We describe plasticity and damage of the metal layers by the Johnson–Cook phenomenological model, progressive failure in the fiber-reinforced plies through a 2D Hashin criterion with damage evolution, and interlaminar debonding at multiple cohesive interfaces governed by the Benzeggagh–Kenane criterion. The outlet speed of the bullet measured during the experiments was retrieved correctly by this model, and a satisfactory agreement of the finite element predictions was found with the deformation patterns and the damage mechanisms identified by post mortem visual inspection. Finally, several discussion points are raised, concerning the robustness of the numerical analyses, the reliability of the constitutive modeling and the identification of the governing parameters. Full article

To investigate the mechanical bearing characteristics of the “excavation-backfill” composite after the excavation of coal pillars and backfill replacement with gangue-based cemented paste backfill, mechanical bearing characteristic experiments are conducted on a series of coal samples with rectangular “excavation-backfill” roadways under uniaxial loading, covering the full deformation and failure process. The MTS universal testing machine and DS5-type acoustic emission signal acquisition system are employed, and a high-speed camera is adopted to monitor and record the full failure process. The mechanical bearing characteristics and crack evolution mechanisms of unfilled coal pillar (U-C) and backfill coal pillar (B-C) samples are explored. The results show that with the increase in “excavation-backfill” width, the uniaxial compressive strength and elastic modulus of U-C samples decrease significantly, and the samples exhibit brittle–ductile failure. When the “excavation-backfill” width is 60 mm, the backfill can distinctly improve the strength and elastic modulus of B-C samples, showing a strong strength recovery effect. The temporal characteristics of AE signals indicate that both U-C and B-C samples experience four stages subjected to uniaxial compression: quiet period, rising period, active period, and post-peak rising period. In the quiet period and rising period, the b-value fluctuates upward with energy release; in the active period, the b-value decreases significantly with large energy release; in the post-peak rising period, crack propagation and frictional slip increase, leading to an enlarged fluctuation amplitude of the b-value. Based on the location of AE sources, the three-dimensional crack chain evolution is inverted. The crack chain evolution of the U-C is mainly distributed along the dip direction (75°~90°, 255°~270°) and vertical direction (165°~180°) of the coal bedding plane, while the B-C is more uniform, indicating that the backfill evidently affects the crack distribution. This study provides new insights for predicting the crack evolution and failure mode of coal–rock composites. Full article

Aiming at the contradiction between forming quality and efficiency in the existing research on thick-layer laser powder bed fusion (LPBF) manufacturing of Ti6Al4V, this study focused on the influence of near-circular keyhole defects caused by scanning speed on the short-term mechanical properties of Ti6Al4V alloy manufactured by high-efficiency LPBF with a 100 μm layer thickness, the build rate of which reached 14 mm³/s. When the scanning speed decreased to 800 mm/s, the relative density decreased from 99.87% to 99.27%, and the maximum pore size increased from 19.6 μm to 87.2 μm. Under the conditions of high relative density (above 99.8%) and maximum pore size less than 20 μm, the annealed Ti6Al4V samples could achieve a tensile strength of 1009.7 MPa, a yield strength of 914.0 MPa, an elongation of 15.2%, and an impact toughness of 41.71 J/cm². With the increase in porosity and a maximum pore size exceeding 50 μm, the tensile strength became more unstable and exhibited a declining trend, while the impact toughness decreased by more than 5%. This is mainly attributed to the stress concentration around large-sized pores, leading to the easy generation of long and deep cracks at the edges and reducing the material’s ability to resist crack initiation and propagation. Full article

Typhoon avoidance is critical for ship maneuvering safety under extreme meteo-ocean conditions. This study proposes a data-driven framework that converts AIS trajectories into interpretable course deviation and speed change responses for navigational decision support. After AIS cleaning, temporal resampling, and matching with gridded wind, wave, and current fields, rule-based sliding-window and regression procedures, informed by experienced captains and company staff, automatically generate proxy labels for deviation and speed reduction. Samples are stratified by vessel size to reflect differences in inertia and maneuverability, and XGBoost classifiers are trained with simple resampling to mitigate class imbalance. The framework is demonstrated on a single-event case study of Typhoon Yagi in the South China Sea, covering 8609 vessels and reconstructed sailing fragments. On the test set, the deviation model achieves 89.8% accuracy and high recall for deviation cases, while the speed change model reaches 82% balanced accuracy under the proxy-label setting. Results suggest a scale-dependent response: smaller vessels exhibit more frequent course deviation, whereas larger vessels more often reduce speed under severe wind-wave loading. The framework offers a proof-of-concept approach to derive behavior-based indicators from AIS and environmental data and may support situational assessment under adverse weather. Full article

Fifth generation (5G) networks have significantly enhanced connectivity, speed, and reliability, transforming industries with faster and more efficient communication. The Internet of Things (IoT) has introduced unprecedented convenience and automation, revolutionizing sectors such as healthcare, finance, and smart infrastructure. However, both 5G networks and IoT environments are experiencing a high frequency of attacks. Intrusion detection systems (IDSs) built on federated learning (FL) are being proposed to boost data privacy and security. However, these IDSs are related with the inherent drawbacks of FL, namely the existence of non-independently and identically (non-IID) distributed features and the machine learning model complexity. To address these limitations, we present a study that integrates a Mixture of Experts (MoE) into an FL setting in the task of intrusion detection. Specifically, to mitigate the issues of model complexity within the FL setting, we use a sparsely gated MoE layer consisting of a router/gating network and a set of experts. Only a subset of experts is selected via applying noisy top-k gating. To alleviate the issue of non-IID data, we adopt the Label-based Dirichlet Partition method, utilizing Dirichlet sampling with a hyperparameter $\alpha$ to simulate a label-based non-IID data distribution. Four FL strategies are employed. We perform our experiments on the 5G-NIDD and BoT-IoT datasets. Findings show that the proposed approach achieves competitive performance across both datasets under heterogeneous federated settings. Full article

Natural gas hydrate (NGH) deposits represent a vast and clean energy source. However, sustainable gas production from these resources remains an unsolved technical problem due to potential geohazards and climate challenges. A critical issue in this regard is the difficulty of obtaining in situ samples, which are essential for detailed laboratory studies of NGH’s geomechanical and chemical behavior for safe and green gas production after hydrate dissociation. Currently, the retrieval of representative samples from NGH reservoirs is hindered by significant technological limitations and high costs. Consequently, laboratory-synthesized gas hydrate-bearing sediment (HBS) samples are crucial for controlled research purposes and validating numerical simulation models and are used in the majority of research studies. With this in mind and considering the complexity of synthesizing HBS samples, this study comprehensively reviews different methods of synthesizing gas hydrates in porous media, including excess-gas, excess-water, dissolved-gas, spray, bubble injection, and hybrid techniques. Each method produces distinct hydrate morphologies (e.g., pore-filling, cementing, grain-coating, etc.) and saturation levels, with trade-offs in speed, uniformity, reproducibility, and ease of control. Furthermore, the current review details the synergic application of non-invasive characterization techniques, i.e., X-ray Computed Tomography (CT) and Nuclear Magnetic Resonance (NMR), in studying gas hydrates. CT provides high-resolution three-dimensional (3D) structural images of pore geometry and hydrate distribution, while NMR/MRI (Magnetic Resonance Imaging) quantifies fluid saturations and tracks hydrate formation/dissociation dynamics in real time. The synergistic use of CT and NMR offers a powerful multimodal approach, overcoming individual limitations such as CT’s poor hydrate–water contrast detection and NMR’s indirect hydrate inference, which could help in the sustainable synthesis of particular hydrate morphologies. Finally, the critical analysis of current technological challenges or gaps and also the emerging trends and future directions in the study of HBS, including advanced imaging techniques, AI-assisted analysis, and standardization efforts, etc., are discussed. It was found that the selection of the most appropriate method for natural gas hydrate synthesis is mostly task-specific, and the emerging technologies have facilitated the synthesis of HBS samples with more precise control of morphology, saturation, etc. This review provides the required insights for sustainable synthesis and characterization of hydrate-bearing sediments samples and serves sustainable gas production from natural gas hydrate reservoirs. Full article

Crack segmentation on building surfaces is challenging due to the thin, curvilinear crack morphology and background interference from textures and illumination variations. This study proposes SDCrackSeg, a U-shaped network combining frequency-domain enhancement with geometry-adaptive convolution. The core Frequency Spatial Convolution module integrates two branches: Adaptive Frequency Convolution enhances high-frequency crack details, while Dynamic Snake Convolution adapts sampling to curvilinear structures. A topology-aware loss based on persistent homology further regularizes structural connectivity. Experiments on CHCrack5K demonstrate state-of-the-art performance with Precision 0.900, mIoU 0.816, F1-score 0.888, and Dice 0.675, while maintaining nearly 200 FPS inference speed. Results confirm that frequency–spatial fusion with topology regularization effectively improves crack detection reliability for practical building inspection. Full article

Real-time lithology identification while drilling is widely applied in oil and gas exploration, development drilling, geo-steering, unconventional resource extraction, well logging, and environmental monitoring, enhancing efficiency and accuracy in subsurface operations. This study investigates the frequency characteristics of rock-drilling sounds generated during drilling operations and explores their potential for real-time lithology identification. Experiments were conducted using 8 mm and 14 mm drill bits at both high and low rotational speeds on four types of rock samples: sandstone, limestone, granite, and shaly sandstone. Sound signals were recorded both within the rock and in air using high-fidelity sensors. The results reveal distinct frequency patterns for each rock type, with sandstone exhibiting dominant low-frequency energy, limestone and granite showing broader frequency bands with strong high-frequency components, and shaly sandstone displaying a mix of low- and high-frequency energy. Quadratic polynomial regression models between the Vp or Vs and the peak frequencies of the four distinct rock samples are built, and the corresponding coefficients of determination are 0.9878 and 0.9799. The study also demonstrates that drilling parameters, such as drill bit diameter and revolutions per minute (RPM), significantly influence the frequency distribution of rock-drilling sounds, with larger drill bits and higher RPMs producing broader frequency bands and stronger high-frequency energy. Comparisons between in-rock and in-air recordings show that the latter captures richer high-frequency information, though the overall trends remain consistent. These findings provide an experimental foundation for using rock-breaking sounds as a potential tool for lithology identification during drilling operations. The study highlights the importance of considering rock heterogeneity and drilling conditions when interpreting acoustic data and suggests future work to validate the method in field conditions and integrate advanced data processing techniques. Full article

Neural rendering techniques aim to generate photorealistic images and accurate 3D geometries from multi-view images but often struggle with efficiency and geometric consistency in complex or dynamic scenes. Optimized Neural Radiance Fields (OptiNeRF) addresses these challenges through several innovations. It uses spatially optimized sampling to focus on points near object surfaces, reducing computation while improving precision. Leveraging the pre-trained Marigold model, it generates depth and normal maps as geometric priors. Sampled points are processed through a hybrid network combining an MLP and a multi-resolution feature grid (MRF), capturing fine details and large-scale structures. To handle varying illumination and complex materials, OptiNeRF introduces adaptive volume rendering (AVR), dynamically adjusting light transparency and scattering. A progressive sampling strategy further focuses computation on regions with high geometric complexity. The loss function incorporates RGB, normal, depth, boundary, and lighting optimization losses, with adaptive weight modulation for geometric priors, ensuring both visual fidelity and geometric consistency even with inaccurate depth/normal estimates. Experiments on dynamic scenes show strong performance, with a PSNR of 32.10 dB, SSIM of 0.936, Chamfer distance of 1.28 × 10⁻³, training time of 12 h, and rendering speed of 25 FPS, demonstrating high geometric accuracy, realistic rendering, and computational efficiency over conventional methods. Full article

Folding-wing Unmanned Aerial Vehicles (UAVs) have become a key platform in modern aerial applications, owing to their superior portability and rapid deployment capabilities. While the tandem-wing configuration offers a compact solution for strict folding constraints, the resulting high wing loading necessitates a maximized lift coefficient (C_L) to ensure efficient low-speed loitering. This study presents an aerodynamic optimization framework aiming to maximize the C_L of a folding tandem-wing UAV. A combined optimization strategy integrating Optimal Latin Hypercube Sampling (OLHS), orthogonal polynomial surrogate models, and the Multi-Island Genetic Algorithm (MIGA) is established. With aft wing parameters determined, global sensitivity analysis identifies the fore wing span as the dominant factor, contributing 47.40% to lift performance. Crucially, although vertical separation contributes only 6.53% to C_L and sweep angle just −1.22% to drag coefficient, their strong interaction effects with wing span confirm their non-negligible role. Finally, the flow field characteristics at the wing root of the optimized configuration undergo significant changes, resulting in a 4.28% increase in the C_L. This work validates the important role of parameter interaction effects in aerodynamic optimization and provides a theoretical basis for the design of geometrically constrained aerial vehicles requiring high lift coefficients. Full article