We construct new conformal minimal 2-spheres of constant curvature linearly full in the quaternion projective space
The multi-objective airline crew pairing problem tries to find a subset of feasible pairings such that all flights are covered and each objective function is minimized. For this problem， we propose two mathematical models： a novel mathematical model that contains logical relationships， and an integer programming model based on all feasible pairings enumerated by a depth-first search method. To solve the problem， we propose an improved multi-objective evolutionary algorithm， which is a non-dominated sorting genetic algorithm that distinguishes individuals using the distance between the objective vector corresponding to each solution and an adaptive evaluation vector. The proposed algorithm uses the direction of the adaptive evaluation vector and the Pareto orientation for guidance to derive Pareto solutions. We also use a repairing strategy and a local optimization strategy for deriving feasible and better solutions. For this problem， in our experimental results， the performance of the proposed algorithm is superior to those of the traditional nondominated sorting genetic algorithm Ⅱ and another multi-objective genetic algorithm.
Constructive solid geometry （CSG） is a geometric modeling technique that defines complex shapes through Boolean operations between basic geometric primitives. However， reverse modeling to recover the CSG construction sequence from existing geometric shapes has been a challenging research problem. In this paper， we propose a Transformer-based deep network architecture that can parse input geometric shapes and output their corresponding CSG modeling sequences. Using the CSGNet algorithm as a baseline， we first expanded the synthetic dataset used for training， enabling the model to learn and parse a broader range of shapes. Additionally， we adopted an autoregressive learning architecture with a VGG encoder and a Transformer decoder， leveraging the attention mechanism to better correlate the generated sequences. This approach translates 2D shape inputs into CSG sequence outputs in a fixed format， resulting in improved reconstruction quality. We also introduced a validity correction module to ensure that the model does not output invalid reconstruction sequences. Experimental results show that the proposed model achieves better quality on both synthetic and real CAD datasets.
Liquid lead-bismuth is considered as the core coolant of the fourth-generation lead-cooled fast reactors， and its compatibility with structural steels is one of the focuses of current research. In this paper， the aggregation process of iron atoms in liquid lead-bismuth and the diffusion behavior of iron clusters were simulated by using the molecular dynamics method. The influence laws and microscopic mechanisms of the temperature of the system， the size and mass concentration of iron clusters on the coalescence process between iron clusters and the diffusion behavior of iron clusters were analyzed. The results show that iron atoms tend to form clusters in liquid lead-bismuth and that high temperatures can accelerate the aggregation process. In addition， it is found that the coalescence between iron clusters in liquid lead-bismuth needs to overcome energy barriers， the energy barrier to be overcome for the coalescence of two iron clusters of radii 10 Å at 473 K is about 2.14 eV， and the increase of system temperature and the increase of iron clusters size are favorable for the occurrence of coalescence between iron clusters. Furthermore， the diffusion coefficients of iron clusters were calculated， and found to increase with the decrease of their mass concentration， decrease with the increase of their size， and increase with the increase of the temperature of the system. The effect of iron clusters size on the diffusion activation energies was not significant， and the diffusion activation energies of iron clusters with radii of 10， 12.5， and 15 Å were 0.128， 0.140， and 0.134 eV， respectively.
The distributed formation synthetic aperture radar （SAR） can achieve ocean surface velocity measurement through along-track interferometry. However， the existence of cross-track baselines introduces phase influences from flat earth effect， ocean wave height， and sea surface height into the interferometric phase， making it challenging to directly retrieve sea surface velocity accurately from the interferometric phase. This paper proposes a method for measuring ocean surface velocity using a multibaseline interferometric SAR formation system. Firstly， considering the characteristics of wave motion and noise distribution in the scene， a joint suppression method for wave height phase， large-scale wave orbital velocity phase， and noise phase based on spatial averaging is proposed， which effectively suppresses these interferometric phases. Then， to address the coupling issue between the radial velocity phase and sea surface height phase， a method for decoupling the radial velocity phase and height phase based on multibaseline is introduced. By introducing another interference pair to improve the degree of freedom in data processing， the effective separation of these two phase terms is achieved through two sets of coupled interference phases， achieving high-precision measurement of radial flow velocity. Finally， the proposed method was validated using the measured data from the Hongtu-1 satellite， and the results proved the effectiveness of the proposed method.
Radar signal sorting is a key aspect of radar electronic reconnaissance in modern warfare. However， as the electromagnetic environment becomes increasingly complex， the signals intercepted by reconnaissance receivers often suffer from a large number of missing pulses， spurious pulses， and pulse timing jitter. The complex electromagnetic environment is characterized by long-range signal correlations， diverse data distributions， and complementary information across different time steps. Existing bidirectional long short-term memory（Bi-LSTM）-based radar signal sorting methods perform inadequately， primarily due to their insufficient ability to model global dependencies， risks of gradient vanishing and overfitting， and limited feature interaction. This paper proposes an integrated attention Bi-LSTM method that addresses these issues through the incorporation of multi-head self-attention mechanisms， layer normalization， and residual connections. The main improvements include： 1） Data preprocessing： Converting the original time of arrival data into time difference of arrival data to enhance the robustness and accuracy of the input data. 2） Feature extraction and fusion： Utilizing Bi-LSTM layers to extract both forward and backward temporal information， while introducing a multi-head self-attention mechanism for deep feature fusion， balancing the capture of long-range dependencies and local detail extraction. 3） Optimizing training stability： Employing residual connections and dropout regularization to ensure smooth gradient propagation. Experimental results indicate that this method achieves significantly improved accuracy compared to traditional Bi-LSTM approaches， and the structural improvements effectively enhance the model’s robustness against pulses loss， spurious pulse， and pulse jitter.
The L-band digital aeronautical communication system （LDACS） is one of the primary candidate solutions for future aviation broadband communication. The spectrum of the LDACS system is embedded between adjacent channels of the distance measuring equipment （DME）. The orthogonal frequency-division multiplexing （OFDM） modulation technique it employs results in significant out-of-band radiation， causing some interference with the DME system. Additionally， the OFDM waveform has a relatively low frequency band utilization. To address these issues， a waveform design method for LDACS based on universal-filtered multi-carrier （UFMC） is proposed. Initially referencing the OFDM configuration of the LDACS system， a transceiver framework based on UFMC is designed. Subsequently， Chebyshev prototype filters are chosen， and a method involving interpolation masking is employed to design sharp cutoff filters suitable for the UFMC system. Finally， the performance is compared with LDACS systems based on OFDM and filtered OFDM. Simulation results indicate that the UFMC waveform design method， while maintaining system error rates， achieves lower out-of-band radiation， reduced peak to average power ratio， and improved time-frequency efficiency.
To meet the requirements of low-cost and low-sidelobe level for space-borne phased array antennas， an optimization method for sparse arrays based on deep learning is proposed. For the optimization problem of large-scale sparse arrays， traditional genetic algorithms face issues such as high computational complexity and time-consuming fitness evaluation during the optimization process. In this paper， a deep-learning model is introduced to predict the sidelobe level， replacing the time-consuming simulation calculations， and significantly reducing the computational complexity. Experimental results show that， compared with traditional genetic algorithms， the method proposed in this paper shows some improvement in optimization effect， exhibits superior computational efficiency particularly suited for high-frequency engineering applications and can effectively solve the optimization problem of large-scale sparse arrays.
Registration of non-rigid 3D models has a wide range of applications in different application areas such as animation driving， texture transfer， semantic understanding， etc. and is an important research topic in computer graphics.However， non-rigid objects based on skeletal deformation， such as human bodies and animals， often induce overall shape deformations in accordance with the movements of the joints. The non-rigid deformations and complex interactions of such objects thus pose difficulties and challenges to the 3D alignment task. Although recent studies using deep learning techniques can achieve good results in reconstructing 3D shapes with skeleton structures， these methods tend to rely heavily on large datasets with annotations. In the unsupervised case， they are still prone to generating overstretched and partially crossed model meshes.The paper proposes an unsupervised GAN-based network framework for the registration of 3D models， using a 3D generator to generate model meshes that fit the scanned point clouds， and two novel discriminators to impose regularization constraints on the deformed meshes， which achieves good results on public datasets.
In existing model-based reinforcement learning methods， a single policy is adopted to interact with the real environment and the environment model， which makes it hard for the agent to balance the efficiency of exploring the environment and the stability of policy updating. To address this issue， this paper proposes a model-based explorer-learner joint optimization via uncertainty augmentation method （MELO-UA）. MELO-UA simultaneously optimizes a pair of policies， namely the explorer policy for interacting with the real environment， and the learner policy for interacting with the environment model. During the optimization of the explorer， an implicit bonus based on model uncertainty is introduced to enhance the efficiency of exploring the real environment. At the same time， during the optimization of the learner， the model uncertainty is used as a constraint to ensure the stability of the policy optimization. Experimental results on multiple continuous control tasks show that the proposed method has significant advantages in both asymptotic performance and sample efficiency compared to state-of-the-art methods.
Video anomaly detection （VAD） is a technique aimed at identifying abnormal events within videos and has significant applications in public safety and video content understanding. Traditional VAD methods primarily rely on extracting pixel features from either the entire video frame or specific regions. To reduce the impact of unstructured noise， VAD methods based on human skeletal data have garnered considerable attention. However， in open-set scenarios， these methods face two major challenges： erroneous reconstruction of abnormal behaviors and insufficient generalization to diverse normal behaviors. To address these issues， this paper proposes a skeleton-based video anomaly detection framework with memory enhancement and diffusion model （MEDM-SVAD）. This framework incorporates a memory enhancement module to expand the model’s memory capacity for normal samples， preventing abnormal behaviors from being misclassified due to minimal reconstruction error. Additionally， the diffusion model significantly improves the framework’s ability to generalize to out-of-domain normal behaviors. Experimental results on three public datasets， HR-STC， HR-Avenue， and HR-UBnormal， show that the proposed method MEDM-SVAD achieves AUC scores of 78.5%， 90.1%， and 69.7%， respectively， demonstrating various levels of performance improvement over the current state-of-the-art MoCoDAD algorithm， thus verifying its effectiveness and superiority across multiple scenarios.
With the surge in the number of satellites in mega-constellations， the computational resources required for constellation orbit simulation and visualization have increased dramatically. To address the issue of inefficient visualization of mega-constellations on mobile devices with limited storage and computational power， this paper proposes an orbit model simplification method based on the HDBSCAN algorithm. This method employs a two-step clustering strategy： first， categorizing the constellation orbits， and then further subdividing the orbital planes within each category， using the cluster centers as simplified representations. Using the Starlink constellation as an example， experiments show that the orbit data is compressed from 6 778 to 279 tracks. Under the experimental hardware environment， the visualization frame rate is significantly improved， and the simplified orbit model effectively retains the structural characteristics of the mega-constellation.
This paper conducts a numerical study on the problem of flow-induced vibration under an axial magnetic field. The finite element method is used to simulate the velocity field of the metal fluid under the combined effects of the magnetic field and the vibration of a cylinder-flexible beam. This research analyzes the influence of magnetic field intensity and flexible beam length on the amplitude， vibration frequency of the flexible beam， and the flow characteristics of the flow field. The results indicate that the flexible beam undergoes periodic vibrations due to vortex shedding effects， and the dominant vibration mode exhibits a close resemblance to the first-order natural mode of an Euler-Bernoulli beam. As the magnetic field intensity increases， the unsteady flow in the flow field is suppressed， resulting in a decrease in the amplitude of the flexible beam. Increasing the length of the flexible beam decreases the vibration frequency， while the amplitude shows a nonlinear relationship that first increases and then decreases. Regarding the vortex shedding characteristics of the flow field， an increase in magnetic field intensity promotes vortex dissipation， and reduces the frequency of vortex shedding. Additionally， the longer the flexible beam， the greater the energy dissipation during its interaction with the cylinder shear layer， which significantly changes the vortex shedding pattern； the critical Hartmann number for the transition from vortex shedding flow to steady flow decreases with the increase in the flexible beam length.
Net primary productivity （NPP） serves as a vital metric for evaluating the carbon sequestration potential of terrestrial ecosystems. Investigating the impacts of climate change and land use change on vegetation NPP is crucial for understanding regional ecosystem carbon sink management. This study utilizes moderate resolution imaging spectroradiometer （MODIS） NPP data from 2003 to 2022， combined with Theil-Sen trend analysis and the Mann-Kendall test， to examine the spatiotemporal patterns of NPP in Guangdong Province over the past two decades. Furthermore， correlation analysis and the Geodetector were applied to quantify the effects of climatic factors and land use changes on vegetation NPP. The findings reveal that low-NPP regions in the Pearl River Delta exhibit a significant declining trend， whereas high-NPP regions in eastern and northern Guangdong demonstrate either pronounced or gradual improvement. Additionally， the relative contributions of climate change and land use change to NPP variations were 59.48% and 40.51%， respectively. Among climatic factors， precipitation had the highest contribution （58.32%）， followed by radiation （24.17%） and temperature （17.51%）. Under future climate scenarios， high-emission conditions are projected to reduce the extent of high-NPP areas while expanding low-NPP areas， whereas moderate-emission scenarios are expected to sustain a steady increase in NPP.