Search Results (983)

New assembly methods for aircraft structural parts, such as skins and stringers, are being investigated to address issues like galvanic corrosion, stress concentration, and weight. For this, many researchers are examining the mechanical and fracture properties of adhesively bonded parts through experimental testing and numerical modelling methods, including Cohesive Zone Modelling (CZM), Compliance-Based Beam Method (CBBM), Double Cantilever Beam (DCB), and End Notched Flexural (ENF) tests. In this study, similarly, DCB and ENF tests were conducted on skin and beam parts bonded with AF163-2K adhesive using CBBM and then modelled and analysed in ABAQUS CAE 2018 software. Four different skin–stringer connection models were analysed, respectively, using only adhesive, only rivets, both adhesive and rivets, and also a reduced number of rivets in the adhesively bonded joint. This study found that adhesive increased initial strength, while rivets improved strength after the adhesive began to crack. Using a hybrid connection that combines both rivets and adhesive has been observed to enhance the overall strength and durability of the assembly. Then, experimental results were compared, and four numerical models for skin–stringer connections (adhesive only, rivets only, adhesive and rivets, and adhesive with reduced rivets) were analysed and discussed. In this context, the results were supported and reported with graphs, tables, and analysis images. Full article

Advanced Persistent Threat (APT) refers to a highly targeted, sophisticated, and prolonged form of cyberattack, typically directed at specific organizations or individuals. The primary objective of such attacks is the theft of sensitive information or the disruption of critical operations. APT attacks are characterized by their stealth and complexity, often resulting in significant economic losses. Furthermore, these attacks may lead to intelligence breaches, operational interruptions, and even jeopardize national security and political stability. Given the covert nature and extended durations of APT attacks, current detection solutions encounter challenges such as high detection difficulty and insufficient accuracy. To address these limitations, this paper proposes an innovative high-accuracy APT attack detection model, CNN-KOA-BiGRU, which integrates Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and the Kepler optimization algorithm (KOA). The model first utilizes CNN to extract spatial features from network traffic data, followed by the application of BiGRU to capture temporal dependencies and long-term memory, thereby forming comprehensive temporal features. Simultaneously, the Kepler optimization algorithm is employed to optimize the BiGRU network structure, achieving globally optimal feature weights and enhancing detection accuracy. Additionally, this study employs a combination of sampling techniques, including Synthetic Minority Over-sampling Technique (SMOTE) and Tomek links, to mitigate classification bias caused by dataset imbalance. Evaluation results on the CSE-CIC-IDS2018 experimental dataset demonstrate that the CNN-KOA-BiGRU model achieves superior performance in detecting APT attacks, with an average accuracy of 98.68%. This surpasses existing methods, including CNN (93.01%), CNN-BiGRU (97.77%), and Graph Convolutional Network (GCN) (95.96%) on the same dataset. Specifically, the proposed model demonstrates an accuracy improvement of 5.67% over CNN, 0.91% over CNN-BiGRU, and 2.72% over GCN. Overall, the proposed model achieves an average improvement of 3.1% compared to existing methods. Full article

To address the problems of poor applicability of existing fault link recovery algorithms in network topology migration and backup path congestion, this paper proposes a backup path algorithm based on graph convolutional neural to improve deep deterministic policy gradient. First, the BPDM-GCN backup path algorithm is constructed within a deep deterministic policy gradient training framework. It uses graph convolutional networks to detect changes in network topology, aiming to optimize data transmission delay and bandwidth occupancy within the network topology. After iterative training of the BPDM-GCN algorithm, the comprehensive link weights within the network topology are generated. Then, according to the comprehensive link weight and taking the shortest path as the optimization objective, a backup path implementation method based on the incremental shortest path tree is designed to reduce the phasor data transmission delay in the backup path. In conclusion, the experimental results show that the backup path formulated by this algorithm exhibits reduced data transmission delay, minimal path extension, and a high success rate in recovering failed links. Compared to the superior NRLF-RL algorithm, the BPDM-GCN algorithm achieves a reduction of approximately 14.29% in the average failure link recovery delay and an increase of approximately 5.24% in the failure link recovery success rate. Full article

Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), with millimeter-level accuracy and full-resolution capabilities, is essential for monitoring urban deformation. With the advancement of SAR sensors in spatial and temporal resolution and the expansion of wide-swath observation capabilities, the number of permanent scatterers (PSs) in high-density urban areas has surged exponentially. To address these computational and memory challenges in high-density urban PSInSAR processing, this paper proposes an efficient method for integrating regular grid partitioning and connected component constraints. First, adaptive dynamic regular grid partitioning was employed to divide monitoring areas into sub-blocks, balancing memory usage and computational efficiency. Second, a weighted least squares adjustment model using common PS points in overlapping regions eliminated systematic inter-sub-block biases, ensuring global consistency. A graph-based connected component constraint mechanism was introduced to resolve multi-component segmentation issues within sub-blocks to preserve discontinuous PS information. Experiments on TerraSAR-X data covering Fuzhou, China (590 km²), demonstrated that the method processed 1.4 × 10⁷ PS points under 32 GB memory constraints, where it achieved a 25-fold efficiency improvement over traditional global PSInSAR. The deformation rates and elevation residuals exhibited high consistency with conventional methods (correlation coefficient ≥ 0.98). This method effectively addresses the issues of memory overflow, connectivity loss between sub-blocks, and cumulative merging errors in large-scale PS networks. It provides an efficient solution for wide-area millimeter-scale deformation monitoring in high-density urban areas, supporting applications such as geohazard early warning and urban infrastructure safety assessment. Full article

Origin–destination (OD) flows are essential for urban studies, yet their acquisition is often hampered by high costs and privacy constraints. Prevailing inference methodologies inadequately address latent spatial dependencies between non-contiguous and distant areas, which are useful for understanding modern transportation systems with expanding regional interactions. To address these challenges, this paper propose a hybrid learning model with the Global–Local Graph Attention Network and XGBoost (GLGAT-XG) to infer OD flows from both global and local geographic contextual information. First, we represent the study area as an undirected weighted graph. Second, we design the GLGAT to encode spatial correlation and urban feature information into the embeddings within a multitask setup. Specifically, the GLGAT employs a graph transformer to capture global spatial correlations and a graph attention network to extract local spatial correlations followed by weighted fusion to ensure validity. Finally, OD flow inference is performed by XGBoost based on the GLGAT-generated embeddings. The experimental results of multiple real-world datasets demonstrate an 8% improvement in RMSE, 7% in MAE, and 10% in CPC over baselines. Additionally, we produce a multi-scale OD dataset in Xian, China, to further reveal spatial-scale effects. This research builds on existing OD flow inference methodologies and offers significant practical implications for urban planning and sustainable development. Full article

This paper presents an optimization model for wireless channel allocation in cellular networks, specifically designed for the transmission of smart meter (SM) data through a mobile virtual network operator (MVNO). The model efficiently allocates transmission channels, minimizing smart grid (SG) costs. The MVNO manages fixed and random channels through a shared access scheme, optimizing meter connectivity. Channel allocation is based on a Markovian approach and optimized through the Hungarian algorithm that minimizes the weight in a bipartite network between meters and channels. In addition, cumulative tokens are introduced that weight transmissions according to channel availability and network congestion. Simulations show that dynamic allocation in virtual networks improves transmission performance, contributing to sustainability and cost reduction in cellular networks. This study highlights the importance of inefficient resource management by cognitive mobile virtual network and cognitive radio virtual network operators (C-MVNOs), laying a solid foundation for future applications in intelligent networks. This work is motivated by the increasing demand for efficient and scalable data transmission in smart metering systems. The novelty lies in integrating cumulative tokens and a Markovian-based bipartite graph matching algorithm, which jointly optimize channel allocation and transmission reliability under heterogeneous wireless conditions. Full article

(1) Background: Brain–computer interface (BCI) technology represents a cutting-edge field that integrates brain intelligence with machine intelligence. Unlike BCIs that rely on external stimuli, motor imagery-based BCIs (MI-BCIs) generate usable brain signals based on an individual’s imagination of specific motor actions. Due to the highly individualized nature of these signals, identifying individuals who are better suited for MI-BCI applications and improving its efficiency is critical. (2) Methods: This study collected four motor imagery tasks (left hand, right hand, foot, and tongue) from 50 healthy subjects and evaluated MI-BCI adaptability through classification accuracy. Functional networks were constructed using the weighted phase lag index (WPLI), and relevant graph theory parameters were calculated to explore the relationship between motor imagery adaptability and functional networks. (3) Results: Research has demonstrated a strong correlation between the network characteristics of tongue imagination and MI-BCI adaptability. Specifically, the nodal degree and characteristic path length in the right hemisphere were found to be significantly correlated with classification accuracy (p < 0.05). (4) Conclusions: The findings of this study offer new insights into the functional network mechanisms of motor imagery, suggesting that tongue imagination holds potential as a predictor of MI-BCI adaptability. Full article

Walking is recognised as a healthy and sustainable mode of transport. Providing adequate infrastructure is pivotal for the promotion of walking and, subsequently, for achieving the benefits derived from its numerous positive effects. However, efficiently measuring the walkability at the street level remains challenging. In this paper, we present an indicator-based assessment model that can be used with open spatial data to evaluate segment-based walkability. The model incorporates eleven indicators describing the street segments and their close surroundings that are relevant for pedestrians, such as the presence and type of pedestrian infrastructure, road category, noise levels, and exposure to green and blue space. A weighted average calculation results in walkability index values for each street segment within a road network graph. The model’s generic approach and the ability to be used with open data ensure its reproducibility, adaptability, and scalability. The feasibility of the walkability model was shown using a case study for Salzburg, Austria. The model’s validity was evaluated through a large-scale study involving 660 full responses to an online survey. Participants provided ratings on the walkability of randomly selected street segments in Salzburg, which were compared with the calculated index, revealing a strong correlation (Spearman’s rank correlation = 0.82). Full article

Pedestrian trajectory prediction poses significant challenges for autonomous systems due to the intricate nature of social interactions in densely populated environments. While the existing methods frequently encounter difficulties in effectively quantifying the nuanced social relationships, we propose a novel dual social graph attention network (DSGAT) that systematically models multi-level interactions. This framework is specifically designed to enhance the extraction of pedestrian interaction features within the environment, thereby improving the trajectory prediction accuracy. The network architecture consists of two primary branches, namely an individual branch and a group branch, which are responsible for modeling personal and collective pedestrian behaviors, respectively. For individual feature modeling, we propose the Spatio-Temporal Weighted Graph Attention Network (STWGAT) branch, which incorporates a newly developed directed social attention function to explicitly capture both the direction and intensity of pedestrian interactions. This mechanism enables the model to more effectively represent the fine-grained social dynamics. Subsequently, leveraging the STWGAT’s processing of directed weighted graphs, the network’s ability to aggregate spatiotemporal information and refine individual interaction representations is further strengthened. To effectively account for the critical group dynamics, a dedicated group attention function is designed to identify and quantify the collective behaviors within pedestrian crowds. This facilitates a more comprehensive understanding of the complex social interactions, leading to an enhanced trajectory prediction accuracy. Extensive comparative experiments conducted on the widely used ETH and UCY benchmark datasets demonstrate that the proposed network consistently surpasses the baseline methods across the key evaluation metrics, including the Average Displacement Error (ADE) and Final Displacement Error (FDE). These results confirm the effectiveness and robustness of the DSGAT-based approach in handling complex pedestrian interaction scenarios. Full article

To enhance the logistics scheduling efficiency of automated guided vehicles (AGVs) in automated ports and achieve the orderly charging and battery swapping of AGVs as well as self-sufficient clean energy, this paper proposes an integrated optimization method. The method first utilizes graph theory to construct a theoretical model that includes AGVs, the port road network, and charging and battery-swapping stations, in order to analyze the optimal logistics scheduling and charging and swapping strategies. Subsequently, for the multi-objective optimization problems in AGV logistics scheduling and charging and swapping, a fast solution method based on the immune optimization algorithm is proposed, with scheduling time and the self-sufficiency rate of clean energy for port AGVs as the constraint conditions. Finally, the effectiveness of the proposed model and algorithm is verified through a simulation scenario. The results show that in the simulated port logistics scenario, after optimization, the total operation time of AGVs is significantly reduced. Compared with the cases that only consider scheduling time, the charging strategy, or wind and solar output, the average clean energy self-sufficiency rate under the proposed strategy increased by 82.7%, 27.5%, and 53.9%, respectively. In addition, as the weight of the self-sufficiency rate increases, both the total driving time and the total clean energy self-sufficiency rate of AGVs show an upward trend and are approximately linearly related. Within the specified maximum scheduling time, the actual scheduling time and self-sufficiency rate can be flexibly coordinated, with significant carbon reduction benefits. Full article

Landscape spatial structure serves as the foundational framework for vegetation arrangement and spatial organization, playing a crucial role in assessing landscape morphology. Traditional 2D graph theory methods have provided insights into planar structural characteristics but fail to capture the complexity of three-dimensional spatial attributes and organizational processes inherent in landscape systems. To address these limitations, this study proposes a novel multidimensional framework for the quantitative analysis and evaluation of landscape spatial structure by integrating 3D point cloud technology with spatial network analysis. The methodology consists of three key components: (1) the formulation of multidimensional spatial organization theory, (2) spatial unit extraction and structure analysis through ArcGIS 10.5 and Cytoscape v3.6.1, and (3) the development of an indicator system for evaluating spatial structure organization. The framework was validated through the analysis of 30 urban parks, where the regularity and range of indicators are generalized to establish evaluation criteria and determine weights. The findings indicate that spatial structure indicators are moderation indicators with optimal value ranges. The evaluation system was subsequently applied across the 30 parks for comprehensive evaluation. A total of 6 of 30 parks have comprehensive scores over 0.95. In practical application, the design score of Shuyang Park improved from 0.692 to 0.826 after evaluation and optimization, demonstrating the method’s effectiveness. This study underscores the potential of digital methodologies in advancing landscape spatial structure modeling, enhancing the understanding of spatial organization, and transitioning subjective assessments toward evidence-based objective evaluations. The proposed methodology and findings offer valuable insights for diagnosing, assessing, optimizing, and managing urban green spaces. Full article

We introduce a new $(p,q)$-Post-Widder operator along with its modified form which preserves the test functions $x^{\gamma },\gamma \in \mathbb{N}$. This paper aims to investigate the approximation properties of the $(p,q)$-Post-Widder operator while preserving $x^{\gamma }$. We estimate the convergence rate of the operators with the help of a continuity module and discuss their asymptotic behavior in terms of the weighted modulus of continuity. Also, our numerical results show that the new operator preserving $x^3$ provides the best approximation. In addition, we establish quantitative estimates of the difference between the two kinds of $(p,q)$-Post-Widder operators. Finally, using numerical examples and graphs, we illustrate that, for particular cases, our results provide improved convergence estimates. Full article

This paper introduces an alternative forecasting approach that leverages the application of visibility graphs in the context of multivariate energy forecasting for a regional airport, which incorporates energy demand of diverse types of buildings and wind power generation. The motivation for this research stems from the urgent need to enhance the accuracy and reliability of load forecasting in microgrids, which is crucial for optimizing energy management, integrating renewable sources, and reducing operational costs, thereby contributing to more sustainable and efficient energy systems. The proposed methodology employs visibility graph transformations, the superposed random walk method, and temporal decay adjustments, where more recent observations are weighted more significantly to predict the next time step in the data set. The results indicate that the proposed method exhibits satisfactory performance relative to comparison models such as Exponential smoothing, ARIMA, Light Gradient Boosting Machine and CNN-LSTM. The proposed method shows improved performance in forecasting energy consumption for both stationary and highly variable time series, with SMAPE and NMRSE values typically in the range of 4–10% and 5–20%, respectively, and an $R^2$ reaching 0.96. The proposed method affords notable benefits to the forecasting of energy demand, offering a versatile tool for various kinds of structures and types of energy production in a microgrid. This study lays the groundwork for further research and real-world applications within this field by enhancing both the theoretical and practical aspects of time series forecasting, including load forecasting. Full article

The physiological functions of newborn calves are undeveloped, especially the immune system, making them susceptible to infections. In recent years, the theory of trained immunity has attracted attention and provided new strategies to prevent unknown infections in animals. This study investigated the effects of feeding yeast β-glucan on the intestinal and respiratory health of calves during the suckling period. Newborn Holstein calves (average birth weight: 36.18 ± 0.61 kg, mean ± SE) were randomly assigned to two groups: the PO (Per Os) group (n = 22) and the CON (Control) group (n = 22). Calves in the PO group were fed a yeast β-glucan solution (0.1 g/mL, 65 mg/kg body weight) at 3 and 6 days of age, respectively, while calves in the CON group received equal volumes of sterile saline orally at the same time. Blood and fecal samples were collected at 7 and 30 days of age, respectively. The results showed that (1) Compared to the CON group, being fed yeast β-glucan resulted in an inflammatory response after 24 h of the second administration, including increased gene expression of interleukin-6 (IL-6, p < 0.01), interleukin-1 beta (IL-1β, p < 0.01), and malonaldehyde (MDA, p < 0.001) content. Also, stimulation with β-glucan increased the concentrations of secreted immunoglobulin A (sIgA, p < 0.01) and defensins (p < 0.05) in the rectal feces. (2) Pre-stimulation with yeast β-glucan effectively reduced the incidence of diarrhea (p < 0.05) and bovine respiratory disease (BRD, p < 0.05) from day 31 to day 60. (3) At 30 days of age, the pre-stimulated calves had significantly lower serum DAO (p < 0.001) and MDA levels (p < 0.05), while they had higher levels of serum IL-6 (p < 0.01) and fecal slgA (p < 0.05) than calves in the CON group. (4) Pre-stimulation with yeast β-glucan altered the intestinal bacterial community; the Beta diversity results showed that the CON group and the PO group were clustered separately in the principal coordinate analysis (PCoA) graph. Obviously, the PO group sample points were more clustered. In conclusion, this study highlights the potential of yeast β-glucan-induced trained immunity to improve calf health during the suckling period. The findings offer new insights into the prevention of intestinal and respiratory infections in calves. Full article

Existing network intrusion detection models effectively capture relationships between nodes and extract key features. However, they often struggle to accurately represent node characteristics, particularly in modeling the spatiotemporal dynamics and topological structures with sufficient granularity. To address these limitations, we propose the dynamic interaction multi-channel graph convolutional network (DIMK-GCN), which integrates three key components: a spatiotemporal feature weighting module, an interactive graph feature fusion module, and a temporal feature learning module. The spatiotemporal feature weighting module constructs a dynamic graph structure that incorporates both nodes and edges, leveraging self-attention mechanisms to enhance critical feature representations. The interactive graph feature fusion module employs graph attention networks (GATs) to refine node relationships while integrating a multi-channel graph convolutional network (GCN) to extract multi-perspective features, thereby enhancing model depth and robustness. The temporal feature learning module utilizes gated recurrent units (GRUs) to effectively capture long-term dependencies and address challenges posed by non-stationary time series data. Experimental results on the CIC-IDS2017, CIC-IDS2018, and Edge-IIoTSet datasets demonstrate that DIMK-GCN significantly outperforms existing models in key performance metrics, including detection accuracy, recall, and F1-score. Notably, on the Edge-IIoTSet dataset, DIMK-GCN achieves an accuracy of 97.31%, verifying its effectiveness and robustness in detecting various types of network attacks. Full article