Search Results (617)

Background: The present study investigates VKINE, a bioactive proteolytic fragment of the proteoglycan VCAN, as a novel and significant element in the tumor extracellular matrix (ECM). Although VKINE has been recognized for its immunomodulatory potential in certain tumor types, its impact on ECM degradation and prognostic implications remains poorly understood. Objectives: This study aimed to evaluate VCAN proteolysis and its association with ADAMTS enzymes involved in extracellular matrix remodeling in spontaneous canine mammary gland cancer. Methods: The expression levels of VKINE, ADAMTS enzymes, and collagen fibers were comparatively analyzed in situ and in invasive areas of carcinoma in mixed tumor (CMT) and carcinosarcoma (CSS) with different prognoses. Results: VKINE was notably expressed in the stroma adjacent to the invasion areas in CMT, whereas ADAMTS-15 was identified as the enzyme associated with VCAN proteolysis. Inverse correlations were observed between type III collagen and VCAN expression in in situ areas. Conclusions: Our findings suggest that VKINE and ADAMTS-15 play crucial roles in the tumor microenvironment, influencing invasiveness and type III collagen deposition. This study contributes to a better understanding of the dynamics within the ECM, paving the way for potential new tools in diagnosing and treating human and canine mammary tumors. Full article

The increasing complexity of urban road networks has driven the development of Intelligent Transportation Systems (ITS) to optimize vehicle flow. To address this challenge, this paper presents an algorithm and MATLAB function that generates an adjacency list of traffic signals to provide detailed information about the relationships between all signals within a network. This list is based on stable structural road and traffic lights data and offers a crucial global perspective for signal coordination, especially in managing multiple intersections. An adjacency list is more efficient than matrices in terms of space and computational cost, allowing for the identification of critical signals before applying advanced optimization techniques such as neural networks or hypergraphs. We successfully tested the proposed method on three networks of varying complexity extracted from VISSIM and VISUM, demonstrating its effectiveness even in networks with up to 8372 links and 547 traffic lights. This tool provides a solid foundation for improving urban traffic management and coordinating signals across intersections. Full article

New data, including Raman spectroscopy, characterize unusual mineral assemblages from rocks of the Naylga and Khamaryn–Khyral–Khiid combustion metamorphic complexes in Mongolia. Several samples of melilite–nepheline paralava and other thermally altered (metamorphosed) sedimentary rocks contain troilite (FeS), metallic iron Fe⁰, kamacite α-(Fe,Ni) or Ni-bearing Fe⁰, taenite γ-(Fe,Ni) or Ni-rich Fe⁰, barringerite or allabogdanite Fe₂P, schreibersite Fe₃P, steadite Fe₄P = eutectic α-Fe + Fe₃P, wüstite FeO, and cohenite Fe₃C. The paralava matrix includes a fragment composed of magnesiowüstite–ferropericlase (FeO–MgO solid solution), as well as of spinel (Mg,Fe)Al₂O₄ and forsterite. The highest-temperature mineral assemblage belongs to a xenolithic remnant, possibly Fe-rich sinter, which is molten ash left after underground combustion of coal seams. The crystallization temperatures of the observed iron phases were estimated using phase diagrams for the respective systems: Fe–S for iron sulfides and Fe–P ± C for iron phosphides. Iron monosulfides (high-temperature pyrrhotite) with inclusions of Fe⁰ underwent solid-state conversion into troilite at 140 °C. Iron phosphides in inclusions from the early growth zone of anorthite–bytownite in melilite–nepheline paralava crystallized from <1370 to 1165 °C (Fe₂P), 1165–1048 °C (Fe₃P), and <1048 °C (Fe₄P). Phase relations in zoned spherules consisting of troilite +Fe⁰ (or kamacite + taenite) +Fe₃P ± (Fe₃C, Fe₄P) reveal the potential presence of a homogeneous Fe–S–P–C melt at T~1350 °C, which separated into two immiscible melts in the 1350–1250 °C range; namely, a dense Fe–P–C melt in the core and a less dense Fe–S melt in the rim. The melts evolved in accordance with cooling paths in the Fe–S and Fe–P–C phase diagrams. Cohenite and schreibersite in the spherules crystallized between 988 °C and 959 °C. The crystallization temperatures of minerals were used to reconstruct redox patterns with respect to the CCO, IW, IM, and MW buffer equilibria during melting of marly limestone and subsequent crystallization and cooling of melilite–nepheline paralava melts. The origin of the studied CM rocks was explained in a model implying thermal alteration of low-permeable overburden domains in reducing conditions during wild subsurface coal fires, while heating was transferred conductively from adjacent parts of ignited coal seams. The fluid (gas) regime in the zones of combustion was controlled by the CCO buffer at excess atomic carbon. Paralava melts exposed to high-temperature extremely reducing conditions contained droplets of immiscible Fe–S–P–C, Fe–S, Fe–P, and Fe–P–C melts, which then crystallized into reduced mineral assemblages. Full article

Aiming at the problem that the existing human skeleton behavior recognition methods are insensitive to human local movements and show inaccurate recognition in distinguishing similar behaviors, a multi-scale spatio-temporal graph convolution method incorporating multi-granularity features is proposed for human behavior recognition. Firstly, a skeleton fine-grained partitioning strategy is proposed, which initializes the skeleton data into data streams of different granularities. An adaptive cross-scale feature fusion layer is designed using a normalized Gaussian function to perform feature fusion among different granularities, guiding the model to focus on discriminative feature representations among similar behaviors through fine-grained features. Secondly, a sparse multi-scale adjacency matrix is introduced to solve the bias weighting problem that amplifies the multi-scale spatial domain modeling process under multi-granularity conditions. Finally, an end-to-end graph convolutional neural network is constructed to improve the feature expression ability of spatio-temporal receptive field information and enhance the robustness of recognition between similar behaviors. The feasibility of the proposed algorithm was verified on the public behavior recognition dataset MSR Action 3D, with a accuracy of 95.67%, which is superior to existing behavior recognition methods. Full article

Due to data sparsity, insufficient spatial relationships, and the complex spatial and temporal characteristics of passenger flow, it is very challenging to achieve a high prediction accuracy on Origin–Destination (OD) in a large urban rail transit network. This paper proposes a two-stage prediction network GCN-GRU, using a Graph Convolutional Network (GCN) with a Gated Recursive Unit (GRU). The GCN can obtain the adjacency relationship between different stations by selecting the adjacent neighborhoods and interacting neighborhoods of a station and capturing the spatial characteristics of the OD passenger flow. Then, an advanced weighted aggregator is employed to gather important information from the two above-mentioned types of neighborhoods to capture the spatial relationship of the network OD passenger flow and to perceive the sparsity and range of the OD data. On the other hand, the GRU can extract the temporal relationship, such as periodicity and other time-varying trends. The effectiveness of GCN-GRU is tested with a real-world urban rail transit dataset. The experimental results show that whether it is the OD passenger flow matrix of each period (one hour) on weekdays and weekends or the single-pair OD passenger flow between stations, the proposed GCN-GRU models perform better than the benchmark models. This study provides an important theoretical basis and practical applications for operators, thus promoting the sustainable development of urban rail transit systems. Full article

The concept of the energy of a graph has been widely explored in the field of mathematical chemistry and is defined as the sum of the absolute values of the eigenvalues of its adjacency matrix. The energy of a hypergraph is the trace norm of its connectivity matrices, which generalize the concept of graph energy. In this paper, we establish bounds for the adjacency energy of hypergraphs in terms of the number of vertices, maximum degree, eigenvalues, and the norm of the adjacency matrix. Additionally, we compute the sum of squares of adjacency eigenvalues of linear k-hypergraphs and derive its bounds for k-hypergraph in terms of number of vertices and uniformity of the k-hypergraph. Moreover, we determine the Nordhaus–Gaddum type bounds for the adjacency energy of k-hypergraphs. Full article

The actuator is a critical component of the micromanipulator. By utilizing the properties of expansion and contraction, the piezoelectric actuator enables the manipulator to handle and grasp miniature objects during micromanipulation. However, in piezoelectric ceramic disc actuators with conventional surface electrode configurations, the actuating force generated in the radial direction is relatively limited. When used as the actuation element of the manipulator, achieving regulation over a wide range of operating strokes becomes challenging. Therefore, altering the electrode structure is necessary to generate a greater radial force, thus enhancing the positioning and grasping capabilities of the operating arm. This paper investigates a piezoelectric actuator with interdigitated spiral electrodes, featuring a constant pitch between adjacent electrodes. The radial force was tested under mechanical clamping conditions, and the influence of the electrical signal was examined. The characteristics of the electrode structure were described, and the working principles of the piezoelectric actuators were analyzed. Theoretical equations were derived for the macroscopic characterization of the radial clamping force of the actuator, based on the piezoelectric constitutive equation, geometric principles, and Bond matrix transformation relationships. A finite element model was developed, reflecting the features of the electrode structure, and finite element simulations were employed to verify the theoretical equations for radial force. To prepare the samples, encircled interdigitated spiral electrode lines were printed on the PZT-52 piezoelectric ceramic disc using a screen printing method. The clamping force experimental platform was established, and experiments on the clamping radial force were conducted with electrical signals of varying waveforms, frequencies, and voltages. The experimental results show that the piezoelectric ceramic disc actuator with an interdigitated spiral electrode line structure, when excited by a stable sine wave operating at 200 V and 0.2 Hz, generated a peak force of 0.37 N. It was 1.76 times greater than that produced by a previously utilized piezoelectric disc with conventional electrode structures. Full article

For information security, entity and relation extraction can be applied in sensitive information protection, data leakage detection, and other aspects. The current approaches to entity relation extraction not only ignore the relevance and dependency between name entity recognition and relation extraction but also may result in the cumulative propagation of errors. To solve this problem, it is proposed that an end-to-end joint entity and relation extraction model based on the Attention mechanism and Graph Convolutional Network (GCN) to simultaneously extract named entities and their relationships. The model includes three parts: the detection of entity span, the construction of an entity relation weighted graph, and the inference of entity relation type. Firstly, the detection of entity spans is viewed as a sequence labeling problem, and a multi-feature fusion approach for word embedding representation is designed to calculate all entity spans in a sentence to form an entity span matrix. Secondly, the entity span matrix is employed in the Multi-Head Attention mechanism for constructing the weighted adjacency matrix of the entity relation graph. Finally, for the inference of entity relation type, considering the interaction between entities and relations, the entity span matrix and relation connection matrix are simultaneously fed into the GCN for integrated extraction of entities and relations. Our model is evaluated on the public NYT dataset, attaining a precision of 66.4%, a recall of 63.1%, and an F1 score of 64.7% for joint entity and relation extraction, significantly outperforming other approaches. Experiments demonstrate that the proposed model is helpful for inferring entities and relations, considering the interaction between entities and relations through the Attention mechanism and GCN. Full article

The computer network has fundamentally transformed modern interactions, enabling the effortless transmission of multimedia data. However, the openness of these networks necessitates heightened attention to the security and confidentiality of multimedia content. Digital images, being a crucial component of multimedia communications, require robust protection measures, as their security has become a global concern. Traditional color image encryption/decryption algorithms, such as DES, IDEA, and AES, are unsuitable for image encryption due to the diverse storage formats of images, highlighting the urgent need for innovative encryption techniques. Chaos-based cryptosystems have emerged as a prominent research focus due to their properties of randomness, high sensitivity to initial conditions, and unpredictability. These algorithms typically operate in two phases: shuffling and replacement. During the shuffling phase, the positions of the pixels are altered using chaotic sequences or matrix transformations, which are simple to implement and enhance encryption. However, since only the pixel positions are modified and not the pixel values, the encrypted image’s histogram remains identical to the original, making it vulnerable to statistical attacks. In the replacement phase, chaotic sequences alter the pixel values. This research introduces a novel encryption technique for color images (RGB type) based on DNA subsequence operations to secure these images, which often contain critical information, from potential cyber-attacks. The suggested method includes two main components: a high-speed permutation process and adaptive diffusion. When implemented in the MATLAB software environment, the approach yielded promising results, such as NPCR values exceeding 98.9% and UACI values at around 32.9%, demonstrating its effectiveness in key cryptographic parameters. Security analyses, including histograms and Chi-square tests, were initially conducted, with passing Chi-square test outcomes for all channels; the correlation coefficient between adjacent pixels was also calculated. Additionally, entropy values were computed, achieving a minimum entropy of 7.0, indicating a high level of randomness. The method was tested on specific images, such as all-black and all-white images, and evaluated for resistance to noise and occlusion attacks. Finally, a comparison of the proposed algorithm’s NPCR and UAC values with those of existing methods demonstrated its superior performance and suitability. Full article

The current strategy for treating osteomyelitis includes surgical procedures for complete debridement of the formed biofilm and necrotic tissues, systemic and oral antibiotic therapy, and the clinical use of cements and three-dimensional scaffolds as bone defect fillers and delivery systems for therapeutic agents. The aim of our research was to formulate a low-cost hybrid nanoparticulate biomaterial using poly(lactic-co-glycolic acid) (PLGA), in which we incorporated the therapeutic agent (ciprofloxacin), and to deposit this material on titanium plates using the matrix-assisted pulsed laser evaporation (MAPLE) technique. The deposited material demonstrated antibacterial properties, with all analyzed samples inhibiting the growth of tested bacterial strains, confirming the release of active substances from the investigated biocomposite. The poly(lactic-co-glycolic acid)-ciprofloxacin (PLGA-CIP) nanoparticle scaffolds displayed a prolonged local sustained release profile over a period of 45 days, which shows great promise in bone infections. Furthermore, the burst release ensures a highly efficient concentration, followed by a constant sustained release which allows the drug to remain in the implant-adjacent area for an extended time period. Full article

Timely and accurate traffic flow prediction is crucial for stabilizing road conditions, reducing environmental pollution, and mitigating economic losses. While current graph convolution methods have achieved certain results, they do not fully leverage the true advantages of graph convolution. There is still room for improvement in simultaneously addressing multi-graph convolution, optimizing graphs, and simulating road conditions. Based on this, this paper proposes MSA-GCN: Multistage Spatio-Temporal Aggregation Graph Convolutional Networks for Traffic Flow Prediction. This method overcomes the aforementioned issues by dividing the process into different stages and achieves promising prediction results. In the first stage, we construct a latent similarity adjacency matrix and address the randomness interference features in similarity features through two optimizations using the proposed ConvGRU Attention Layer (CGAL module) and the Causal Similarity Capture Module (CSC module), which includes Granger causality tests. In the second stage, we mine the potential correlation between roads using the Correlation Completion Module (CC module) to create a global correlation adjacency matrix as a complement for potential correlations. In the third stage, we utilize the proposed Auto-LRU autoencoder to pre-train various weather features, encoding them into the model’s prediction process to enhance its ability to simulate the real world and improve interpretability. Finally, in the fourth stage, we fuse these features and use a Bidirectional Gated Recurrent Unit (BiGRU) to model time dependencies, outputting the prediction results through a linear layer. Our model demonstrates a performance improvement of 29.33%, 27.03%, and 23.07% on three real-world datasets (PEMSD8, LOSLOOP, and SZAREA) compared to advanced baseline methods, and various ablation experiments validate the effectiveness of each stage and module. Full article

A graph G is singular if its adjacency matrix is singular. The starting vertices of two paths $P_{b_1}$ and $P_{b_2}$ are simultaneously bound to the ending vertex of the path $P_{s_1}$, and the ending vertices of the paths $P_{b_1}$ and $P_{b_2}$ are bound to the starting vertex of path $P_{s_2}$. Meanwhile, the starting vertex of the path $P_{s_1}$ is bound to a vertex of the cycle $C_{a_1}$, and the ending vertex of the path $P_{s_2}$ is bound to a vertex of the cycle $C_{a_2}$. Thus, the resulting graph is written as $\xi (a_1,a_2,b_1,b_2,s_1,s_2)$. This is denoted by $\zeta (a_1,a_2,b_1,b_2,s)=\xi (a_1,a_2,b_1,b_2,1,s)$ and $\epsilon (a_1,a_2,b_1,b_2)=\zeta (a_1,a_2,b_1,b_2,1)$, which are referred to as the $\xi$-graph, $\zeta$-graph and $\epsilon$-graph for short, respectively. It is known that there are 15 kinds of tricyclic graphs. The purpose of this paper is to study the necessary and sufficient conditions for $\xi$-graphs, $\zeta$-graphs and $\epsilon$-graphs to be singular graphs. We analyzed the structure of the elementary spanning subgraphs of the graph $G=\xi (a_1,a_2,b_1,b_2,s_1,s_2)$. By calculating the determinant of the adjacency matrix of the graph G, the necessary and sufficient conditions for the determinant of the graph G to be zero is obtained, and so the necessary and sufficient conditions for graph $\xi (a_1,a_2,b_1,b_2,s_1,s_2)$ to be singular are obtained. As the corollaries, the necessary and sufficient conditions for graphs $\zeta (a_1,a_2,b_1,b_2,s)$ and $\epsilon (a_1,a_2,b_1,b_2)$ to be singular are also obtained. Full article

Accurate and timely fault diagnosis is of great significance for the stable operation of a distribution network. Traditional artificial intelligence-based localization methods rely heavily on large-scale labeled datasets, making them prone to being affected by distributed generators. To address this issue, this study proposes a fault location method for distribution systems using a cost-sensitive graph attention network model. In this approach, the physical structure of the distribution network is considered a crucial constraint for model training, thereby enhancing its information perception capabilities. Specifically, the electrical nodes and lines of the distribution network are mapped to the vertices and edges within the graph attention network, with the attention weights determined by the correlation of adjacent node fault characteristics, thus improving the sensitivity to grounding faults. Additionally, a cost-sensitive matrix is used to balance class distribution, enhancing the robustness and generalization ability of the model. Fault localization experiments were conducted on the IEEE-33 bus distribution system to validate the effectiveness of the proposed fault localization method. Factors such as data disturbance, varying fault grounding resistances, and distributed power supply access were employed to assess the model’s anti-interference performance. The experimental results demonstrate that the fault location method exhibits high positioning accuracy and excellent robustness. Full article

Accurate prediction of ship traffic flow is essential for developing intelligent maritime transportation systems. To address the complexity of ship traffic flow data in the port and the challenges of capturing its dynamic spatiotemporal dependencies, a dynamic spatiotemporal model called Temporal convolutional network-bidirectional Gated recurrent unit-Pearson correlation coefficient-Graph Attention Network (TG-PGAT) is proposed for predicting traffic flow in port waters. This model extracts spatial features of traffic flow by combining the adjacency matrix and spatial dynamic coefficient correlation matrix within the Graph Attention Network (GAT) and captures temporal features through the concatenation of the Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BiGRU). The proposed TG-PGAT model demonstrates higher prediction accuracy and stability than other classic traffic flow prediction methods. The experimental results from multiple angles, such as ablation experiments and robustness tests, further validate the critical role and strong noise resistance of different modules in the TG-PGAT model. The experimental results of visualization demonstrate that this model not only exhibits significant predictive advantages in densely trafficked areas of the port but also outperforms other models in surrounding areas with sparse traffic flow data. Full article

The graph spectra analyze the structure of the graph using eigenspectra. The spectral graph theory deals with the investigation of graphs in terms of the eigenspectrum. In this paper, the sequence of lower bounds for the spectral radius of digraph $\mathcal{D}$ having at least one doubly adjacent vertex in terms of indegree is proposed. Particularly, it is exhibited that $\rho (\mathcal{D})\ge {\alpha }^j=\sqrt{{\displaystyle \frac{{\sum }_{p=1}^m{({\chi }_{j+1}^{(p)})}^2}{{\sum }_{p=1}^m{({\chi }_j^{(p)})}^2}}}$, such that equality is attained iff $\mathcal{D}=\overleftrightarrow{\mathfrak{G}}+$ {$\mathcal{DE}\notin$ Cycle}, where each component of associated graph is a k-regular or $(k_1,k_2)$ semiregular bipartite. By utilizing the sequence of lower bounds of the spectral radius of $\mathcal{D}$, the sequence of upper bounds of energy of $\mathcal{D}$, where the sequence decreases when $\sqrt{{\displaystyle \frac{\mathbf{e}}{\mathfrak{U}}}}\le {\alpha }^j$ and increases when $\sqrt{{\displaystyle \frac{\mathbf{e}}{\mathfrak{U}}}}>{\alpha }^j$, are also proposed. All of the obtained inequalities are elaborated using examples. We also discuss the monotonicity of these sequences. Full article