Search Results (128)

As the scale of shipboard power systems expands, their vulnerability becomes increasingly prominent. Identifying vulnerable points in ship power grids is essential for enhancing system stability, optimizing overall performance, and ensuring safe navigation. To address this issue, this paper proposes an algorithm based on fuzzy local dimension and topology (FLDT). The algorithm distinguishes contributions from nodes at different radii and within the same radius to a central node using fuzzy sets, and then derives the final importance value of each node by combining the local dimension and topology. Experimental results on nine datasets demonstrate that the FLDT algorithm outperforms degree centrality (DC), closeness centrality (CC), local dimension (LD), fuzzy local dimension (FLD), local link similarity (LLS), and mixed degree decomposition (MDD) algorithms in three metrics: network efficiency (NE), largest connected component (LCC), and monotonicity. Furthermore, in a ship power grid experiment, when 40% of the most important nodes were removed, FLDT caused a network efficiency drop of 99.78% and reduced the LCC to 2.17%, significantly outperforming traditional methods. Additional experiments under topological perturbations—including edge addition, removal, and rewiring—also show that FLDT maintains superior performance, highlighting its robustness to structural changes. This indicates that the FLDT algorithm is more effective in identifying and evaluating vulnerable points and distinguishing nodes with varying levels of importance. Full article

This study examines the daily top 100 cryptocurrencies on Uniswap V3. It denoises the correlation coefficient matrix of cryptocurrencies by using sliding window techniques and random matrix theory. Further, this study constructs a time-varying correlation network of cryptocurrencies under different thresholds based on complex network methods and analyzes the Uniswap V3 network’s time-varying topological properties and risk contagion intensity of Uniswap V3. The study findings suggest the presence of random noise on the Uniswap V3 cryptocurrency market. The strength of connection relationships in cryptocurrency networks varies at different thresholds. With a low threshold, the cryptocurrency network shows high average degree and average clustering coefficient, indicating a small-world effect. Conversely, at a high threshold, the cryptocurrency network appears relatively sparse. Moreover, the Uniswap V3 cryptocurrency network demonstrates heterogeneity. Additionally, cryptocurrency networks exhibit diverse local time-varying characteristics depending on the thresholds. Notably, with a low threshold, the local time-varying characteristics of the network become more stable. Furthermore, risk contagion analysis reveals that WETH (Wrapped Ether) exhibits the highest contagion intensity, indicating its predominant role in propagating risks across the Uniswap V3 network. The novelty of this study lies in its capture of time-varying characteristics in decentralized exchange network topologies, unveiling dynamic evolution patterns in cryptocurrency correlation structures. Full article

A comprehensive investigation of the chemical bonding, electronic structure, and spectroscopic properties of the lanarkite-type Pb₂SO₅ (PSO) structure was conducted, for the first time, using density functional theory simulations. Thus, different functionals, PBE, PBE0, PBESOL, PBESOL0, BLYP, WC1LYP, and B3LYP, were used, and their results were compared to predict their fundamental properties accurately. All DFT calculations were performed using a triple-zeta valence plus polarization basis set. Among all the DFT functionals, PBE0 showed the best agreement with the experimental and theoretical data available in the literature. Our results also reveal that the [PbO₅] clusters were formed with five Pb–O bond lengths, with values of 2.29, 2.35, 2.57, 2.60, and 2.79 Å. Meanwhile, the [SO₄] clusters exhibited uniform S–O bond lengths of 1.54 Å. Also, a complete topological analysis based on Bader’s Quantum Theory of Atoms in Molecules (QTAIM) was applied to identify atom–atom interactions in the covalent and non-covalent interactions of the PSO structure. Additionally, PSO has an indirect band gap energy of 4.83 eV and an effective mass ratio ($m_h^{*}$/$m_e^{*}$) of about 0.192 (PBE0) which may, in principle, indicate a low degree of recombination of electron–hole pairs in the lanarkite structure. This study represents the first comprehensive DFT investigation of Pb₂SO₅ reported in the literature, providing fundamental insights into its electronic and structural properties. Full article

The human genome is widely transcribed, with part of these transcribed regions producing stably expressed protein-coding or non-coding RNAs. Long intergenic non-coding RNAs (lincRNAs) are significantly differentially expressed in various cell lines and tissues. However, the influence of their transcription events remains unclear. In this study, we constructed a human genomic interaction network and found frequent interactions between lincRNA genes and protein-coding genes that are highly related to the occupancy of RNA polymerase II on the lincRNA gene. Interestingly, in the human genome interaction networks, the degree of lincRNA genes was significantly higher than that of protein-coding genes. The promoter regions of the protein-coding genes interacting with the lincRNA genes are enriched with R-loop structures, indicating that lincRNA may influence the target genes through R-loop structures. These promoters were enriched in more transcription factor binding sites. Furthermore, the whole network and sub-network could be utilized to explore potential biomarkers of leukemia. We found that zinc finger protein 668 (ZNF668), eosinophil granule ontogeny transcript (EGOT), and glutamate metabotropic receptor 7 (GRM7) could serve as novel biomarkers for acute myeloid leukemia (LMAL). Pasireotide acetate (CAS No. 396091-76-2) represents a potential drug for LMAL patients. These results suggested that potential biomarkers and corresponding drugs for cancer could be identified based on lincRNA–promoter network/sub-network topological parameters. Full article

We propose a vertiport location-allocation methodology for urban air mobility (UAM) from the perspective of transportation network topology. The location allocation of vertiports within a transportation network is a crucial factor in determining the unique characteristics of UAM compared to existing transportation modes. However, as UAM is still in the pre-commercialization phase, with significant uncertainties, there are limitations in applying location-allocation models that optimize objective functions such as maximizing service coverage or minimizing travel distance. Instead, vertiport location allocation should be approached from a strategic perspective, taking into account public capital investments aimed at improving the transportation network by leveraging UAM’s distinct characteristics compared to existing urban transportation modes. Therefore, we present a methodology for evaluating the impact of vertiport location-allocation strategies on changes in transportation network topology. To analyze network topology, we use the Seoul Metropolitan railway network as the base network and construct scenarios where vertiports are allocated based on highly connected nodes and those prioritizing structurally vulnerable nodes. We then compare and analyze global network efficiency, algebraic connectivity, average shortest path length, local clustering coefficient, transitivity, degree assortativity and modularity. We confirm that while allocating vertiports based on network centrality improves connectivity compared to vulnerability-based allocation, the latter approach is superior in terms of network efficiency. Additionally, as the proportion of vertiports increases, the small-world property of the network rapidly increases, indicating that the vertiport network can fundamentally alter the structure of multimodal transportation systems. Regardless of whether centrality or vulnerability is prioritized, we observe that connectivity increase exponentially, while network efficiency changes linearly with the increase in vertiport proportion. Our findings highlight the necessity of a network-based approach to vertiport location allocation in the early stages of UAM commercialization, and we expect our results to inform future research directions on vertiport allocation in multimodal transportation networks. Full article

Fractals are geometric patterns that appear self-similar across all length scales and are constructed by repeating a single unit on a regular basis. Entropy, as a core thermodynamic function, is an extension based on information theory (such as Shannon entropy) that is used to describe the topological structural complexity or degree of disorder in networks. Topological indices, as graph invariants, provide quantitative descriptors for characterizing global structural properties. In this paper, we investigate two types of generalized Sierpiński graphs constructed on the basis of different seed graphs, and employ six topological indices—the first Zagreb index, the second Zagreb index, the forgotten index, the augmented Zagreb index, the Sombor index, and the elliptic Sombor index—to analyze the corresponding entropy. We utilize the method of edge partition based on vertex degrees and derive analytical formulations for the first Zagreb entropy, the second Zagreb entropy, the forgotten entropy, the augmented Zagreb entropy, the Sombor entropy, and the elliptic Sombor entropy. This research approach, which integrates entropy with Sierpiński network characteristics, furnishes novel perspectives and instrumental tools for addressing challenges in chemical graph theory, computer networks, and other related fields. Full article

Molecular fractals are geometric patterns that appear self-similar across all length scales and are constructed by repeating a single unit on a regular basis. Entropy, as a core thermodynamic function, is an extension based on information theory (such as Shannon entropy) and is used to describe the topological structural complexity or degree of disorder in networks. A topological index is a numeric quantity associated with a network or a graph that characterizes its whole structural properties. In this study, we focus on fractal structures formed by systematically repeating a fixed unit of coronene, a polycyclic aromatic hydrocarbon composed of six benzene rings fused in a hexagonal pattern. In this paper, three types of coronal fractal structures, namely zigzag (ZHCF), armchair (AHCF), and rectangular (RCF), are studied, and their five degree-based topological indices and corresponding entropies are calculated. Full article

Background/Objectives: Infectious diseases caused by Staphylococcus aureus (S. aureus) have become alarming health issues worldwide due to the ever-increasing emergence of multidrug resistance. In silico approaches can accelerate the identification and/or design of versatile antibacterial chemicals with the ability to target multiple S. aureus strains with varying degrees of drug resistance. Here, we develop a perturbation theory machine learning model based on a multilayer perceptron neural network (PTML-MLP) for the prediction and design of versatile virtual inhibitors against S. aureus strains. Methods: To develop the PTML-MLP model, chemical and biological data associated with antibacterial activity against S. aureus strains were retrieved from the ChEMBL database. We applied the Box–Jenkins approach to convert the topological indices into multi-label graph-theoretical indices; the latter were used as inputs for the creation of the PTML-MLP model. Results: The PTML-MLP model exhibited accuracy higher than 80% in both training and test sets. The physicochemical and structural interpretation of the PTML-MLP model was performed through the fragment-based topological design (FBTD) approach. Such interpretations permitted the analysis of different molecular fragments with favorable contributions to the multi-strain antibacterial activity and the design of four new drug-like molecules using different fragments as building blocks. The designed molecules were predicted/confirmed by our PTML model as multi-strain inhibitors of diverse S. aureus strains, thus representing promising chemotypes to be considered for future synthesis and biological testing of versatile anti-S. aureus agents. Conclusions: This work envisages promising applications of PTML modeling for early antibacterial drug discovery and related antimicrobial research areas. Full article

There is relatively little research on cyclic amphiphilic block polymers, having both hydrophilic and hydrophobic segments placed in the ring and thus resulting in a higher degree of topological restriction, as drug vehicles. Cyclic amphiphilic binary block polymer is synthesized by the click coupling reaction of bimolecular homodifunctional precursors. The results indicate that cyclization between linear polymer precursors is successful if the trace linear by-products generated are ignored, which also suggests that the small molecule bifunctional terminating agent applied in traditional bimolecular homodifunctional ring-closure process can be extended to large molecule. Moreover, the study on the self-assembly behavior of polymers shows that, compared with linear counterparts, the stability and drug loading capacity of micelles based on the resultant cyclic polymer are not significantly improved due to the influence of topological structure and linear impurities. Nevertheless, drug loaded micelles formed by the obtained cyclic polymers still exhibit superior cellular uptake ability. It can be seen that topological effects do play an irreplaceable role in the application performance of polymers. Therefore, the construction and synthesis of cyclic and its derivative polymers with moderate topological confinement and high purity may be a key direction for future exploration of polymer drug delivery carriers. Full article

Several topological indices are possibly the most widely applied graph-based molecular structure descriptors in chemistry and pharmacology. The capacity of topological indices to discriminate is a crucial component of their study. In light of this, the literature has introduced the exponential vertex-degree-based topological index. The exponential atom-bond connectivity index is defined as follows: $e^{\mathcal{ABC}}=e^{\mathcal{ABC}}({\rm Y})={\displaystyle \sum _{v_i{v}_j\in E({\rm Y})}}{e}^{\sqrt{{\displaystyle \frac{d_i+d_j-2}{d_i{d}_j}}}},$ where $d_i$ is the degree of the vertex $v_i$ in ${\rm Y}$. In this paper, we prove that the double star $D{S}_{n-3,1}$ is the second maximal graph with respect to the $e^{\mathcal{ABC}}$ index of trees of order n. We give an upper bound on $e^{\mathcal{ABC}}$ of unicyclic graphs of order n and characterize the maximal graphs. The graph $K_1\vee (P_3\cup (n-4)K_1)$ gives the maximal graph with respect to the $e^{\mathcal{ABC}}$ index of bicyclic graphs of order n. We present several relations between $e^{\mathcal{ABC}}({\rm Y})$ and $ABC({\rm Y})$ of graph ${\rm Y}$. Finally, we provide a conclusion summarizing our findings and discuss potential directions for future research. Full article

Currently, the rapid development of social media enables people to communicate more and more frequently in the network. Classifying user activities in social networks helps to better understand user behavior in social networks. This paper first creates an ego network for each user, encodes the higher-order topological features of the ego network as persistence diagrams using persistence homology, and computes the persistence entropy. Then, based on the persistence entropy, this paper defines the Norm Entropy-NE(X) to represent the complexity of the topological features of the ego network, a larger NE(X) indicates a higher topological complexity, i.e., the higher the activity of the nodes, thus indicating the degree of activity of the nodes. The paper uses the extracted set of feature vectors to train the machine learning model to classify the users in the social network. Numerical experiments are conducted to evaluate the performance of clustering quality metrics such as profile coefficients. The results show that the proposed algorithm can effectively classify social network users into different groups, which provides a good foundation for further research and application. Full article

In this paper, we examine an ultra-compact on-chip balanced filter based on novel hybrid resonators (NHRs) comprising short transmission line sections (STLSs) and series LC blocks using low-temperature co-fired ceramic (LTCC) technology. Based on a rigorous theoretical analysis, the proposed NHR demonstrates the potential for intrinsic ultra-wideband differential-mode (DM) and common-mode (CM) suppression without any additional suppressing structures. Furthermore, the resonance of NHRs was determined by four degrees of freedom, providing flexibility for miniaturization. Theoretical extensions of the Nth-order topology can be easily achieved by the simple coupling schemes that occur exclusively between STLSs. For verification, a balanced filter covering the 5G band n78 with an area of 0.065λ_g × 0.072λ_g was designed using the proposed optimization-based design procedure. An ultra-low insertion loss of 0.8 dB was obtained. The quasi-full CM stopband with a 20 dB rejection level ranged from 0 to 12.9 GHz. And the ultra-wide upper DM stopband with a 20 dB rejection level ranged from 4.4 to 11.5 GHz. Good agreement between the theoretical, simulated, and measured results indicate the validity of the proposed design principle. Full article

The NAC gene family plays a crucial role in plant growth, development, and responses to biotic and abiotic stresses. Paspalum Vaginatum, a warm-season turfgrass with exceptional salt tolerance, can be irrigated with seawater. However, the NAC gene family in seashore paspalum remains poorly understood. In this study, genome-wide screening and identification were conducted based on the NAC (NAM) domain hidden Markov model in seashore paspalum, resulting in the identification of 168 PvNAC genes. A phylogenetic tree was constructed, and the genes were classified into 18 groups according to their topological structure. The physicochemical properties of the PvNAC gene family proteins, their conserved motifs and structural domains, cis-acting elements, intraspecific collinearity analysis, GO annotation analysis, and protein–protein interaction networks were analyzed. The results indicated that the majority of PvNAC proteins are hydrophilic and predominantly localized in the nucleus. The promoter regions of PvNACs are primarily enriched with light-responsive elements, ABRE motifs, MYB motifs, and others. Intraspecific collinearity analysis suggests that PvNACs may have experienced a large-scale gene duplication event. GO annotation indicated that PvNAC genes were essential for transcriptional regulation, organ development, and responses to environmental stimuli. Furthermore, the protein interaction network predicted that PvNAC73 interacts with proteins such as BZIP8 and DREB2A to form a major regulatory hub. The transcriptomic analysis investigates the expression patterns of NAC genes in both leaves and roots under varying durations of salt stress. The expression levels of 8 PvNACs in roots and leaves under salt stress were examined and increased to varying degrees under salt stress. The qRT-PCR results demonstrated that the expression levels of the selected genes were consistent with the FPKM value trends observed in the RNA-seq data. This study established a theoretical basis for understanding the molecular functions and regulatory mechanisms of the NAC gene family in seashore paspalum under salt stress. Full article

As a range-free localization algorithm, DV-Hop has gained widespread attention due to its advantages of simplicity and ease of implementation. However, this algorithm also has some defects, such as poor localization accuracy and vulnerability to network topology. This paper presents a comprehensive analysis of the factors contributing to the inaccuracy of the DV-Hop algorithm. An improved proportional integral derivative (PID) search algorithm (PSA) DV-Hop hybrid localization algorithm based on weighted hyperbola (IPSA-DV-Hop) is proposed. Firstly, the first hop distance refinement is employed to rectify the received signal strength indicator (RSSI). In order to replace the original least squares solution, a weighted hyperbolic algorithm based on the degree of covariance is adopted. Secondly, the localization error is further reduced by employing the improved PSA. In addition, the selection process of the node set is optimized using progressive sample consensus (PROSAC) followed by a 3D hyperbolic algorithm based on coplanarity. This approach effectively reduces the computational error associated with the hopping distance of the beacon nodes in the 3D scenarios. Finally, the simulation experiments demonstrate that the proposed algorithm can markedly enhance the localization precision in both isotropic and anisotropic networks and reduce the localization error by a minimum of 30% in comparison to the classical DV-Hop. Additionally, it also exhibits stability under the influence of a radio irregular model (RIM). Full article

A vertex-degree-based topological index $\phi$ associates a real number to a graph G which is invariant under graph isomorphism. It is defined in terms of the degrees of the vertices of G and plays an important role in chemical graph theory, especially in QSPR/QSAR investigations. A subset of k edges in G with no common vertices is called a k-matching of G, and the number of such subsets is denoted by $m\left(G,k\right)$. Recently, this number was naturally extended to weighted graphs, where the weight function is induced by the topological index $\phi$. This number was denoted by $m_k\left(G,\phi \right)$ and called the k-matchings of G with respect to the topological index $\phi$. It turns out that $m_1\left(G,\phi \right)=\phi \left(G\right),$ and so for $k\ge 2,$ the k-matching numbers $m_k\left(G,\phi \right)$ can be viewed as kth order topological indices which involve both the topological index $\phi$ and the k-matching numbers. In this work, we solve the extremal value problem for the number of 2-matchings with respect to general sum-connectivity indices ${\mathcal{SC}}_{\mathit{\alpha }}$, over the set ${\mathcal{T}}_n$ of trees with n vertices, when $\alpha$ is a real number in the interval $\left[-1,0\right).$ Full article