Search Results (187)

In cybersecurity, constructing an accurate knowledge graph is vital for discovering key entities and relationships in security incidents buried in vast unstructured threat reports. Traditional knowledge-graph construction pipelines based on handcrafted rules or conventional machine learning models falter when the data scale and linguistic variety grow. GraphRAG, a retrieval-augmented generation (RAG) framework that splits documents into fixed-length chunks and then retrieves the most relevant ones for generation, offers a scalable alternative yet still suffers from fragmentation and semantic gaps that erode graph integrity. To resolve these issues, this paper proposes SC-LKM, a cybersecurity knowledge-graph construction method that couples the GraphRAG backbone with hierarchical semantic chunking. SC-LKM applies semantic chunking to build a cybersecurity knowledge graph that avoids the fragmentation and inconsistency seen in prior work. The semantic chunking method first respects the native document hierarchy and then refines boundaries with topic similarity and named-entity continuity, maintaining logical coherence while limiting information loss during the fine-grained processing of unstructured text. SC-LKM further integrates the semantic comprehension capacity of Qwen2.5-14B-Instruct, markedly boosting extraction accuracy and reasoning quality. Experimental results show that SC-LKM surpasses baseline systems in entity-recognition coverage, topology density, and semantic consistency. Full article

We present a theoretical model of the hydrogenation and amination of a primal carbon cluster of the tangled polycyclic type. Hydrogen atoms were introduced via H₂, while the nitrogen source was NH₃. The initial chemical processes were modeled using Born–Oppenheimer Molecular Dynamics. Metadynamics was employed to accelerate the saturation. The reactions were characterized in terms of barriers, topology, and intricate changes in the electronic structure. All transition states were identified. Multiple mechanisms for each type of reaction were discovered. Occasional unbiased changes in the carbon skeleton, induced by the guided processes, were observed. The initial addition reactions had no barriers due to the instability and high reactivity of the carbon structure. The final product of barrierless hydrogen saturation was C₂₅H₂₆. This molecule included multiple isolated double bonds, a medium-sized conjugated π system, and no triple bonds. Ammonia additions resulted in quaternary ammonium groups and primary amino groups. In the subsequent amination, a barrier appeared in fewer steps than in repetitive hydrogenation. The final product of barrierless saturation with NH₃ was C₂₅H₂(NH₃)₂NH₂. Further amination was characterized by a forward free-energy barrier of an order of magnitude larger than the reverse reaction, and the product was found to be unstable. Full article

Substitutional doping of fullerenes represents a significant category of heterofullerenes. Due to the vast number of isomers, confirming the ground state structure poses considerable challenges. In this study, we generated isomers of C_60−nB_n and C_60−nN_n with n ranging from 2 to 12. To avoid overlooking the ground state structures, we applied specific filtering rules: no adjacent nitrogen (N) or boron (B) atoms are allowed, and substitutions in meta-positions within pentagons are prohibited when the substitution number n exceeds nine. Approximately 15,000 isomers across various values of n within the range of 2 to 12 for B and N substituted fullerenes were selected and optimized using density functional theory (DFT) calculations, forming our dataset. We developed a Graph Neural Network (GNN) that aggregates both topological connections and its dual graph with ring types as input information to predict their binding energies. The GNN achieved high accuracy, reaching a root mean square error (RMSE) of 1.713 meV. Furthermore, it operates efficiently; indeed, it can predict over six thousand isomers per second on an eight-core PC. Several predicted stable structures were further optimized by DFT to confirm their ground state configurations. The energy cutoffs of each composition were determined through statistical simulations to ensure that the selected ground state structures possess high confidence levels. Notably, new lower-energy structures have been discovered for boron-substituted fullerenes with substitution number ranging from seven to twelve and nitride-substituted fullerenes with substitution number ranging from seven to eleven. Full article

Discovering spatially associated objects involves measuring objects’ similarities and retrieving associated objects. The integration of spatial topology and network models for discovering associated objects remains largely unexplored. Here, the concept of a maximum topological accessibility path was developed to quantify objects’ similarity attenuation. Considering the topological accessibility and spatial feature similarity of network nodes, an approach named the Weighted Similarity measure method considering Topological Accessibility (WSTA) is proposed to measure object association. The WSTA can capture both spatial interaction patterns and topological relationships in complex urban environments, thereby improving the accuracy of spatially associated object discovery. The proposed approach is validated using real-world point-of-interest (POI) datasets from Beijing city. The results suggest that integrating topological relationship approaches yields significant accuracy improvements in existing baseline methods, thereby enriching geospatial data retrieval in the era of big geospatial data. Full article

The newly discovered 2D spin-gapless magnetic materials, which provide new opportunities for combining spin polarization and the quantum anomalous Hall effect, provide a new method for the design and application of memory and nanoscale devices. However, a low Curie temperature (T_C) is a common limitation in most 2D ferromagnetic materials, and research on the topological properties of nontrivial 2D spin-gapless materials is still limited. We predict a novel spin-gapless semiconductor of monolayer h-VN, which has a high Curie temperature (~543 K), 100% spin polarization, and nontrivial topological properties. A nontrivial band gap is opened in the spin-gapless state when considering the spin–orbit coupling (SOC); it can increase with the intensity of spin–orbit coupling and the band gap increases linearly with SOC. By calculating the Chern number and edge states, we find that when the SOC strength is less than 250%, the monolayer h-VN is a quantum anomalous Hall insulator with a Chern number C = 1. In addition, the monolayer h-VN still belongs to the quantum anomalous Hall insulators with its tensile strain. Interestingly, the quantum anomalous Hall effect with a non-zero Chern number can be maintained when using h-BN as the substrate, making the designed structure more suitable for experimental implementation. Our results provide an ideal candidate material for achieving the QAHE at a high Curie temperature. Full article

The first immune response controls many bacterial and viral inflammatory diseases. Oral immunization with cholera toxin (CT) elicits antibodies and can prevent cholerae in endemic environments. While the IgG immune response to the toxin is well-documented, the IgA and IgM epitopes responsible for the initial immune reaction to the toxin remained uncharted. In this study, our objective was to identify and characterize immunologically and structurally these IgA and IgM epitopes. We conducted SPOT synthesis to create two libraries, each containing one hundred twenty-two 15-mer peptides, encompassing the entire sequence of the three chains of the CT protein. We could map continuous IgA and IgM epitopes by testing these membrane-bound peptides with sera from mice immunized with an oral vaccine (Schankol™). Our approach involved topological studies, peptide synthesis, and the development of an ELISA. We successfully identified seven IgA epitopes, two in CTA, two in CTB, and three in protein P. Additionally, we discovered eleven IgM epitopes, all situated within CTA. Three IgA-specific and three IgM-specific epitopes were synthesized as MAP4 and validated using ELISA. We then used two chimeric 45-mer peptides, which included these six epitopes, to coat ELISA plates and screened them with sera from immunized mice. This yielded sensitivities and specificities of 100%. Our findings have unveiled a significant collection of IgA and IgM-specific peptide epitopes from cholera toxins A, B, and P. These epitopes, along with those IgG previously identified by our group, reflect the immunoreactivity associated with the dynamic of the immunoglobulins switching associated with the cholera toxin vaccination. Full article

With the rapid development of complex network science, exploring the characteristics of nodes and their interrelationships in networks has emerged as a topical issue which has been extensively applied in a variety of scenarios, such as market analysis, social networks, and recommendation systems. In this paper, a complex network node clustering method based on graph contrastive learning is proposed in combination with a topology of the network and a behavioral analysis of the network nodes, which is used to deeply mine the preferences and behavioral patterns of the network nodes in order to formulate a differentiated recommendation strategy. The model automatically learns the deep feature representation of data by optimizing the distance relationship between positive and negative sample pairs, especially when dealing with complex and heterogeneous data, and is able to capture the underlying structure that is difficult to discover using traditional methods. Meanwhile, the model captures the global structure of the data by utilizing the correlation between data points and mapping the high-dimensional data to the low-dimensional space, which provides strong robustness and high clustering accuracy when dealing with non-linearly differentiable data. The research in this paper not only provides new ideas for clustering research in complex networks but also promotes the application of related methods of complex networks in multiple fields, which has important theoretical significance and practical value. Full article

While there is enormous interest in studying oxide perovskites with stoichiometries based upon or derived from ABO₃, including oxygen-deficient compositions and organometallics, other closely related topologies have been overlooked. Hydroxyperovskites are such a group. Their structures are perovskite-like octahedral frameworks with vacant cavity A sites, and all oxygen atoms form hydroxyl groups. There are fifteen naturally occurring hydroxyperovskites and numerous synthetic analogues. There are two stoichiometries: BB′(OH)₆ and B(OH)₃. The former consist of alternating divalent and tetravalent cations (B = Mg, Ca, Mn²⁺, Fe²⁺, Co²⁺, Cu²⁺, Zn; B′ = Sn, Ge). B(OH)₃ structures have only trivalent cations (Al, Fe³⁺, Ga). The properties and behavior of solid solutions in hydroxyperovskites are largely unexplored. This article summarizes our current knowledge of the crystallography and crystal chemistry of hydroxyperovskites and suggests productive areas of research in relation to their potential as functional materials. It should be evident that much of the findings remains to be discovered. Full article

Shapes and topologies of lattice materials have been extensively studied, yet very few studies have dealt with shapes inspired by ancient mathematicians, such as the Platonic solids discovered by Plato in 360 BC or the mathematical behavior of the unexplored “semi-regular” solids of Pacioli (1445–1517). Using the finite element analysis method, the buckling and post-buckling behavior of Platonic and Paciolian cells subjected to a compressive load were analyzed. In these solids, the energy absorbed per unit mass is an increasing function with the number of faces, similar to porosity, which reaches a maximum value for solids comprised of 90–100 surfaces. Full article

This paper considers a decentralized and autonomous wireless network of low SWaP (size, weight, and power) fixed-wing UAVs (unmanned aerial vehicles) used for remote exploration and monitoring of targets in an inaccessible area lacking communication infrastructure. Here, the UAVs collaborate to find target(s) and use routing protocols to forward the sensed data of target(s) to an aerial base station (BS) in real-time through multihop communication, which can then transmit the data to a control center. However, the unpredictability of target locations and the highly dynamic nature of autonomous, decentralized UAV networks result in frequent route breaks or traffic disruptions. Traditional routing schemes cannot quickly adapt to dynamic UAV networks and can incur large control overhead and delays. In addition, their performance suffers from poor network connectivity in sparse networks with multiple objectives (exploration and monitoring of targets), which results in frequent route unavailability. To address these challenges, we propose two routing schemes: Pipe routing and TC-Pipe routing. Pipe routing is a mobility-, congestion-, and energy-aware scheme that discovers routes to the BS on-demand and proactively switches to alternate high-quality routes within a limited region around the routes (referred to as the “pipe”) when needed. TC-Pipe routing extends this approach by incorporating a decentralized topology control mechanism to help maintain robust connectivity in the pipe region around the routes, resulting in improved route stability and availability. The proposed schemes adopt a novel approach by integrating the topology control with routing protocol and mobility model, and rely only on local information in a distributed manner. Comprehensive evaluations under diverse network and traffic conditions—including UAV density and speed, number of targets, and fault tolerance—show that the proposed schemes improve throughput by reducing flow interruptions and packet drops caused by mobility, congestion, and node failures. At the same time, the impact on coverage performance (measured in terms of coverage and coverage fairness) is minimal, even with multiple targets. Additionally, the performance of both schemes degrades gracefully as the percentage of UAV failures in the network increases. Compared to schemes that use dedicated UAVs as relay nodes to establish a route to the BS when the UAV density is low, Pipe and TC-Pipe routing offer better coverage and connectivity trade-offs, with the TC-Pipe providing the best trade-off. Full article

G-quadruplexes (G4s) are unique nucleic acid structures composed of guanine-rich (G-rich) sequences that can form diverse topologies based on the arrangement of their four strands. G4s have attracted attention for their potential roles in various biological processes and human diseases. In this review, we focus on the G4 structures formed by human telomeric sequences, (GGGTTA)_n, and the hexanucleotide repeat expansion, (GGGGCC)_n, in the first intron region of the chromosome 9 open reading frame 72 (C9orf72) gene, highlighting their structural diversity and biological significance. Human telomeric G4s play crucial roles in telomere retention and gene regulation. In particular, we provide an in-depth summary of known telomeric G4s and focus on our recently discovered chair-type conformation, which exhibits distinct folding patterns. The chair-type G4s represent a novel folding pattern with unique characteristics, expanding our knowledge of telomeric G4 structural diversity and potential biological functions. Specifically, we emphasize the G4s formed by the (GGGGCC)_n sequence of the C9orf72 gene, which represents the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). The thorough structural analysis in this review advances our comprehension of the disease mechanism and provides valuable insights into developing targeted therapeutic strategies in ALS/FTD. Full article

In this work, an efficient graph-induced neighborhood search heuristic is proposed to address the capacitated multicommodity network design problem. This problem, which commonly arises in transportation and telecommunication, is well known for its inherent complexity and is often classified as $\mathcal{NP}$-hard. Our approach commences with an arbitrary feasible solution and iteratively improves it by solving a series of small-scale auxiliary mixed-integer programming problems. These small-scale problems are closely tied to the cycles inherent in the network topology, enabling us to reroute the flow more effectively. Furthermore, we have developed a novel resource-efficient facility assignment technique that departs from standard variable neighborhood search algorithms. By solving a series of small knapsack problems, this technique not only enhances the quality of solutions further but also can serve as a primary heuristic to generate initial feasible solutions. Furthermore, we theoretically guarantee that our algorithm will always produce an integer-feasible solution within polynomial time. The experimental results highlight the superior performance of our method compared to other existing approaches. Our heuristic algorithm efficiently discovers high-quality feasible solutions, substantially reducing the computation time and number of nodes in the branch-and-bound tree. Full article

Scanning the large-scale topology of the IPv6 internet presents a significant challenge in network engineering, particularly for understanding the underlying network structure and assessing network security. The sheer size of the IPv6 address space makes traditional brute-force scanning techniques, such as traceroute, inefficient and impractical. Existing methodologies are often unable to cope with the inherent complexity of IPv6’s network structure and its probing requirements, leading to issues such as redundant probes, ICMPv6 rate limits, and network congestion. To address these challenges, this paper introduces 6Trace, an innovative solution that mitigates the impact of rate-limiting and congestion by distributing scanning traffic across the network. Furthermore, 6Trace incorporates a stateless, asynchronous scanning approach combined with a bisection-like dynamic probing strategy, significantly reducing redundancy. Experimental results demonstrate that 6Trace enhances scanning efficiency by 70% over current solutions, discovering the maximum number of interface addresses while minimizing probing time. Notably, this paper also provides the first comprehensive analysis of topology probing results across different types of target networks. The insights gained from this study will inform future research on optimal target selection and IPv6 internet measurement techniques. Full article

The phononic crystals composed of soft materials have received extensive attention owing to the extraordinary behavior when undergoing large deformations, making it possible to provide tunable band gaps actively. However, the inverse designs of them mainly rely on the gradient-driven or gradient-free optimization schemes, which require sensitivity analysis or cause time-consuming, lacking intelligence and flexibility. To this end, a deep learning-based framework composed of a conditional variational autoencoder and multilayer perceptron is proposed to discover the mapping relation from the band gaps to the topology layout applied with prestress. The nonlinear superelastic neo-Hookean model is employed to describe the constitutive characteristics, based on which the band structures are obtained via the transfer matrix method accompanied with Bloch theory. The results show that the proposed data-driven approach can efficiently and rapidly generate multiple candidates applied with predicted prestress. The band gaps are in accord with each other and also consistent with the prescribed targets, verifying the accuracy and flexibility simultaneously. Furthermore, based on the generalization performance, the design space is deeply exploited to obtain desired soft structures whose stop bands are characterized by wider bandwidth, lower location, and enhanced wave attenuation performance. Full article

In mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange different types of information, including control data that direct unmanned vehicle movements and sensor data that capture real-world environmental conditions. The GCS and nodes communicate wirelessly, leading to loss or delays in control and sensor data. Minimizing these issues is crucial to ensure nodes operate as intended over wireless links. In dynamic networks, distributed path calculation methods lead to increased network traffic, as each node independently exchanges control messages to discover new routes. This heightened traffic results in internal interference, causing communication delays and data loss. In contrast, software-defined networking (SDN) offers a centralized approach by calculating paths for all nodes from a single point, reducing network traffic. However, shifting from a distributed to a centralized approach with SDN does not inherently guarantee faster route creation. The speed of generating new routes remains independent of whether the approach is centralized, so SDN does not always lead to faster results. Therefore, a key challenge remains: determining how to create new routes as quickly as possible even within an SDN framework. This paper introduces a caching technique for forwarding rules based on predicted link states in SDN, which was named the CRIMSON (Cashing Routing Information in Mobile SDN Network) algorithm. The CRIMSON algorithm detects network link state changes caused by node mobility and caches new forwarding rules based on predicted topology changes. We validated that the CRIMSON algorithm consistently reduces end-to-end latency by an average of 88.96% and 59.49% compared to conventional reactive and proactive modes, respectively. Full article