Search Results (71)

Community detection is a key task in the analysis of complex networks, particularly in social network analysis, where uncovering cohesive and well-separated groups is essential for understanding structural organization and interaction patterns. Many existing centroid-based community detection methods rely primarily on node degree for centroid selection, which often leads to centroid crowding and insufficient spatial separation among communities. To address these limitations, this paper proposes Degree–Distance Centroid–Community Detection with Influential Nodes (DDC-CDIN), a distance-driven and influence-aware community detection framework. In the proposed approach, nodes are first ranked according to an Enhanced Degree Centrality measure that incorporates degree information, neighbourhood structure, and local clustering characteristics to identify structurally influential nodes. Centroids are then selected iteratively from the top-ranked influential nodes by maximizing shortest-path distances, ensuring that the chosen centroids are both representative and well dispersed within the network. Once the centroids are determined, the remaining nodes are assigned to communities based on the minimum geodesic distance, yielding compact, clearly separated clusters. Extensive experiments across multiple real-world networks show that DDC-CDIN achieves competitive performance compared to traditional centroid-based and modularity-driven methods in terms of modularity, community cohesion, and boundary clarity. The results indicate that jointly incorporating influence-aware node ranking with distance-based centroid dispersion effectively mitigates centroid crowding and enhances overall community detection quality. These findings demonstrate the effectiveness and robustness of DDC-CDIN for detecting well-structured and topologically coherent communities in complex networks. Full article

In this paper, we present and describe a new type of fuzzy open sets, called fuzzy $(\mathfrak{n},\mathfrak{m})$-e-open sets in double fuzzy topological spaces (DFT Ss,) based on Šostak’s approach. This type belongs to the group of fuzzy $(\mathfrak{n},\mathfrak{m})$-$\delta$-$\beta$-open sets and includes all fuzzy $(\mathfrak{n},\mathfrak{m})$-$\delta$-$\alpha$-open sets, fuzzy $(\mathfrak{n},\mathfrak{m})$-$\delta$-pre-open sets, and fuzzy $(\mathfrak{n},\mathfrak{m})$-$\delta$-semi-open sets. We then explore the idea of $DF$-e-continuity between DFT Ss $(Q,\Gamma ,{\Gamma }^{*})$ and $(K,\mathcal{T},{\mathcal{T}}^{*})$. We also introduce and examine the concepts of $DF$-almost e-continuity and $DF$-weakly e-continuity, which are less strict than $DF$-e-continuity. Subsequently, we introduce and analyze new $DF$ mappings through Fuzzy $(\mathfrak{n},\mathfrak{m})$-e-open and Fuzzy $(\mathfrak{n},\mathfrak{m})$-e-closed sets. Finally, we present and introduce some novel new types of $DF$-separation axioms, named Fuzzy $(\mathfrak{n},\mathfrak{m})$-e-regular and Fuzzy $(\mathfrak{n},\mathfrak{m})$-e-normal spaces, and we examine some of their properties. Full article

Background: The human gut microbiota plays a central role in host physiology by influencing digestion, immune function, and metabolism. Characterizing age-associated differences in the organization of microbial metabolism may provide insights into functional variation in the gut microbiome across the human lifespan. Methods: Gut microbiota metabolic organization was analyzed in a cohort of 30 individuals spanning three age groups (infants, adults, and elderly individuals) and comprising 156 stool samples. Community metabolic networks were reconstructed using the metabolic Directed Acyclic Graph (m-DAG) framework derived from KEGG Ortholog annotations. Network topology was characterized to assess whether the resulting networks conform to previously described global structural patterns and to examine age-associated variability. Pairwise m-DAG dissimilarities were computed, and hierarchical clustering was applied to evaluate similarities among samples. Results: All samples revealed a conserved global network organization, alongside marked variability in specific structural features. Hierarchical clustering did not strictly reflect chronological age. A homogeneous cluster composed exclusively of adult samples was identified, whereas elderly samples were distributed across two clusters, one grouping with adults and the other with infants. Exploratory discriminative analyses identified functional reactions contributing to the separation between the adult cluster and the remaining samples, indicating age-associated differences in metabolic network organization. Conclusions: Gut microbiota metabolic networks in adults tend to exhibit lower redundancy and structural complexity, whereas those in infant and elderly samples display more heterogeneous network configurations. This network-based analysis provides a functional perspective on age-associated variation in gut microbiota metabolism and offers a framework for future integrative studies. Full article

The family Capitellidae performs critical roles in bioturbation and sediment remediation within global marine benthic ecosystems. However, they are a taxonomically challenging group due to their simple morphology and a ‘morphological mosaic’, where traditional classificatory traits, such as thoracic chaetiger counts, appear convergently across genera. Previous multi-locus studies (using 18S, 28S, H3, and COI) first highlighted this conflict, revealing the polyphyly of major genera like Notomastus and even Leiochrides itself (based on unidentified specimens). More recently, mitogenomic studies uncovered massive gene order rearrangements and a conflicting topology but did not include Leiochrides. Critically, with no complete mitogenome reported for a formally identified Leiochrides species, its true phylogenetic position and the validity of its polyphyly remain unresolved. To address this critical gap, we sequenced and characterized the first complete mitochondrial genome from a formally identified species, Leiochrides yokjidoensis, recently described from Korean waters. The complete mitogenome was 17,933 bp in length and included the typical 13 protein-coding genes (PCGs), 2 ribosomal RNAs (rRNAs), and 22 transfer RNAs (tRNAs). Gene order (GO) analysis revealed the occurrence of gene rearrangements in Capitellidae and in its sister clade, Opheliidae. A phylogenomic analysis using the amino acid sequences of 13 PCGs from 30 species established the first robust systematic position for the genus Leiochrides (based on this formally identified species). Phylogenetic results recovered Leiochrides as a sister group to the clade comprising Mediomastus, Barantolla, Heteromastus, and Notomastus hemipodus (BS 99%). This distinct placement confirms that Leiochrides represents an independent evolutionary lineage, phylogenetically separate from the polyphyletic Notomastus complex, despite their morphological similarities. Furthermore, our analysis confirmed the polyphyly of Notomastus, with N. hemipodus clustering distinctly from other Notomastus species. Additionally, signatures of positive selection were detected in ND4, and ND5 genes, suggesting potential adaptive evolution to the subtidal environment. This placement provides a critical, high-confidence anchor point for the genus Leiochrides. It provides a reliable reference to investigate the unresolved polyphyly suggested by previous multi-locus studies and provides compelling evidence for the hypothesis that thoracic chaetiger counts are of limited value for inferring phylogenetic relationships. This study provides the foundational genomic cornerstone for Leiochrides, representing an essential first step toward resolving the systematics of this taxonomically challenging family. Full article

In low-altitude multi-object tracking (MOT), challenges such as frequent inter-object occlusion and complex non-linear motion disrupt the appearance of individual targets and the continuity of their trajectories, leading to frequent tracking failures. We posit that the relatively stable spatio-temporal relationships within object groups (e.g., pedestrians and vehicles) offer powerful contextual cues to resolve such ambiguities. We present NOWA-MOT (Neighbors Know Who We Are), a novel tracking-by-detection framework designed to systematically exploit this principle through a multi-stage association process. We make three primary contributions. First, we introduce a Low-Confidence Occlusion Recovery (LOR) module that dynamically adjusts detection scores by integrating IoU, a novel Recovery IoU (RIoU) metric, and location similarity to surrounding objects, enabling occluded targets to participate in high-priority matching. Second, for initial data association, we propose a Graph Cross-Attention (GCA) mechanism. In this module, separate graphs are constructed for detections and trajectories, and a cross-attention architecture is employed to propagate rich contextual information between them, yielding highly discriminative feature representations for robust matching. Third, to resolve the remaining ambiguities, we design a cascaded Matched Neighbor Guidance (MNG) module, which uniquely leverages the reliably matched pairs from the first stage as contextual anchors. Through MNG, star-shaped topological features are built for unmatched objects relative to their stable neighbors, enabling accurate association even when intrinsic features are weak. Our comprehensive experimental evaluation on the VisDrone2019 and UAVDT datasets confirms the superiority of our approach, achieving state-of-the-art HOTA scores of 51.34% and 62.69%, respectively, and drastically reducing identity switches compared to previous methods. Full article

Background and Objectives: Avian influenza poses a continuous public health threat, particularly to individuals with occupational exposure to poultry such as farm workers, live animal market employees, and processing plant staff. This study aimed to investigate the systemic metabolic effects of such exposure and to identify potential biomarkers for early detection and health risk assessment. Materials and Methods: An untargeted liquid chromatography–mass spectrometry (LC-MS)-based metabolomics approach was applied to analyze serum samples from occupationally exposed individuals and healthy controls. Multivariate statistical analysis, pathway enrichment, and topology analysis were performed to identify significantly altered metabolites and metabolic pathways. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select key metabolites. Results: Multivariate statistical analysis revealed a clear separation between the exposure group and control, suggesting distinct metabolic profiles between the two populations. Pathway analysis indicated significant alterations in alanine, aspartate, and glutamate metabolism, as well as tryptophan metabolism, which are closely linked to immune regulation, energy metabolism, and host–pathogen interactions. LASSO feature selection and subsequent manual verification identified 17 key metabolites with strong discriminative power. Furthermore, lipidomic profiling revealed a pronounced increase in lysophosphatidylcholine (LPC) levels and a concurrent decrease in phosphatidylcholine (PC) species in exposed individuals. Conclusions: This study reveals metabolic disruptions associated with occupational avian influenza exposure and identifies potential serum biomarkers related to immune and lipid metabolism. These findings provide novel insights into host responses to avian influenza exposure and may support early detection and health risk assessment in high-risk occupational populations. Full article

Host phylogeny can imprint the gut microbiota, but it is often masked by diet and environment. Leveraging the standardized husbandry of a zoological collection, we profiled fecal microbiota from 55 captive artiodactyls representing 12 species in Bovidae, Cervidae, and Camelidae using 16S rRNA amplicon sequencing targeting the V3–V4 region on the Illumina MiSeq platform. Community composition differed significantly among host families (Bray–Curtis PERMANOVA, R² = 0.1075, p = 0.001). A host–microbiota tanglegram, which juxtaposes the host phylogeny with a dendrogram of microbiota similarity, recovered a topology congruent with the host phylogeny, with camelids forming a distinct branch separate from true ruminants in both trees. The linear discriminant analysis effect size (LEfSe; LDA ≥ 3.5) identified family-specific biomarkers, including enrichment of Acinetobacter/Moraxellaceae in Bovidae, Rikenellaceae (the Rikenellaceae_RC9_gut_group) in Cervidae, and Rummeliibacillus together with the Christensenellaceae_R-7_group in Camelidae. Functional inference with PICRUSt2 revealed significant differences in KEGG level-2 pathways (e.g., carbohydrate metabolism and xenobiotics biodegradation), consistent with taxonomic shifts. Altogether, these findings show that—even under uniform captive conditions—host evolutionary history remains a primary determinant of both the structure and the predicted functions of the artiodactyl gut microbiota, refining the scope of phylosymbiosis and providing actionable baselines for veterinary monitoring and conservation management in zoo settings. Full article

We present a topologically protected teleportation protocol based on projective parity measurements between spatially separated Majorana zero modes (MZMs), eliminating the need for dynamic braiding. Unlike conventional teleportation schemes, our method preserves logical information through nonlocal encoding and suppresses decoherence exponentially with Majorana separation. We provide a rigorous mathematical framework that includes six theorems and a lemma, proving fidelity bounds, no entropy increase under ideal QND parity measurement under quantum non-demolition (QND) measurements, and compliance with the no-cloning theorem. We demonstrate that all correction operations lie within the Clifford group, enabling efficient, fault-tolerant implementation. Furthermore, we outline a scalable architecture for multi-qubit teleportation and relate our framework to recent experimental advances in quantum-dot-based Kitaev chains and superconducting nanowire platforms. These results position Majorana-based teleportation as a thermodynamically stable and experimentally viable approach to scalable quantum information transfer. All operations discussed are Clifford-only; achieving universality requires non-Clifford resources and lies outside our scope. Full article

We review the connections between condensed matter physics, symmetry, and topology. Physics goes back to at least the time of Galileo, but condensed matter physics, or solid-state physics, is a much newer, emerging only as a separate subject in the 1940s. The subject of symmetry, which is the mathematics of groups and representations, only came to the fore with the advent of quantum mechanics. Early applications to crystalline solids include Bloch’s theorem, the symmetry of electronic and phononic energy bands, and selection rules. Topology, on the other hand, did not exist as a mathematical subject before the twentieth century, but has had a profound influence on physics in general, and on condensed matter physics in particular. The quantum Hall effect is recognized as the first solid-state topological phenomenon and, along with the Berry phase, led to the development of topological materials. This, in turn, led to the topological description of energy bands and to the development of topological quantum chemistry and the energy band representation. Topology has also led to the description of martensitic transformations and the shape memory effect in terms of topological transformations. Apart from a concise statement of martensitic transformations, topology provides a fast-screening method for the discovery of new shape-memory materials. We review these phenomena, providing background material in topology and differential geometry to enable the reader to understand applications to topological materials and to materials physics. Full article

This paper proposes a novel SDA-Mask R-CNN framework for precise seabed terrain edge feature extraction from Side-Scan Sonar (SSS) images to enhance Unmanned Underwater Vehicle (UUV) perception and navigation. The developed architecture addresses critical challenges in underwater image analysis, including low segmentation accuracy and ambiguous edge delineation, through three principal innovations. First, we introduce a Structural Synergistic Group-Attention Residual Network (SSGAR-Net) that integrates group convolution with an enhanced convolutional block attention mechanism, complemented by a layer-skipping architecture for optimized information flow and redundancy verification for computational efficiency. Second, a Depth-Weighted Hierarchical Fusion Network (DWHF-Net) incorporates depthwise separable convolution to minimize computational complexity while preserving model performance, which is particularly effective for high-resolution SSS image processing. This module further employs a weighted pyramid architecture to achieve multi-scale feature fusion, significantly improving adaptability to diverse object scales in dynamic underwater environments. Third, an Adaptive Synergistic Mask Optimization (ASMO) strategy systematically enhances mask generation through classification head refinement, adaptive post-processing, and progressive training protocols. Comprehensive experiments demonstrate that our method achieves 0.695 (IoU) segmentation accuracy and 1.0 (AP) edge localization accuracy. The proposed framework shows notable superiority in preserving topological consistency of seabed features, offering a reliable technical framework for underwater navigation and seabed mapping in marine engineering applications. Full article

It is common knowledge that any topological group that satisfies the lowest separation axiom, $T_0$, is immediately Hausdorff and completely regular; however, this is not the case for normality. This motivates us to introduce the concept of pseudo-normal groups along with pseudo-Tychonoff topological groups as generalizations of the normality and Tychonoffness of topological groups, respectively. We show that every pseudo-normal (resp. pseudo-Tychonoff) topological group is normal (resp. Tychonoff). Generally, the reverse implication of the latter does not hold. Then, we discuss their main properties in detail. To clarify these properties, we provide some examples. Finally, we establish some other results. Full article

Intentional controlled islanding (ICI) is a crucial strategy to avert power system collapse and blackouts caused by severe disturbances. This paper introduces an innovative IoT-based ICI strategy that identifies the optimal location for system segmentation during emergencies. Initially, the algorithm transmits essential data from phasor measurement units (PMUs) to the IoT cloud. Subsequently, it calculates the coherency index among all pairs of generators. Leveraging IoT technology increases system accessibility, enabling the real-time detection of changes in network topology post-disturbance and allowing the coherency index to adapt accordingly. A novel algorithm is then employed to group coherent generators based on relative coherency index values, eliminating the need to transfer data points elsewhere. The “where to island” subproblem is formulated as a mixed integer linear programming (MILP) model that aims to boost system transient stability by minimizing power flow interruptions in disconnected lines. The model incorporates constraints on generators’ coherency, island connectivity, and node exclusivity. The subsequent layer determines the optimal generation/load actions for each island to prevent system collapse post-separation. Signals from the IoT cloud are relayed to the circuit breakers at the terminals of the optimal cut-set to establish stable isolated islands. Additionally, controllable loads and generation controllers receive signals from the cloud to execute load and/or generation adjustments. The proposed system’s performance is assessed on the IEEE 39-bus system through time-domain simulations on DIgSILENT PowerFactory connected to the ThingSpeak cloud platform. The simulation results demonstrate the effectiveness of the proposed ICI strategy in boosting power system stability. Full article

Phlebotomus sichuanensis, considered a potential vector for visceral leishmaniasis (VL), is distributed in the southern Gansu and northern Sichuan regions in China. However, the high similarity in the morphology of P. sichuanensis and P. chinensis s.s. poses unresolved taxonomic challenges. In this study, phlebotomine sand flies were collected from three locations in the southern Gansu and northern Sichuan regions (SCB group) and three locations that are the dominant distribution areas of P. chinensis s.s. (ZHB group). Their whole mitochondrial genomes were sequenced and analyzed. The differential analysis revealed that there were 339 fixed differential sites in the mitochondrial genome-coding region of P. chinensis s.s. and P. sichuanensis, among which the COI gene had the most differential sites (57), followed by ND5 (46), ND4 (38), and CYTB (37), while ATP8 had the least differential sites (4). The molecular genetic p-distance was calculated based on 13 protein-coding regions, and the genetic distance ranged from 0.001 to 0.018 in the ZHB group and from 0.001 to 0.006 in the SCB group, while the interspecies molecular genetic distance was 0.464–0.466 between the two groups. A phylogenetic maximum likelihood tree was constructed from 16 samples via tandem sequence of 13 protein-coding regions, and the topology showed that the ZHB and SCB groups formed separate clusters. A real-time PCR method was established based on the differences in the COI fragment, which can identify P. sichuanensis from P. chinensis s.s. effectively. This study presents objective evidence of the genetic differentiation between P. sichuanensis and P. chinensis s.s., and provides a method for identifying these two morphologically highly similar VL-transmitting sandflies. Full article

Current methods for studying the effects of climate change on plants and pollinators can be grouped into two main categories. The first category involves using species distribution models (SDMs) to generate habitat suitability maps, followed by applying climate change scenarios to predict the future distribution of plants and pollinators separately. The second category involves constructing interaction matrices between plants and pollinators and then either randomly removing species or selectively removing generalist or specialist species, as a way to estimate how climate change might affect the plant–pollinator network. The primary limitation of the first approach is that it examines plant and pollinator distributions separately, without considering their interactions within the context of a pollination network. The main weakness of the second approach is that it does not accurately predict climate change impacts, as it arbitrarily selects species to remove without knowing which species will truly shift, decline, or increase in distribution due to climate change. Therefore, a new approach is needed to bridge the gap between these two methods while avoiding their specific limitations. In this context, we introduced an innovative approach that first requires the creation of binary climate suitability maps for plants and pollinators, based on SDMs, for both the current and future periods. This step aligns with the first category of methods mentioned earlier. To assess the effects of climate change within a network framework, we consider species co-overlapping in a geographic matrix. For this purpose, we developed a Python program that overlays the binary distribution maps of plants and pollinators, generating interaction matrices. These matrices represent potential plant–pollinator interactions, with a ‘0’ indicating no overlap and a ‘1’ where both species coincide in the same cell. As a result, for each cell within the study area, we can construct interaction matrices for both the present and future periods. This means that for each cell, we can analyze at least two pollination networks based on species co-overlap. By comparing the topology of these matrices over time, we can infer how climate change might affect plant–pollinator interactions at a fine spatial scale. We applied our methodology to Chile as a case study, generating climate suitability maps for 187 plant species and 171 pollinator species, resulting in 2906 pollination networks. We then evaluated how climate change could affect the network topology across Chile on a cell-by-cell basis. Our findings indicated that the primary effect of climate change on pollination networks is likely to manifest more significantly through network extinctions, rather than major changes in network topology. Full article

The Anostostomatidae of Aotearoa New Zealand are well-characterized at the genus and species level, but the higher-level systematics of the family as a whole remain poorly resolved. We tested the hypothesis that the New Zealand anaostostomatid fauna consists of a single monophyletic group consistent with a single common ancestor. For phylogenetic analysis, we sampled the genera in Aotearoa New Zealand as well as representatives of the family from Australia and New Caledonia. Maximum likelihood analyses including topological comparison statistics with a DNA alignment of thirteen mitochondrial and four nuclear protein coding genes rejected the monophyly of lineages in New Zealand. We found phylogenetic support for four separate New Zealand lineages; three with their closest relatives in Australia and one in New Caledonia. The New Zealand genus Hemiandrus is paraphyletic and the establishment of a morphologically distinct genus is justified. We determined that six of the valid species previously placed in Hemiandrus form a distinct clade that we designated here as Anderus gen. nov. The putative Hemiandrus that we sampled from Australia was sister to neither of the New Zealand lineages. Full article