Search Results (23)

Amidst the swift progression of urban expansion, transformations in land utilization have become increasingly pronounced, posing significant threats to ecosystem coherence and continuity. Establishing a well-designed ecological security pattern (ESP) framework proves essential for preserving environmental equilibrium and enhancing species diversity. This investigation centers on the Fuzhou urban agglomeration as its primary study zone, employing the patch-oriented land utilization simulation (PLUS) approach to forecast 2030 land cover modifications under environmentally conscious conditions. By integrating morphological spatial configuration assessment (MSPA) with habitat linkage evaluation, critical ecological hubs were pinpointed. Subsequent application of electrical circuit principles alongside the minimal cumulative resistance (MCR) methodology enabled the identification of vital ecological pathways and junctions, culminating in the development of a comprehensive territorial ESP framework. Key findings reveal the subsequent outcomes: (1) the main land use type in the Fuzhou metropolitan area is woodland, which accounts for over 80% of its area, and under the ecological priority scenario for 2030, woodland fragmentation was significantly improved; (2) ecological sources are mainly distributed in the northwest, northeast, and central regions, with their total area proportion increasing to 40.49% by 2030; (3) we constructed 35 ecological corridors and 42 ecological nodes, including 14 key ecological pinch points, 9 potential ecological pinch points, and 4 ecological barrier points; and (4) the final ESP formed the pattern of “three cores, three areas, multiple corridors, and multiple sources,” providing strong support for ecological protection and regional sustainable development in the Fuzhou metropolitan area. In this research, we explore the coupled methods of land use simulation and ecological network construction, offering insights for optimizing ESPs in other rapidly urbanizing areas. Full article

Hubs are strategic locations that function as central nodes within clusters of cities, playing a pivotal role in the distribution of goods, services, and connectivity. Identifying these vital hubs—through analyzing influential locations within transportation networks—is essential for effective urban planning, logistics optimization, and enhancing infrastructure resilience. This task becomes even more crucial in developing and less-developed countries, where such hubs can significantly accelerate urban growth and drive economic development. However, existing hub identification approaches face notable limitations. Traditional centrality measures often yield low variance in node scores, making it difficult to distinguish truly influential nodes. Moreover, these methods typically rely solely on either local metrics or global network structures, limiting their effectiveness. To address these challenges, we propose a novel method called Hybrid Community-based Gravity Centrality (HCGC), which integrates local influence measures, community detection, and gravity-based modeling to more effectively identify influential nodes in complex networks. Through extensive experiments, we demonstrate that HCGC consistently outperforms existing methods in terms of spreading ability across varying truncation radii. To further validate our approach, we introduce ThaiNet, a newly constructed real-world transportation network dataset. The results show that HCGC not only preserves the strengths of traditional local approaches but also captures broader structural patterns, making it a powerful and practical tool for real-world network analysis. Full article

In complex networks, the identification of critical nodes is vital for optimizing information dissemination. Given the significant role of these nodes in network structures, researchers have proposed various identification methods. In recent years, deep learning has emerged as a promising approach for identifying key nodes in networks. However, existing algorithms fail to effectively integrate local and global structural information, leading to incomplete and limited network understanding. To overcome this limitation, we introduce a transformer framework with multi-scale feature fusion (MSF-Former). In this framework, we construct local and global feature maps for nodes and use them as input. Through the transformer module, node information is effectively aggregated, thereby improving the model’s ability to recognize key nodes. We perform evaluations using six real-world and three synthetic network datasets, comparing our method against multiple baselines using the SIR model to validate its effectiveness. Experimental analysis confirms that MSF-Former achieves consistently high accuracy in the identification of influential nodes across real-world and synthetic networks. Full article

Identifying key nodes in networks is a fundamental problem in network science. This study proposes a quantum deep reinforcement learning (QDRL) framework that integrates reinforcement learning with a variational quantum graph neural network, effectively identifying distributed influential nodes while preserving the network’s fundamental topological properties. By leveraging principles of quantum computing, our method is designed to reduce model parameters and computational complexity compared to traditional neural networks. Trained on small networks, it demonstrated strong generalization across diverse scenarios. We compared the proposed algorithm with some classical node ranking and network dismantling algorithms on various synthetical and empirical networks. The results suggest that the proposed algorithm outperforms existing baseline methods. Moreover, in synthetic networks based on Erdős–Rényi and Watts–Strogatz models, QDRL demonstrated its capability to alleviate the issue of localization in network information propagation and node influence ranking. Our research provides insights into addressing fundamental problems in complex networks using quantum machine learning, demonstrating the potential of quantum approaches for network analysis tasks. Full article

Robustness in networks plays a vital role in mitigating the effects of failures caused by nodes or links, which can disrupt essential services. Among the various vulnerability parameters in graph theory, such as connectivity and integrity, their applications to fuzzy graphs remain underexplored, despite fuzzy graphs being a powerful tool for modeling uncertainty. In this paper, we introduce the parameter ’fuzzy node integrity’, which considers both the number of disrupted elements and the strength of residual connections. We derive general formulas for different types of symmetric and asymmetric fuzzy graph structures, including cycle graphs, wheel graphs, and star graphs, to systematically demonstrate the utility of this parameter. The proposed parameter is then applied to a military logistics problem to gain insights into the identification of critical nodes and route optimization under uncertainty. This study bridges an important gap in fuzzy graph theory by redefining node integrity through the inclusion of connection strength, offering a promising tool for assessing network vulnerability. These findings lay the foundation not only for theoretical research but also for practical improvements in transportation, disaster management, and communication networks. Full article

Soybean (Glycine max (L.) Merr.) is a vital agricultural crop and a key source of protein and oil for food and feed production. The search for new genetic factors affecting the main agronomic traits of soybean is a significant step for efficient breeding strategies. This study aimed to identify marker–trait associations (MTAs) for seed protein and oil content and yield by conducting a genome-wide association study (GWAS). The collection of 252 soybean accessions of five different origins was analyzed over a period of five years. The GWAS was conducted using 44,385 SNP markers extracted from whole-genome resequencing data using Illumina HiSeq X Ten. The multiple-locus mixed linear model (MLMM) facilitated the identification of 38 stable MTAs: nine for protein content, nine for oil content, seven for the number of fertile nodes, six for the number of seeds per plant, four for thousand seeds weight, and three for yield per plant. Fifteen of these MTAs are presumed to be novel, with one linked to seed protein content, three linked to seed oil content, and the remaining MTAs linked to yield-related traits. These findings offer valuable insights for soybean breeding programs aimed at developing new, competitive cultivars with improved seed quality and yield characteristics. Full article

The identification of key nodes in complex networks is an important topic in many network science areas. It is vital to a variety of real-world applications, including viral marketing, epidemic spreading and influence maximization. In recent years, machine learning algorithms have proven to outperform the conventional, centrality-based methods in accuracy and consistency, but this approach still requires further refinement. What information about the influencers can be extracted from the network? How can we precisely obtain the labels required for training? Can these models generalize well? In this paper, we answer these questions by presenting an enhanced machine learning-based framework for the influence spread problem. We focus on identifying key nodes for the Independent Cascade model, which is a popular reference method. Our main contribution is an improved process of obtaining the labels required for training by introducing “Smart Bins” and proving their advantage over known methods. Next, we show that our methodology allows ML models to not only predict the influence of a given node, but to also determine other characteristics of the spreading process—which is another novelty to the relevant literature. Finally, we extensively test our framework and its ability to generalize beyond complex networks of different types and sizes, gaining important insight into the properties of these methods. Full article

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial–mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer. Full article

Forests are vital for terrestrial ecosystems, providing crucial functions like carbon sequestration and water conservation. In the Yellow River Basin, where 70% of forest coverage is concentrated in the middle reaches encompassing Sichuan, Shaanxi, and Shanxi provinces, there exists significant potential for coal production, with nine planned coal bases. This study centered on Jincheng City, Shanxi Province, a representative coal mining area in the Yellow River Basin, and combined the MSPA analysis method and MCR model to generate the five-period forest ecological network of Jincheng City from 1985 to 2022 under the background of coal mining and calculate the degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality; the correlation between the four centralities and carbon sequestration ability is further explored. Simultaneously, employing the RAND-ESU algorithm for motif identification within forest ecological networks, this study integrates the ecological policies of the research area with the specific conditions of the coal mining region to optimize the forest ecological network in Jincheng City. Findings reveal the following. (1) Forest ecological spatial networks: Forest ecological networks exhibit robust overall ecological connectivity in the study area, with potential ecological corridors spanning the region. However, certain areas with high ecological resistance hinder connectivity between key forest ecological nodes under the background of coal mining. (2) Correlation between topological indices and carbon sequestration ecological services: From 1985 to 2022, the carbon sequestration capacity of Jincheng City’s forest source areas increased year by year, and significant positive correlations were observed between degree centrality, betweenness centrality, eigenvector centrality with carbon sequestration ecological services, indicating a strengthening trend over time. (3) Motif Recognition and Ecological Network Optimization: During the study, four types of motifs were identified in the forest ecological network of Jincheng City based on the number of nodes and their connections using the RAND-ESU network motif algorithm. These motifs are 3a, 4a, 4b, and 4d (where the number represents the number of nodes and the letter represents the connection type). Among these, motifs 3a and 4b play a crucial role. Based on these motifs and practical considerations, network optimization was performed on the existing ecological source areas to enhance the robustness of the forest ecological network. Full article

Non-intrusive load monitoring (NILM) technology, crucial for intelligent electricity management, has gained considerable attention in residential electricity usage studies. NILM enables monitoring of total electrical current and voltage in homes, offering insights vital for enhancing safety and preventing domestic electrical accidents. Despite its importance, accurately discerning the operational status of appliances using non-intrusive methods remains a challenging area within this field. This paper presents a novel methodology that integrates an advanced clustering algorithm with a Bayesian network for the identification of appliance operational states. The approach involves capturing the electrical current signals during appliance operation via NILM, followed by their decomposition into odd harmonics. An enhanced clustering algorithm is then employed to ascertain the central coordinates of the signal clusters. Building upon this, a three-layer Bayesian network inference model, incorporating leak nodes, is developed. Within this model, harmonic signals are used as conditions for node activation. The operational states of the appliances are subsequently determined through probabilistic reasoning. The proposed method’s effectiveness is validated through a series of simulation experiments conducted in a laboratory environment. The results of these experiments (low mode 89.1%, medium mode 94.4%, high mode 92.0%, and 98.4% for combination) provide strong evidence of the method’s accuracy in inferring the operational status of household electrical appliances based on NILM technology. Full article

Tick-borne diseases (TBDs) of livestock are endemic across various parts of tropical countries. Theileriosis is one such economically important TBD, caused by the Theileriidae family of organisms, which is transmitted by ticks. Theileria annulata, the causative agent of tropical theileriosis, contributes a significant loss to the dairy sector by causing anorexia, high fever, anemia, inflammatory changes in vital organs and icterus, thus, a loss in milk yield. Though vaccines are available, their protective efficacy is not absolute, and treatment is limited to early diagnosis of the causative agent. Routinely, microscopic identification of piroplasms in the erythrocytes (Giemsa-stained) of infected animals or schizonts in lymph node biopsies are practiced for diagnosis. PCR-based techniques (multiplex, uniplex, nested and real-time) have been reported to perform well in diagnosing active infection. Several attempts have been made using serological assays like Dot blot, ELISA and ICT, but the results were of variable sensitivity and specificity. Recombinant proteins like the Theileria annulata merozoite surface antigen (Tams1) and Theileria annulata surface protein (TaSP) have been explored as antigenic candidates for these assays. In the present study, we predicted an immunogenic peptide, i.e., TaSP-34, from the TaSP using various computational tools. The predicted peptide was custom synthesized. The diagnostic potential of the peptide was assessed by indirect plate ELISA to detect the bovine-IgM against Theileria annulata. Alongside, a recombinant truncated TaSP (rTaSP(tr)) was expressed and purified, which was used to compare the performance of the peptide as a diagnostic candidate. The IgM-based peptide ELISA was 100% sensitive and 92.77% specific as compared to PCR (Tams1 targeting), while 98.04% sensitivity and 97.44% specificity were observed in comparison with rTaSP(tr) ELISA. Almost perfect agreement between peptide ELISA and Tams1 PCR was observed with a Cohen’s kappa coefficient (κ-value) of 0.901 and agreement of 95.31%. Further, the κ-value between the peptide ELISA and rTaSP(tr) ELISA was found to be 0.95, and the agreement was 97.65%, which shows a good correlation between the two tests. The findings suggest that the TaSP-34 peptide can be an efficient and new-generation diagnostic candidate for the diagnosis of T. annulata. Furthermore, the peptide can be synthesized commercially at a larger scale and can be a cost-effective alternative for the protein-based diagnostic candidates for T. annulata. Full article

Precision irrigation and fertilization in agriculture are vital for sustainable crop production, relying on accurate determination of the crop’s nutritional status. However, there are challenges in optimizing traditional neural networks to achieve this accurately. This paper aims to propose a rapid identification method for crop water and nitrogen content using optimized neural networks. This method addresses the difficulty in optimizing the traditional backpropagation neural network (BPNN) structure. It uses 179 multi−spectral images of crops (such as maize) as samples for the neural network model. Particle swarm optimization (PSO) is applied to optimize the hidden layer nodes. Additionally, this paper proposes a double−hidden−layer network structure to improve the model’s prediction accuracy. The proposed double−hidden−layer PSO−BPNN model showed a 9.87% improvement in prediction accuracy compared with the traditional BPNN model. The correlation coefficient R² for predicted crop nitrogen and water content was 0.9045 and 0.8734, respectively. The experimental results demonstrate high training efficiency and accuracy. This method lays a strong foundation for developing precision irrigation and fertilization plans for modern agriculture and holds promising prospects. Full article

Hypergraphs have become an accurate and natural expression of high-order coupling relationships in complex systems. However, applying high-order information from networks to vital node identification tasks still poses significant challenges. This paper proposes a von Neumann entropy-based hypergraph vital node identification method (HVC) that integrates high-order information as well as its optimized version (semi-SAVC). HVC is based on the high-order line graph structure of hypergraphs and measures changes in network complexity using von Neumann entropy. It integrates $s$-line graph information to quantify node importance in the hypergraph by mapping hyperedges to nodes. In contrast, semi-SAVC uses a quadratic approximation of von Neumann entropy to measure network complexity and considers only half of the maximum order of the hypergraph’s $s$-line graph to balance accuracy and efficiency. Compared to the baseline methods of hyperdegree centrality, closeness centrality, vector centrality, and sub-hypergraph centrality, the new methods demonstrated superior identification of vital nodes that promote the maximum influence and maintain network connectivity in empirical hypergraph data, considering the influence and robustness factors. The correlation and monotonicity of the identification results were quantitatively analyzed and comprehensive experimental results demonstrate the superiority of the new methods. At the same time, a key non-trivial phenomenon was discovered: influence does not increase linearly as the $s$-line graph orders increase. We call this the saturation effect of high-order line graph information in hypergraph node identification. When the order reaches its saturation value, the addition of high-order information often acts as noise and affects propagation. Full article

In the era of the popularization of the Internet of Things (IOT), analyzing people’s daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to analyze the relationship between people’s daily behaviors, while the mainstream first-order network (FON) method ignores the high-order dependencies between daily behaviors. A higher-order dependency network (HON) can more accurately mine the requirements by considering higher-order dependencies. Firstly, our work adopts indoor daily behavior sequences obtained by video behavior detection, extracts higher-order dependency rules from behavior sequences, and rewires an HON. Secondly, an HON is used for the RandomWalk algorithm. On this basis, research on vital node identification and community detection is carried out. Finally, results on behavioral datasets show that, compared with FONs, HONs can significantly improve the accuracy of random walk, improve the identification of vital nodes, and we find that a node can belong to multiple communities. Our work improves the performance of user behavior analysis and thus benefits the mining of user requirements, which can be used to personalized recommendations and product improvements, and eventually achieve higher commercial profits. Full article

Endoscopic submucosal dissection (ESD) has been widely used to treat superficial esophageal cancer. The advantages of esophageal ESD include a high en bloc resection rate and accurate pathological diagnosis. It enables local resection of the primary tumor and accurate identification of the risk factors for lymph node metastasis, including depth, vascular invasion, and types of invasion. Even in cases with clinical T1b-SM cancer, ESD and additional treatment can achieve radical cure, depending on the risk of lymph node metastasis. Esophageal ESD will be increasingly vital in minimally invasive and effective esophageal cancer treatment. This article describes the current status and prospects of esophageal ESD. Full article