Network Clustering Research Articles

Resilience of finite clusters of carbon flux network under localized attack.

The investigation into the resilience of the carbon flux network regarding its capability to sustain the normal flow and transformation of carbon under extreme climatic events, pollutant emissions, biological invasions, and other factors, and the stability of connections between its nodes, has not yet been deeply studied. In this study, we developed carbon flux network models for various regional lands using complex networks, percolation theory, and introducing time delay effects using carbon flux daily data from 2000 to 2019 for three regions: China, the mainland United States, and Europe, to measure the resilience of finite clusters with sizes greater than or equal to s of the carbon flux network under localized attack. The analysis revealed that the carbon flux networks in different regions are characterized by a degree distribution consistent with the Poisson distribution. The carbon flux network demonstrated continuous phase transition behavior under localized attack. Interestingly, numerical simulation revealed a consistent relationship between the carbon flux network and the theoretical Erdős-Rényi network model. Moreover, the carbon flux network becomes more vulnerable as s increases. In addition, we discovered that there is a general scaling relationship of critical exponent δ≈-2 between the fraction of finite clusters and s. Therefore, investigating the resilience of carbon flux networks can enable us to predict and respond to the various risks and challenges, which will help policy designers formulate appropriate response strategies and enhance carbon flux systems' stability and resilience.

Perspectives of Perceived Organizational Support (POS) Theory Towards Individual Happiness

Introduction: Organizations must take a proactive stance when it comes to their workers' welfare and well-being to demonstrate their best commitment in this regard. Objective: This paper presents the descriptive data of organizational factors that contribute to staff happiness in one of the Malaysian Technical University Network (MTUN) clusters in Malaysia. Methods: This study was conducted at one of the Malaysian Technical University Network (MTUN) clusters in Malaysia. It involved 20 respondents comprising academicians who had served more than 11 years. The session at the Round Table Discussion was organized to discuss the issues that were highlighted in the survey data. Results and Conclusion: Analysis showed that the personal economy and personal appreciation dimension were among the highest contributors in determining employee happiness, followed by a support system, workstation environment, and trust. Furthermore, the discussion is related to the general aspect of Perceived Organizational Support Theory which supports the data analysis on individual happiness aspect in the institution of higher education. Originality/Value: By emphasizing the role of factors such as support systems, workstation environment, and trust in determining employee happiness, the study provides valuable insights into the broader dynamics of the workplace. Understanding how these factors influence employee satisfaction can inform strategies for improving organizational culture and employee engagement. Hence, this finding is related to Sustainable Development Goal (SDG) 3: Good Health and Well-being because it focuses on individual happiness, which is closely tied to mental well-being and overall health, both of which are central to SDG 3.

Hepatitis C virus subtype diversity and transmission clusters characteristics among drug users in Zhuhai, South China

BackgroundHepatitis C virus (HCV) infection poses a major public health challenge globally, especially among injecting drug users. China has the world’s largest burden of HCV infections. However, little is known about the characteristics of transmission networks among drug user populations. This study aims to investigate the molecular epidemiology and transmission characteristics of HCV infections among drug users in Zhuhai, a bustling port city connecting Mainland China and its Special Administrative Regions.MethodsParticipants enrolled in this study were drug users incarcerated at Zhuhai’s drug rehabilitation center in 2015. Their sociodemographic and behavioral information, including gender, promiscuity, drug use method, and so forth, was collected using a standardized questionnaire. Plasmas separated from venous blood were analyzed for HCV infection through ELISA and RT-PCR methods to detect anti-HCV antibodies and HCV RNA. The 5’UTR fragment of the HCV genome was amplified and further sequenced for subtype identifications and phylogenetic analysis. The phylogenetic tree was inferred using the Maximum Likelihood method based on the Tamura-Nei model, and the transmission cluster network was constructed using Cytoscape3.8.0 software with a threshold of 0.015. Binary logistic regression models were employed to assess the factors associated with HCV infection.ResultsThe overall prevalence of HCV infection among drug users was 44.37%, with approximately 19.69% appearing to clear the HCV virus successfully. Binary logistic regression analysis revealed that those aged over 40, engaging in injecting drug use, and being native residents were at heightened risk for HCV infection among drug user cohorts. The predominant HCV subtypes circulating among those drug users were 6a (60.26%), followed by 3b (16.7%), 3a (12.8%), 1b (6.41%) and 1a (3.85%), respectively. Molecular transmission network analysis unveiled the presence of six transmission clusters, with the largest propagation cluster consisting of 41 individuals infected with HCV subtype 6a. Furthermore, distinct transmission clusters involved eight individuals infected with subtype 3b and seven with subtype 3a were also observed.ConclusionThe genetic transmission networks revealed a complex transmission pattern among drug users in Zhuhai, emphasizing the imperative for a targeted and effective intervention strategy to mitigate HCV dissemination. These insights are pivotal for shaping future national policies on HCV screening, treatment, and prevention in port cities.

Image Forgery Detection Using Machine Learning

Digital Image Forgery can be done by deceiving the digital image to mask some meaningful or important data of the image. It is usually difficult to spot out the manipulated region of the original image. To sustain the uprightness and legitimacy of the image, the detection of forgery in the image is mandating. Acclimation of the modern way of life and advancement in photography gadgetry has made exploitation of digital image easy with the help of image editing software. Therefore, it is crucial to detect such image forgery operations in the images. The image forgery detection can be done based on object removal, object addition, unusual size modifications in the image. Images are one of the powerful media for communication. In this project we have used algorithms such as Copy move, Canny Edge Defection, Structure Similarity index, Hierarchical Agglomerate Clustering and Neural Network algorithms with improved accuracy rate.

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning.

Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC's full potential.

ENO1 promotes trophoblast invasion regulated by E2F8 in recurrent miscarriage.

Recurrent miscarriage (RM) is related to the dysfunction of extravillous trophoblast cells (EVTs), but the comprehensive mechanisms remain largely unexplored. We analyzed single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing and microarray datasets obtained from Gene Expression Omnibus (GEO) database to explore the hub genes in the mechanisms of RM. We identified 1724 differentially expressed genes (DEGs) in EVTs from the RM, and they were all expressed along the trajectory of EVTs. These DEGs were associated with hypoxia and glucose metabolism. Single-cell Regulatory Network Inference and Clustering (SCENIC) analysis revealed that E2F transcription factor (E2F) 8 (E2F8) was a key transcription factor for these DEGs. And the expression of ENO1 can be positively regulated by E2F8 via RNA sequencing analysis. Subsequently, we performed immunofluorescence assay (IF), plasmid transfection, western blotting, chromatin immunoprecipitation (ChIP), real-time quantitative polymerase chain reaction (qRT-PCR), and transwell assays for validation experiments. We found that the expression of alpha-Enolase 1 (ENO1) was lower in the placentas of RM. Importantly, E2F8 can transcriptionally regulate the expression of ENO1 to promote the invasion of trophoblast cells by inhibiting secreted frizzled-related protein 1/4 (SFRP1/4) to activate Wnt signaling pathway. Our results suggest that ENO1 can promote trophoblast invasion via an E2F8-dependent manner, highlighting a potential novel target for the physiological mechanisms of RM.

Uncovering the shared neuro-immune-related regulatory mechanisms between spinal cord injury and osteoarthritis

Adults with spinal cord injury (SCI), a destructive neurological injury, have a significantly higher incidence of osteoarthritis (OA), a highly prevalent chronic joint disorder. This study aimed to dissect the neuroimmune-related regulatory mechanisms of SCI and OA using bioinformatics analysis. Using microarray data from the Gene Expression Omnibus database, differentially expressed genes (DEGs) were screened between SCI and sham samples and between OA and control samples. Common DEGs were used to construct a protein-protein interaction (PPI) network. Weighted gene co-expression network analysis (WGCNA) was used to mine SCI- and OA-related modules. Shared miRNAs were identified, and target genes were predicted using the Human MicroRNA Disease Database (HMDD) database. A miRNA-gene-pathway regulatory network was constructed with overlapping genes, miRNAs, and significantly enriched pathways. Finally, the expression of the identified genes and miRNAs was verified using RT-qPCR. In both the SCI and OA groups, 185 common DEGs were identified, and three hub clusters were obtained from the PPI network. WGCNA revealed three SCI-related modules and two OA-related modules. There were 43 overlapping genes between the PPI network clusters and the WGCNA network modules. Seventeen miRNAs shared between patients with SCI and OA were identified. A regulatory network consisting of five genes, six miRNAs, and six signaling pathways was constructed. Upregulation of CD44, TGFBR1, CCR5, and IGF1, while lower levels of miR-125b-5p, miR-130a-3p, miR-16-5p, miR-204-5p, and miR-204-3p in both SCI and OA were successfully verified using RT-qPCR. Our study suggests that a miRNA-gene-pathway network is implicated in the neuroimmune-related regulatory mechanisms of SCI and OA. CD44, TGFBR1, CCR5, and IGF1, and their related miRNAs (miR-125b-5p, miR-130a-3p, miR-16-5p, miR-204-5p, and miR-204-3p) may serve as promising biomarkers and candidate therapeutic targets for SCI and OA.

Spatial Analysis of Intercity Migration Patterns of China’s Rural Population: Based on the Network Perspective

Since entering the 21st century, many developing countries around the world have begun to enter a stage of rapid urbanization; large-scale “rural-urban” population migration has become a typical social phenomenon in these countries. Against this backdrop, this paper aims to elucidate the spatial migration characteristics of rural populations and to discuss future rural–urban development strategies. For this purpose, this paper takes China as a case and employs methods such as spatial autocorrelation analysis, hotspot analysis, and network analysis to construct an intercity migration network of rural migrants and analyze its spatial characteristics and internal structure. The results indicate that the migration pattern of the rural population exhibits notable spatial clustering features. Cities in the eastern and central regions are, respectively, hotspots for the inflow and outflow of rural populations, with internal migration dominating in western cities and relatively inactive rural population movements in northeastern cities. Municipalities directly under the central government, sub-provincial cities, and provincial capitals show a significant tendency to attract rural populations, while prefecture-level and county-level cities mainly radiate rural populations outward. Cities nationwide form seven major clusters in the migration network, and these clusters exhibit distinct structural characteristics. Rural population migration is influenced by various factors. In the future, considerations should focus on the county as the primary unit, attracting rural populations for local employment, and promoting rural revitalization and agriculture development. The findings of this paper are of reference significance not only to China but also to many developing countries with similar national conditions in the world.

Integrated Heterogeneous Graph Attention Network for Incomplete Multi-modal Clustering

Integrated Heterogeneous Graph Attention Network for Incomplete Multi-modal Clustering

Research on Complex Network Models and Characteristics of Building Material Enterprises Based on Competitive Relationships

Based on complex network theory, this paper establishes a Boolean competitive relationship network model and a weighted competitive relationship network model for building material enterprises based on big data and performs empirical analyses with construction prefabricated component (PC) enterprises in the Beijing–Tianjin–Hebei region as research samples. Results show the competitive relationship network of PC enterprises in the Beijing–Tianjin–Hebei region is characterised by the development of a small-world network, and its cumulative degree and out-degree (in-degree) intensity distributions are single-scale networks with fast-decaying tails. The network has strong network clustering and assortativity, and it can truly reflect the competitive status and dynamics of the enterprises.

Unsupervised structural damage identification based on covariance matrix and deep clustering

SummaryStructural damage identification is a major task in structural health monitoring. Machine learning and deep learning algorithms have been widely applied in the research of structural damage identification. Supervised algorithms require expert labeling, making it difficult to implement in engineering applications. Unsupervised structural damage identification algorithms are generally divided into two parts: damage‐sensitive factor extraction and damage determination. Existing algorithms all perform these two steps separately. This paper proposes a damage identification method combining covariance matrix and improved deep embedding clustering network (IDEC). IDEC can perform damage‐sensitive factor extraction and damage determination operations at the same time. The covariance matrix that introduces delay information contains rich damage features, and the combination of the two has been proven to effectively mine the damage‐sensitive feature space. After network hyperparameter optimization via Bayesian optimization, the proposed method is applied to the damage identification and quantification using real bridge acceleration response data under vehicle load. The results show that this method can identify structural damage with an accuracy of up to 97% with better performance than existing technologies, and it also has great performance in identifying small damages. The proposed method is expected to increase the damage identification accuracy if applied in engineering practice.

Long-term vegetation restoration promotes lignin phenol preservation and microbial anabolism in forest plantations: Implications for soil organic carbon dynamics

Vegetation restoration contributes to soil organic carbon (C; SOC) sequestration through the accumulation of plant and microbial residues, but the mechanisms underlying this microbially mediated process are not well resolved. To depict the dynamics of plant- and microbial-derived C in restored forest ecosystems, soil samples were collected from Robinia pseudoacacia plantations of different stand ages (15, 25, 35, 45 years old) established on degraded wheat fields. The results showed that the degree of lignin phenol oxidation decreased with increasing stand age (P < 0.05), and hemicellulose-degrading genes were detected at higher relative abundances than other functional gene categories, indicating selective preservation of recalcitrant lignin phenols. Despite both glucosamine (R2 = 0.61, P < 0.001) and muramic acid (R2 = 0.37, P < 0.001) contents trending upward over time, fungal residual C accounted for a greater proportion of SOC compared with bacterial residual C. Accordingly, fungal residual C, which exhibited a similar response pattern as total microbial residual C to vegetation restoration, was considered a major contributor to the SOC pool. These results provided evidence that long-term vegetation restoration enhanced SOC sequestration in R. pseudoacacia forest by promoting the preservation of plant-derived lignin phenols and concomitant microbial anabolism. Partial least squares-discriminant analysis identified two important ecological clusters (i.e., modules) in the fungal network that profoundly influenced lignin phenol oxidation (P < 0.05) and microbial residual C accumulation (P < 0.01). Among the dominant taxa in microbial networks, the bacterial phyla Proteobacteria and Acidobacteriota had potential to degrade recalcitrant C compounds (e.g., cellulose, lignin), whereas the fungal phylum Ascomycota could outcompete for labile C fractions (e.g., dissolved organic C). Findings of this study can enable a mechanistic understanding of SOC stability driven by microbial turnover in restored forest ecosystems.

Early detection of melanoma skin cancer: A hybrid approach using fuzzy C-means clustering and differential evolution-based convolutional neural network

Skin cancer is a prevalent type of disease that is challenging to predict, and early detection is crucial for successful treatment. In this study, we propose an improved strategy for early detection of three types of skin cancers using medical imaging. Our approach uses fuzzy C-means clustering for image segmentation, along with various filters and image features including Local Binary Pattern (LBP), RGB color-space, and GLCM methods. We also employ a Convolutional neural network (CNN) trained with differential evolution (DE) algorithm for classification. We evaluate the proposed technique using skin cancer image datasets HAM10000, and demonstrate its superior performance compared to traditional classifiers. Our approach achieves a detection accuracy of 91 %, which is significantly higher than other traditional methods in the same domain. To enhance the accuracy of skin cancer detection in medical imaging, the proposed technique can be automated using electronic devices like mobile phones.

Bacterial interactome disturbance in chronic obstructive pulmonary disease clinical stability and exacerbations

RationaleOur understanding of airway dysbiosis in chronic obstructive pulmonary disease (COPD) remains incomplete, which may be improved by unraveling the complexity in microbial interactome.ObjectivesTo characterize reproducible features of airway bacterial interactome in COPD at clinical stability and during exacerbation, and evaluate their associations with disease phenotypes.MethodsWe performed weighted ensemble-based co-occurrence network analysis of 1742 sputum microbiomes from published and new microbiome datasets, comprising two case-control studies of stable COPD versus healthy control, two studies of COPD stability versus exacerbation, and one study with exacerbation-recovery time series data.ResultsPatients with COPD had reproducibly lower degree of negative bacterial interactions, i.e. total number of negative interactions as a proportion of total interactions, in their airway microbiome compared with healthy controls. Evaluation of the Haemophilus interactome showed that the antagonistic interaction networks of this established pathogen rather than its abundance consistently changed in COPD. Interactome dynamic analysis revealed reproducibly reduced antagonistic interactions but not diversity loss during COPD exacerbation, which recovered after treatment. In phenotypic analysis, unsupervised network clustering showed that loss of antagonistic interactions was associated with worse clinical symptoms (dyspnea), poorer lung function, exaggerated neutrophilic inflammation, and higher exacerbation risk. Furthermore, the frequent exacerbators (≥ 2 exacerbations per year) had significantly reduced antagonistic bacterial interactions while exhibiting subtle compositional changes in their airway microbiota.ConclusionsBacterial interactome disturbance characterized by reduced antagonistic interactions, rather than change in pathogen abundance or diversity, is a reproducible feature of airway dysbiosis in COPD clinical stability and exacerbations, which suggests that we may target interactome rather than pathogen alone for disease treatment.

Energy-Recognition Clustering Technique Based on Reinforcement Learning In WSN

WSN (Wireless Sensor Network) technology has recently gained a lot of attention. This began with the deployment of small WSNs and moved to the deployment of big and IoT WSNs, all with an emphasis on energy conservation. Wireless sensor networks can benefit from network clustering to increase their energy efficiency. The practise of dividing nodes into clusters before picking multiple cluster heads is known as network clustering (CHs). Clustering in wireless sensor networks is known to save energy and extend the network's lifetime (WSNs). Energy efficiency is a hot topic in existing wireless sensor networks, although it's not generally discussed. In this research, we offer a reinforcement learning (RL) based energy-aware clustering approach, whereby peripheral cluster nodes monitor environmental factors like energy use and choose an optimal cluster leader (CH). Connect the CH (BS) to the base station. In the simulation (PDR), performance factors such as network lifetime, energy tax, network stability period, and packet delivery rate are all taken into account. The simulation results show that the proposed QL-ReLeC performs around 11% better than the reference protocol in terms of PDR and 11% better in terms of energy tax.

Supercomputers and quantum computing on the axis of cyber security

Cybersecurity has become a very critical area to address for governments, industry and the academic community. Cyber attacks are on the rise so is research to address the challenges presented by these attacks. Research yields several technological advancements. This paper explores the development of quantum computing and supercomputers within the context of cybersecurity. As many governments and organizations are under the threat of cyber-attacks, it is critical and timely to explore the status of technological development. We use advanced scientometric techniques to disclose the development status and identify the centers of excellence. The research uses bibliometric data of published papers and utilizes an integration of scientometric methods including social network analysis, entropy analysis, cluster analysis, overlay maps and knowledge diffusion analysis as well as keyword and source analyses.

New treatment alternatives for primary and metastatic colorectal cancer by an integrated transcriptome and network analyses

Metastatic colorectal cancer (CRC) is still in need of effective treatments. This study applies a holistic approach to propose new targets for treatment of primary and liver metastatic CRC and investigates their therapeutic potential in-vitro. An integrative analysis of primary and metastatic CRC samples was implemented for alternative target and treatment proposals. Integrated microarray samples were grouped based on a co-expression network analysis. Significant gene modules correlated with primary CRC and metastatic phenotypes were identified. Network clustering and pathway enrichments were applied to gene modules to prioritize potential targets, which were shortlisted by independent validation. Finally, drug-target interaction search led to three agents for primary and liver metastatic CRC phenotypes. Hesperadin and BAY-1217389 suppress colony formation over a 14-day period, with Hesperadin showing additional efficacy in reducing cell viability within 48 h. As both candidates target the G2/M phase proteins NEK2 or TTK, we confirmed their anti-proliferative properties by Ki-67 staining. Hesperadinin particular arrested the cell cycle at the G2/M phase. IL-29A treatment reduced migration and invasion capacities of TGF-β induced metastatic cell lines. In addition, this anti-metastatic treatment attenuated TGF-β dependent mesenchymal transition. Network analysis suggests IL-29A induces the JAK/STAT pathway in a preventive manner.

Network and attribute-based clustering of tennis players and tournaments

AbstractThis paper aims at targeting some relevant issues for clustering tennis players and tournaments: (i) it considers players, tournaments and the relation between them; (ii) the relation is taken into account in the fuzzy clustering model based on the Partitioning Around Medoids (PAM) algorithm through spatial constraints; (iii) the attributes of the players and of the tournaments are of different nature, qualitative and quantitative. The proposal is novel for the methodology used, a spatial Fuzzy clustering model for players and for tournaments (based on related attributes), where the spatial penalty term in each clustering model depends on the relation between players and tournaments described in the adjacency matrix. The proposed model is compared with a bipartite players-tournament complex network model (the Degree-Corrected Stochastic Blockmodel) that considers only the relation between players and tournaments, described in the adjacency matrix, to obtain communities on each side of the bipartite network. An application on data taken from the ATP official website with regards to the draws of the tournaments, and from the sport statistics website Wheelo ratings for the performance data of players and tournaments, shows the performances of the proposed clustering model.

Kalman filtering and sine arithmetic optimization algorithm (KSAOA) for wireless sensor network clustering

Hierarchical routing is a crucial approach for enhancing the energy efficiency of Wireless Sensor Networks (WSN). The selection of sensor clusters poses a Non-deterministic Polynomial (NP) problem, typically addressed using meta-heuristic algorithms. However, existing research has not adequately considered the observation error of node energy and the limited convergence performance of their meta-heuristic algorithms. To address these challenges, we proposes a novel approach called KSAOA (Kalman filtering and Sine Arithmetic Optimization Algorithm) for WSN clustering, aimed at prolonging the service lifetime and enhancing the network balance of WSN. The proposed model combines Kalman filtering for estimating node energy and the Sine Arithmetic Optimization Algorithm (SAOA) for clustering nodes. SAOA builds upon the Arithmetic Optimization algorithm (AOA) by introducing a sine factor to extend the exploration boundary and a levy flight strategy to improve local exploration. Additionally, greedy algorithms are employed in the data transfer phase to enhance operational efficiency. The experimental results demonstrate the effectiveness of the proposed method in tabular and graphical form. The results are analyzed through comparative experiments to show that the proposed strategy is effective in improving the balance of network lifetime and network energy consumption.

Research on station-network planning of electricity-thermal -cooling regional integrated energy system considering multiple-load clusters and network costs

A reasonable regional integrated energy system (RIES) structure is the key to achieving its economic and efficient operation. However, in actual engineering construction, only the maximum load is considered as a prerequisite for energy design, which leads to waste of energy allocation. In view of this, this article proposes an electricity-thermal-cooling RIES energy stations and networks planning model that coordinates the operation of multiple-user loads, energy networks, and energy stations based on the idea of load clustering. Firstly, an average peak valley difference rate covering electricity, thermal, and cooling loads was proposed to evaluate the load characteristics of the RIES. Secondly, based on the load cluster characteristics and network costs of RIES, a RIES planning optimization framework is proposed, which includes load cluster division, RIES site selection and network planning, and RIES capacity planning. Thirdly, based on the P-median model and load cluster characteristics, establish an RIES location and network layout model. Fourthly, establish an RIES capacity planning model with the goal of annual total cost and carbon emissions. Finally, Dijkstra, improved particle swarm optimization, and VIKOR were used to solve the model. The results showed that the proposed model reduced the annual total cost by 7.13 % and the annual carbon emissions by 3.72 %.