Complex Network Research Articles

A clustered-LPSEIRS malware propagation model in complex networks

In this paper, we present a new malware propagation model that integrates epidemic spread, clustering, and link prediction techniques, tailored for complex network networks. Our model is based on the clustered-link prediction-susceptible-exposed-infected-recovered (clustered-LPSEIRS) epidemic model, which simulates malware dissemination within the network. Our findings reveal a significant decrease in the rate of malware spread compared to the traditional SEIR model, with this enhancement in containment attributed to the integration of clustering and link prediction methods. We also compute the basic reproduction ratio (R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_{0}$$\\end{document}) for our model, providing insights into the potential ramifications of malware within the network. By examining parameter variations, we enhance our understanding of the model's behavior under diverse scenarios. Additionally, we assess the influence of clustering and link prediction on mitigating malware spread, emphasizing its effectiveness in diminishing the overall impact.

Hierarchical architecture and security of Industrial Internet: A new perspective from complex network

Hierarchical architecture and security of Industrial Internet: A new perspective from complex network

XAIEnsembleTL-IoV: A New eXplainable Artificial Intelligence Ensemble Transfer Learning for Zero-Day Botnet Attack Detection in the Internet of Vehicles

The Internet of Vehicles (IoV) is a network of interconnected vehicles that use modern communication technologies to communicate with each other and the surrounding infrastructure. The IoV is a novel network that experiences a continuous emergence and evolution of various forms of attacks. This research addresses the growing challenge of detecting zero-day botnet attacks within the IoV, a complex network that enhances real-time communication among vehicles and surrounding infrastructure to improve traffic management, safety, and driving experiences. Traditional machine learning-based intrusion detection systems (IDS) for IoV face two key limitations: the requirement for large labeled datasets and the “black box” issue, where the reasoning behind model decisions is not transparent, reducing user and stakeholder confidence. To solve these problems, this research proposes an eXplainable Artificial Intelligence (XAI) Ensemble Transfer Learning (TL) model specifically for detecting zero-day attacks in IoV. The proposed model integrates deep Shapley Additive Explanations (SHAP), providing transparency and making decisions understandable to cybersecurity professionals. Additionally, the model employs hybrid bidirectional long-short-term memory with autoencoders (BiLAE) to reduce the dimensionality of IoV network traffic, improving computational efficiency. It also uses Barnacle Mating Optimizer (BMO) to optimize the hyper-parameters of deep learning models such as ResNet, Inception, Inception ResNet, and MobileNet Convolution neural network-transfer learning architecture (CNN-TL), enhancing detection capabilities without needing vast amounts of labeled data. Experimental results showed that the model performed with an accuracy of 100%, precision of 100%, recall of 100%, F1-score of 100%, and Matthew Correlation Coefficient of 100% in binary-class situations for internal vehicular (CAN) networks and achieved 99.88% accuracy and similarly high metrics in multi-class scenarios for external vehicular networks(N-BaIoT). Compared to state-of-the-art techniques, the model proved to be more effective in detecting zero-day botnet attacks, reducing reliance on large datasets. Unlike traditional black-box models, the XAI component of the ensemble model offers insight into the decision-making process. It allows network administrators and security experts to understand how specific patterns in the data contribute to detection, making the system more transparent. The solution is highly adaptable and scalable for real-time application, designed to operate efficiently even on IoV gateway electronic control units with limited computational power.

Vertical heterogeneity enhances network complexity and stability of co-occurrence microbes in the eastern Indian Ocean

Microbes are core to driving biogeochemical cycles and differ between sun-drenched surface and relatively dark deep oceans. However, their distinct contributions to the organization and association of communities are still remaining elusive. Here, their assembly and co-occurrence stability are systematically researched along the surface and vertical gradients in the eastern Indian Ocean. The distribution of surface microbes was grouped tightly with closer phylogenetic distance and broader niche breadth, and separately from those vertical samples. Clear distance-decay of community similarity was observed in surface microbes with lower richness, while more diverse microeukaryotes and prokaryotes were observed in surface and vertical environments, respectively. Co-occurrence microbes along vertical gradients had a more complex network that was dominated by prokaryotes, while exhibited a lower modularity compared to the surface network. Microbial associations along vertical gradients were more stable and resilient, with lower robustness, higher vulnerability, and a relatively consistent fragmentation. Moreover, prokaryotes contribute greatly to the network topology and stability compared to microeukaryotes in surface environments, emphasizing their distinct functions and survival strategies in maintaining community stability across spatial variations. Environmental selection and community differentiation led to the divergence in organization and potential function of microbes. This study shed light on new perspectives on how marine microbes were associated with and influenced by spatial heterogeneity and their distinct roles in community organization in the face of environmental fluctuations.

Skeletal muscle mitochondrial morphology negatively affected in mice lacking Xin.

Altered mitochondrial structure and function are implicated in the functional decline of skeletal muscle. Numerous cytoskeletal proteins are known to affect mitochondrial homeostasis, but this complex network is still being unraveled. Here, we investigated mitochondrial alterations in mice lacking the cytoskeletal adapter protein, XIN (XIN-/-). XIN-/- and wild-type littermate male and female mice were fed a chow or high-fat diet (HFD; 60%kcal fat) for 8 weeks before analyses of their skeletal muscles were conducted. Immuno-electron microscopy (EM) and immunofluorescence staining revealed XIN in the mitochondria and peri-mitochondrial areas, as well as the myoplasm. Intermyofibrillar mitochondria in chow-fed XIN-/- mice were notably different from wild-type (large, and/or swollen in appearance). Succinate dehydrogenase and Cytochrome Oxidase IV staining indicated greater evidence of mitochondrial enzyme activity in XIN-/- mice. No difference in body mass gains or glucose handling was observed between cohorts with HFD. However, EM revealed significantly greater mitochondrial density with evident structural abnormalities (swelling, reduced cristae density) in XIN-/- mice. Absolute Complex I and II-supported respiration was not different between groups, but relative to mitochondrial density, was significantly lower in XIN-/-. These results provide the first evidence for a role of XIN in maintaining mitochondrial morphology and function.

Modeling and analysis of COVID-19 spreading based on complex network theory

Complex networks can effectively describe interactions within real-world complex systems. In researches of epidemic spreading, scientists constructed various physical contact networks between individuals on the microscopic scale and the metapopulation networks on the macroscopic scale. These different types of network structures significantly impact the propagation dynamics of epidemic in human society. For instance, population flows in global airline networks influence the speed and arrival time of epidemics across large-scale space. In this paper we review the epidemic spreading models on various network structures, including fully mixed networks, three types of lower-order networks, three types of higher-order networks, metapopulation networks, and multiple strains competitive epidemic spreading models. We also provide an overview of the application of complex network theory in the COVID-19 pandemic, covering topics of prediction, prevention, and control of the epidemic. Finally, we discuss the strengths and limitations of these models and propose perspectives for future research.

A complex neural network model by Hilbert Transform

The phase information of the optical wave plays a vital role in processing wave-related signals. In deep learning fields, complex-valued neural networks are put forward on the concept of complex amplitudes for full utilization of phase information. To build a complex-valued neural network, the common way is to exploit Fourier Transform of the observed signal to extract amplitude and phase information. However, this will lead to spectrum waste for a real-valued signal by introducing negative frequencies that have no physical meaning. To this end, we attempt to use Hilbert Transform as an alternative to yield a single sideband spectrum and avoid negative frequencies from interacting with positive ones. On the other hand, Fourier transform is a global analysis thus it tells nothing about the time domain. As our key insight, we further explore the usage of instantaneous frequency calculated by Hilbert Transform and propose a new method of constructing complex input from a time–frequency angle. Simple pixel-wise classification experiments are carried out on two hyperspectral datasets and MNIST dataset. Experimental results have demonstrated that Hilbert Transform with instantaneous frequency performs better by a large margin than Fourier Transform owing to the additional time information.

RNA-seq analysis of LPS-induced immune priming in silkworms (Bombyx mori) and the role of cytochrome P450 detoxification system in the process

While immune priming has been identified in many invertebrates, the intricate mechanisms that drive this process in insects continue to be a subject of mystery. In this study, we exposed silkworm larvae to varying doses of lipopolysaccharide (LPS) to induce immune priming and assessed their survival upon challenge with Bacillus thuringiensis (Bt). Transcriptome analysis was performed to identify differentially expressed genes (DEGs) associated with immune priming. The role of CYP450 genes in this process was further explored using RNA interference (RNAi) to knockdown CYP9E2 and CYP6K1, followed by measurements of detoxification enzyme activities and reactive oxygen species (ROS) levels. We found that LPS exposure significantly increased silkworm survival rates upon Bt challenge, indicating the induction of immune priming. Transcriptome analysis revealed 549 DEGs, including a large number involved in detoxification, immunity, and metabolism, suggesting a complex regulatory network that encompasses immune responses and metabolic pathways. Functional enrichment and gene set enrichment analysis (GSEA) highlighted the activation of immune signaling pathways and the involvement of detoxification processes. Knockdown of CYP9E2 and CYP6K1 resulted in increased ROS levels, decreased detoxification enzyme activities, and reduced survival rates post-Bt challenge, implicating the critical role of these genes in immune priming and detoxification. Our findings demonstrate that LPS-induced immune priming in silkworms involves the upregulation of CYP450 genes, which play a critical role in detoxification and immune response modulation. The study provides insights into the molecular mechanisms of immune priming in insects and highlights the potential of CYP9E2 and CYP6K1 as targets for enhancing disease resistance and pest management in insects.

Analysis of rosmarinic acid synthase (RAS) gene family and functional study of SmRAS1/2/4 in Salvia miltiorrhiza

Rosmarinic acid synthase is an essential enzyme involved in the biosynthesis of rosmarinic acid (RA), which facilitates the coupling of phenylpropanoid and tyrosine-derived pathway products. Our study identified six SmRAS genes in Salvia miltiorrhiza, with SmRAS1 being the only one functionally characterized to date. Real-time quantitative PCR was employed to analyze the expression profiles of the SmRAS gene family, revealing that SmRAS1/2/4 are predominantly expressed in the roots, which are the medicinal components of S. miltiorrhiza. SmRAS2 and SmRAS4 exhibited significant responses to abscisic acid (ABA), gibberellin (GA3), and methyl jasmonate (MeJA) stimuli, while SmRAS1 had notable responses to GA3 and MeJA. β-glucuronidase (GUS) staining in transgenic Arabidopsis thaliana confirmed a spatiotemporal expression pattern of SmRAS1/2/4 that was consistent with the qRT-PCR results. SmRAS1/2/4 are primarily localized to the cytoplasm and plasma membrane. Our findings suggested that the overexpressions of SmRAS1 or SmRAS4 led to increased levels of salvianolic acid B (SalB) and RA, with a concomitant decrease in the Danshensu (DSS) content, which served as a substrate. In contrast, RNA interference lines exhibited a downward trend in the content of these substances. Interestingly, no significant changes were detected in the SalB, RA, or DSS contents due to the overexpression of SmRAS2 or RNA interference lines. Collectively, our study demonstrated that SmRAS1 and SmRAS4 are key regulators of RA and SalB biosynthesis in S. miltiorrhiza, while SmRAS2′s role appears less impactful, suggesting a complex regulatory network that influences the medicinal properties of this plant.

Exploring the Impact of Model Complexity on Laryngeal Cancer Detection.

Background: Laryngeal cancer accounts for a third of all head and neck malignancies, necessitating timely detection for effective treatment and enhanced patient outcomes. Machine learning shows promise in medical diagnostics, but the impact of model complexity on diagnostic efficacy in laryngeal cancer detection can be ambiguous. Methods: In this study, we examine the relationship between model sophistication and diagnostic efficacy by evaluating three approaches: Logistic Regression, a small neural network with 4 layers of neurons and a more complex convolutional neural network with 50 layers and examine their efficacy on laryngeal cancer detection on computed tomography images. Results: Logistic regression achieved 82.5% accuracy. The 4-Layer NN reached 87.2% accuracy, while ResNet-50, a deep learning architecture, achieved the highest accuracy at 92.6%. Its deep learning capabilities excelled in discerning fine-grained CT image features. Conclusion: Our study highlights the choices involved in selecting a laryngeal cancer detection model. Logistic regression is interpretable but may struggle with complex patterns. The 4-Layer NN balances complexity and accuracy. ResNet-50 excels in image classification but demands resources. This research advances understanding affect machine learning model complexity could have on learning features of laryngeal tumor features in contrast CT images for purposes of disease prediction.

Shenfu Injection Mediated NLRP3/Caspase 1 Through (R)-Norcoclaurinee Alleviates Sepsis-Induced Cognitive Dysfunction.

Shenfu injection (SF) has demonstrated its potential to enhance cellular immunity and induce clinical regression in patients suffering from sepsis or infectious shock. However, the therapeutic effect of SF on sepsis-induced cognitive dysfunction (SAE) and the mechanisms involved are still unclear. We aimed to investigate the mechanism of SF in mice with SAE. Sepsis was constructed by caecal ligation and puncture. Mice were injected intraperitoneally with SF or NLRP3 inhibitor. The hippocampus injury of brain tissues was evaluated, and the levels of inflammatory cytokines (IL-1β, IL-18) and NLRP3 and Caspase 1 were measured. The active ingredients of SF were analyzed using network pharmacology, and molecular docking of the active ingredients of SF with NLRP3 and Caspase 1 was performed. BV-2 cells were treated with LPS or norcoclaurine. CCK-8 detected the cell viability, and the levels of inflammatory cytokines and NLRP3 and Caspase 1 were measured. SF and NLRP3 inhibitor increased survival rate and the number of crossing the platform and decreased the escape latency time of sepsis mice. Moreover, SF and NLRP3 inhibitor improved neuronal damage and apoptosis in hippocampus of sepsis mice. In addition, SF and NLRP3 inhibitor reduced the levels of inflammatory cytokines, as well as inflammasomes in sepsis mice. There were 43 active ingredients in SF. Among them, 22 were Renshen and 21 were Fuzi. Renshen and Fuzi, the main active components of SF, form a complex regulatory network with NLRP3 and Caspase 1. (R)-norcoclaurine was most closely bound to NLRP3 with binding energy of -7.2 kJ·mol-1, ignavine was most closely bound to Caspase 1 with binding energy of -8.3 kJ·mol-1. Norcoclaurine increased the cell viability and decreased inflammation and pyroptosis. SF regulated NLRP3/Caspase 1 through (R)-norcoclaurinee to prevent SAE.

Leptospirosis in the Platypus (Ornithorhynchus anatinus) in Australia: Who Is Infecting Whom?

The platypus (Ornithorhynchus anatinus) is an amphibious, egg-laying mammal of high conservation value that is found only in Australia. The zoonotic bacterium Leptospira interrogans serovar Hardjo was discovered in platypuses in prior studies, but little is known about its epidemiology. Samples in the Platypus Serum Bank were tested in 2023 and the results were combined with historical records. Antibodies against L. interrogans serovar Hardjo were found in 50% of 464 serum samples from 411 platypuses collected from 14 river basins in southeastern Australia between 1981 and 2012; prevalence remained high over three decades in the Shoalhaven River population. Seroprevalence increased with age, suggesting environmental exposure. Individual platypuses had persistent titres, some for six years. Seropositive females lactated, juveniles were recruited into the population, and there were no reports of clinical leptospirosis. Three necropsied platypuses were seropositive and had mild nephritis with leptospires in the renal tubules. The high seroprevalence, persistent titres, lack of disease, mild renal lesions, and renal colonisation suggest the platypus may be a maintenance host. Sympatric cattle had L. interrogans serovar Hardjo titres, but the spatial association with seropositive platypuses was statistically weak. Other mammalian wildlife species and sheep also have L. interrogans serovar Hardjo titres; therefore, a complex ecological network must be considered. A landscape-wide study is recommended to properly assess transmission pathways and confirm who is infecting whom.

PROTAC-mediated activation, rather than degradation, of a nuclear receptor reveals complex ligand-receptor interaction network.

Proteolysis-targeting chimeras (PROTACs) are heterobifunctional molecules containing a ligand for a protein of interest linked to an E3 ubiquitin ligase ligand that induce protein degradation through E3 recruitment to the target protein. Small changes in PROTAC linkers can have drastic consequences, including loss of degradation activity, but the structural mechanisms governing such changes are unclear. To study this phenomenon, we screened PROTACs of diverse targeting modalities and identified dTAG-13 as an activator of the xenobiotic-sensing pregnane X receptor (PXR), which promiscuously binds various ligands. Characterization of dTAG-13 analogs and precursors revealed interplay between the PXR-binding moiety, linker, and E3 ligand that altered PXR activity without inducing degradation. A crystal structure of PXR ligand binding domain bound to a precursor ligand showed ligand-induced binding pocket distortions and a linker-punctured tunnel to the protein exterior at a region incompatible with E3 complex formation, highlighting the effects of linker environment on PROTAC activity.

Diethyl phthalate removal in waste gas from plastic formulations by membrane biofilm reactor

Diethyl phthalate (DEP), the most extensively used plasticizer in plastic formulations, is categorized as a priority pollutant with carcinogenic, teratogenic, and mutagenic toxicity. A membrane biofilm reactor (MBfR) for diethyl phthalate removal in waste gas from plastic formulations was investigated, MBfR achieved effective DEP removal in 72 days of operation, DEP removal efficiency achieved 91.8 %. DEP hydrolyzing bacteria (Halomonas, Microbacterium, Pseudomonas, Rhodococcus) and phthalic acid (PA)-degrading bacteria (Chelativorans, Halomonas) along with protocatechuic acid-degrading bacteria (Pseudomonas, Microbacterium, Stappia, Rhodococcus, Dietzia, etc.) jointly completed the esterification, ring cleavage, and mineralization of DEP. Functional bacterial consortium formed a complex microbial symbiotic network of DEP degradation. The DEP biodegradation gene clusters, such as pht2345, ligABCIJK, and pcaGHLBCDIJF, encoded corresponding degradation enzymes to accomplish the conversion of DEP→PA, the ring cleavage of PA, and the degradation of protocatechuic acid. DEP was metabolized to PA by de-esterification and re-esterification, further degraded to hydroxyquinol by ring-cleavage before ring-opening, and then further converted into acetyl-CoA, which was eventually metabolized into CO2 and H2O by participating in the tricarboxylic acid (TCA) cycle. This provides a feasible and environmentally friendly biotechnology for the removal of diethyl phthalate in waste gas using a membrane biofilm reactor.

Structural brain network organization in children with prenatal alcohol exposure

IntroductionThere is growing evidence suggesting that children with prenatal alcohol exposure (PAE) struggle with cognitively demanding tasks, such as learning, attention, and language. Complex structural network analyses can provide insight into the neurobiological underpinnings of these functions, as they may be sensitive for characterizing the effects of PAE on the brain. However, investigations on how PAE affects brain networks are limited. We aim to compare diffusion magnetic resonance imaging (MRI) tractography-based structural networks between children with low-to-moderate PAE in trimester 1 only (T1) or throughout all trimesters (T1-T3) with those without alcohol exposure prenatally. MethodsOur cohort included three groups of children aged 6 to 8 years: 1) no PAE (n = 24), 2) low-to-moderate PAE during T1 only (n = 30), 3) low-to-moderate PAE throughout T1-T3 (n = 36). Structural networks were constructed using the multi-shell multi-tissue constrained spherical deconvolution tractography technique. Quantitative group-wise analyses were conducted at three levels: (a) at the whole-brain network level, using both network-based statistical analyses and network centrality; and then using network centrality at (b) the modular level, and (c) per-region level, including the regions identified as brain hubs. ResultsCompared with the no PAE group, widespread brain network alterations were observed in the PAE T1-T3 group using network-based statistics, but no alterations were observed for the PAE T1 group. Network alterations were also detected at the module level in the PAE T1-T3 compared with the no PAE group, with lower eigenvector centrality in the module that closely represented the right cortico-basal ganglia-thalamo-cortical network. No significant group differences were found in network centrality at the per-region level, including the hub regions. ConclusionsThis study demonstrated that low-to-moderate PAE throughout pregnancy may alter brain structural connectivity, which may explain the neurodevelopmental deficits associated with PAE. It is possible that timing and duration of alcohol exposure are crucial, as PAE in T1 only did not appear to alter brain structural connectivity.

MiRNA99a as a Potential target in P13K/Akt1/mTOR signaling pathway in progression of OSCC

BackgroundOral cancer presents a significant global health challenge, driving ongoing research to enhance diagnostics and treatments. MicroRNAs, particularly miRNA99a, have emerged as key players in oral cancer's initiation, progression, and advanced development. However, their precise molecular mechanisms remain unclear. We analyze miRNA99a in modulating the PI3K/Akt1/mTOR signaling pathway within oral squamous cell carcinoma through in silico data analysis. Additionally, we examined miRNA99a levels in both oral submucous fibrosis (OSMF) and oral squamous cell carcinoma (OSCC). Materials and MethodsA comprehensive approach utilizing various insilico tools identified potential target genes regulated by miRNA99a and examined their interactions within the PI3K/Akt1/mTOR signaling pathway. The study involved meticulous screening, functional enrichment analyses, and network analyses to understand the regulatory networks influenced by miRNA99a. Additionally, RT-PCR was used to measure the CT levels of miR-99a in OSMF, OSCC and NM samples. ResultsThe analysis revealed a cohort of putative target genes regulated by miRNA99a, demonstrating their involvement in crucial cellular processes linked to OSCC progression. Functional enrichment analyses highlighted the significant association of these target genes with the PI3K/Akt1/mTOR pathway, indicating their potential impact on pivotal oncogenic signaling pathways. Network analyses revealed complex regulatory networks orchestrated by miRNA99a, its action within the PI3K/Akt1/mTOR signalling pathway and influencing OSCC development. RT-PCR analysis revealed a significant downregulation of miR-99a in OSCC and OSMF samples compared to NM, with mean CT values of 39.0940 and 38.3986 respectively, versus 33.7540 in NM (p = 0.000). ConclusionmiRNA99a's potential as a crucial regulator of the PI3K/Akt1/mTOR pathway in OSCC. The identified target genes and their interactions offer a foundation for further experimental validations, presenting opportunities for discovering novel therapeutic avenues or prognostic markers in managing OSCC. Integrating multi-omics data reinforces the significance of miRNA99a-mediated regulatory mechanisms in the intricate landscape of oral cancer biology.

Laboratory investigation on fracture initiation and propagation behaviors of hot dry rock by radial borehole fracturing

Creating complex and interconnected fracture networks between injection and production wells is crucial for exploiting hot dry rock (HDR) geothermal energy. However, the simple planar fractures created by conventional hydraulic fracturing, primarily controlled by in situ stress, fail to connect directionally with the target well. This study proposes a novel stimulation method, i.e. radial borehole fracturing, which shows great potential for guiding the directional propagation of fractures. The fracture initiation and propagation behaviors of high-temperature granite under radial borehole fracturing are investigated and compared with those of conventional fracturing. Three-dimensional morphological scanning and reinjection tests are used to quantitatively evaluate fracturing performance. Additionally, the influences of key parameters, including rock temperature, in situ stress, injection rate, fluid viscosity, azimuth of the radial borehole, and the number of radial boreholes on the fracture morphology and breakdown pressure are investigated. The results show that radial borehole fracturing can effectively guide the initiation and propagation of fractures along the radial borehole. The breakdown pressure of radial borehole fracturing can be reduced by 14.1%-43.7% compared to conventional fracturing. A higher fluid-rock temperature difference reduces the directional propagation range of fractures guided by the radial borehole. Increases in the vertical density of radial boreholes, injection rate, and fluid viscosity enhance the guiding ability of radial boreholes. Furthermore, there is a competitive relationship between in situ stress and the azimuth of radial boreholes in controlling fracture propagation. This research provides a viable alternative for the directional connection of injection-production wells in HDR reservoirs.

A putative NF-Y complex interacting with ERD15 may positively regulate the expression of a peroxidase gene in response to stress in rapeseed (Brassica napus L.)

Drought stress is one of the major constraints on crop productivity, including rapeseed (Brassica napus L.). Nuclear factors Y (NF-Ys) are important transcription factors involved in plant responses to drought and other stresses. However, the underlying molecular mechanisms remain unclear in rapeseed. By silencing BnaNF-YA9 in rapeseed and transforming BnaNF-YA9 into the Arabidopsis mutant Atnf-ya5, we demonstrated that BnaNF-YA9 plays a positive role in drought resistance. To explore its regulatory mechanism, we performed protein-protein interaction analyses using various approaches. Our study revealed complex interactions among BnaNF-YA9, BnaNF-YB2, BnaNF-YC4, and EARLY RESPONSIVE TO DEHYDRATION 15 (ERD15), suggesting that these proteins form a multimember complex. We also showed that BnaNF-YA9 binds to the CCAAT element in the promoter of a BnaPRX gene, which encodes a peroxidase. Interestingly, overexpression of BnaNF-YC4 or BnaERD15 in Arabidopsis increased sensitivity to salt stress, drought, and abscisic acid. Our results support an NF-Y/ERD15/PRX cascade and suggest a complex regulatory network in rapeseed that may be important in maintaining ROS homeostasis during abiotic stress responses. Our findings provide insights into potential targets for improving drought resilience in crops.

Negative collaboration risk analysis and control in manufacturing service collaboration based on complex network evolutionary game

In the era of the digital economy, manufacturing service collaboration (MSC) efficiently meets the demands of user personalization and customization through networked collaboration among service providers. However, negative collaboration behaviors triggered by opportunism within the network can lead to risk generation, risk propagation and potential dysfunction of the entire collaboration network. Traditional risk control strategies are often based on experience and lack predictive validation. To improve the efficiency of risk control strategy formulation and validate the effectiveness of risk control, the article proposes a negative collaboration risk control optimal strategy exploration method. This method can realize effective optimal decision-making for risk control and validation of effectiveness by combining the complex network evolutionary game and Q-learning. The experiment results show that this method is able to formulate situation-specific risk control strategies and keep negative collaboration providers below 10%, effectively controlling MSC collaboration risk.

Assessing port cluster resilience: Integrating hypergraph-based modeling and agent-based simulation

With increasing global trade and frequent occurrence of disruptive events, the resilience of port clusters has emerged as a critical area of concern. However, studies that focus on the resilience of port clusters considering their complex network structure and operational dynamics remain limited. This study proposes a novel model to assess port cluster resilience by integrating hypergraph-based modeling and agent-based simulation. The model captures the complex relationships among ports and vessels, enabling the dynamic modeling of disruption impacts on port cluster resilience. A case study of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) port cluster demonstrates the model’s applicability and effectiveness. Additionally, the significant impact of typhoon duration on resilience and the potential benefits of vessel port skipping behavior and port cargo handling capacity improvements are analyzed. These findings provide valuable insights for stakeholders in developing effective strategies to enhance the resilience of port clusters and the maritime transportation system.