Integration Of Networks Research Articles

Whole-brain modular dynamics at rest predict sensorimotor learning performance

Abstract Neural measures that predict cognitive performance are informative about the mechanisms underlying cognitive phenomena, with diagnostic potential for neuropathologies with cognitive symptoms. Among such markers, the modularity (subnetwork composition) of whole-brain functional networks is especially promising, due to its longstanding theoretical foundations and recent success in predicting clinical outcomes. We used functional magnetic resonance imaging to identify whole-brain modules at rest, calculating metrics of their spatio-temporal dynamics before and after a sensorimotor learning task on which fast learning is widely believed to be supported by a cognitive strategy. We found that participants’ learning performance was predicted by the degree of coordination of modular reconfiguration, and the strength of recruitment and integration of networks derived during the task itself. Our findings identify these whole-brain metrics as promising network-based markers of cognition, with relevance to basic neuroscience and the potential for clinical application.

Integrating higher-order relations for enhanced twitter bot detection

AbstractBot detection is a critical task in preserving the integrity of social networks and mitigating online disinformation. Despite advances in graph-based methods for detecting bots, these models often rely on follow relations, assuming that users sharing similar characteristics are more likely to connect. However, these methods often fail to address complex behavioral patterns indicative of coordinated bot activities, allowing bot developers to avoid detection more easily. We investigate the potential of integrating behavioral and higher-order relations, focusing on Retweet, Co-Retweet (where two users retweet the same tweet) and Co-Hashtag (where two users frequently use the same hashtag), and compare these against conventional follower and following relations. We further explore the process of relation creation, particularly highlighting the Co-Hashtag relation’s robustness against data collection flaws and its ability to mitigate shortcomings in the collection of datasets. Our experiments not only contribute to the ongoing efforts to address challenges in bot detection, but also open up new avenues for exploring how complex network patterns can be employed for social network analysis.

Profiles of brain topology for dual-functional stability in old age.

Dual-functional stability (DFS) in cognitive and physical abilities is important for successful aging. This study examines the brain topology profiles that underpin high DFS in older adults by testing two hypotheses: (1) older adults with high DFS would exhibit a unique brain organization that preserves their physical and cognitive functions across various tasks, and (2) any individuals with this distinct brain topology would consistently show high DFS. We analyzed two cohorts of cognitively and physically healthy older adults from the UK (Cam-CAN, n = 79) and the US (CF, n = 48) using neuroimaging data and a combination of cognitive and physical tasks. Variability in DFS was characterized using k-mean clustering for intra-individual variability (IIV) in cognitive and physical tasks. Graph theory analyses of diffusion tensor imaging connectomes were used to assess brain network segregation and integration through clustering coefficients (CCs) and shortest path lengths (PLs). Using support vector machine and regression, brain topology features, derived from PLs + CCs, differentiated the high DFS subgroup from low and mix DFS subgroups with accuracies of 65.82% and 84.78% in Cam-CAN and CF samples, respectively, which predicted cross-task DFS score in CF samples at 58.06% and 70.53% for cognitive and physical stability, respectively. Results showed distinctive neural correlates associated with high DFS, notably varying regional brain segregation and integration within critical areas such as the insula, frontal pole, and temporal pole. The identified brain topology profiles suggest a distinctive neural basis for DFS, a trait indicative of successful aging. These insights offer a foundation for future research to explore targeted interventions that could enhance cognitive and physical resilience in older adults, promoting a healthier and more functional lifespan.

Industrial Production Networks and Small Towns: A Case Study from Algeria

This paper investigates the conditions and consequences of integrating small towns into industrial production networks. It is based on empirical research conducted in Algeria, a hydrocarbon-dependent rentier economy characterized by significant regional inequalities and the political aims of economic diversification and spatial rebalancing. Elaborating the case study of a state-owned cement factory in the small town of Sigus, the research provides insights into the multiple roles of the state in shaping production network integration and the characteristics of small towns as economic locations. The methodology combines secondary data and information with primary research based on semi-structured interviews. It reveals the importance of a multi-scalar regional framework in production network integration, whereby national factors played a key role due to the centralized Algerian state, the state-owned character of the investing company, and the shortcomings of the small town’s local environment. It emphasizes the contradictory impacts of production network integration in economic, social, and environmental terms, primarily on a local level. These contradictions underscore the necessity for critical evaluations to maximize the benefits of production network integration while mitigating its adverse effects. They also call for the more consistent involvement of the local community in similar economic development decisions. Notably, this research contributes significantly to the existing body of literature by addressing the underexplored topic of integrating small towns into production networks within the Algerian context. Doing so offers a more nuanced understanding of the particular economic, social, and environmental dynamics at play in these locations, thereby enriching the discourse on economic development strategies for small towns in rentier economies like Algeria.

Social and emotional learning in the cerebellum.

The posterior cerebellum has a critical role in human social and emotional learning. Three systems and related neural networks support this cerebellar function: a biological action observation system as part of an extended sensorimotor integration network, a mentalizing system for understanding a person's mental and emotional state subserved by a mentalizing network, and a limbic network supporting core emotional (dis)pleasure and arousal processes. In this Review, I describe how these systems and networks support social and emotional learning via functional reciprocal connections initiating and terminating in the posterior cerebellum and cerebral neocortex. It is hypothesized that a major function of the posterior cerebellum is to identify and encode temporal sequences of events, which might help to fine-tune and automatize social and emotional learning. I discuss research using neuroimaging and non-invasive stimulation that provides converging evidence for this hypothesized function of cerebellar sequencing, but also other potential functional accounts of the posterior cerebellum's role in these social and emotional processes.

Optimization of a “Social‒Ecological” System Pattern from the Perspective of Ecosystem Service Supply and Demand: A Case Study of Jilin Province

This study establishes and refines a social-landscape ecological security pattern that integrates the demand and supply of ecosystem services, providing a substantial foundation for the ecological restoration of territorial spaces. This foundation is crucial for enhancing the effectiveness of “social–ecological” systems in achieving sustainable development. Jilin Province, serving as a national ecological security buffer and experiencing rapid economic growth, exhibits a significant spatial imbalance between social and economic progress and ecological conservation. The balance of ecosystem service demand and supply is pivotal in this context, making Jilin Province an ideal study area. We employed a multifaceted approach, including MSPA, the InVEST model, landscape connectivity assessment, circuit theory, and ecological network integrity evaluation, to elucidate the spatial disparities between the demand and supply of ecosystem services. We then developed and optimized social and landscape ecological security patterns to meet human demands and safeguard ecological integrity, thereby promoting the sustainable development of “social–ecological” systems. The key findings are as follows: (1) The supply of ecosystem services shows a clear spatial gradient, with lower values in the west and higher in the east, while demand is concentrated in the central region with lower values in the east and west, indicating a pronounced spatial mismatch in Jilin Province. (2) The landscape ecological security pattern includes 18 barrier points, 33 pinch points, 166 ecological corridors, and 101 ecological sources. (3) The social–ecological security pattern comprises 119 demand sources and 150 supply–demand corridors. (4) The study introduces 14 supply–demand nodes and 47 optimization corridors, proposing zoning schemes for the eastern core protection area, the central ecological demand area, and the western core restoration area. Additionally, recommendations are concerning the optimization of the “social–ecological” system pattern. This research advances the theoretical understanding of “social–ecological” system development in Jilin Province and offers insights for more harmonized development strategies.

A Summative Review of Advances in Sensor Technology for Precision Agriculture

Sensor technology has become a cornerstone of precision agriculture, offering a wide array of applications that enhance farming efficiency, productivity, and sustainability. By providing real-time data on soil health, crop growth, and environmental conditions, sensors enable farmers to optimize inputs such as water, fertilizers, and pesticides, reducing waste and improving yields. The development of advanced sensors, such as multi-spectral and hyper-spectral imaging, combined with artificial intelligence (AI) and machine learning (ML) algorithms, has revolutionized crop monitoring and disease detection, allowing for timely interventions that prevent significant losses. Additionally, the integration of wireless sensor networks (WSNs) and the Internet of Things (IoT) facilitates the seamless collection and transmission of data, enabling remote and automated farm management. This technology has proven instrumental in addressing the challenges of food security by increasing agricultural productivity and reducing input costs. Sensor technologies also promote sustainable land and water management by improving irrigation efficiency and reducing chemical runoff, thus minimizing environmental impact. Economic benefits are substantial, with farmers experiencing cost savings, increased yields, and higher profitability. Moreover, sensor technology contributes to climate-smart agriculture by improving resource use efficiency and reducing greenhouse gas emissions. However, challenges remain, including the high cost of sensor installation and maintenance, limited accessibility in remote regions, data privacy concerns, and the lack of interoperability between different sensor platforms. The future of sensor technology lies in the development of low-cost, biodegradable sensors, the increased use of autonomous and robotic platforms, and enhanced AI integration for more accurate data interpretation. Expanding the use of sensors in smallholder and developing world agriculture will be crucial for global food security and environmental sustainability, offering a promising path toward a more resilient and efficient agricultural system.

Improving Communication Networks to Transfer Data in Real Time for Environmental Monitoring and Data Collection

Integrated communication networks (CN) have proven successful in tracking environmental activities, wherein several sensors are installed throughout diverse surroundings to gather data or observe certain events. CNs, comprising several interacting detectors, have proven effective in various applications by transmitting data via diverse transmission methods inside the communication system. The erratic and constantly changing surroundings necessitate conventional CNs to engage in frequent conversations to disseminate the latest data, potentially incurring substantial connection expenses through joint data gathering and dissemination. High-frequency communications are prone to failure due to the extensive distance of data transfer. This research presents a unique methodology for multi-sensor environmental monitoring networks utilizing autonomous systems. The transmission system can mitigate elevated communication costs and Single Point of Failing (SPOF) challenges by employing a decentralized method that facilitates in-network processing. The methodology employs Boolean systems, enabling a straightforward verification process while preserving essential details about the dynamics of the communication system. The methodology further simplifies the data collection process and employs a Reinforcement Learning (RL) technique to forecast future events inside the surroundings by recognizing patterns.

Frontoparietal network integrity supports cognitive function in pre-symptomatic frontotemporal dementia: Multimodal analysis of brain function, structure, and perfusion.

Genetic mutation carriers of frontotemporal dementia can remain cognitively well despite neurodegeneration. A better understanding of brain structural, perfusion, and functional patterns in the pre-symptomatic stage could inform accurate staging and potential mechanisms. We included 207 pre-symptomatic genetic mutation carriers and 188 relatives without mutations. The gray matter volume, cerebral perfusion, and resting-state functional network maps were co-analyzed using linked independent component analysis (LICA). Multiple regression analysis was used to investigate the relationship of LICA components to genetic status and cognition. Pre-symptomatic mutation carriers showed an age-related decrease in the left frontoparietal network integrity, while non-carriers did not. Executive functions of mutation carriers became dependent on the left frontoparietal network integrity in older age. The frontoparietal network integrity of pre-symptomatic mutation carriers showed a distinctive relationship to age and cognition compared to non-carriers, suggesting a contribution of the network integrity to brain resilience. A multimodal analysis of structure, perfusion, and functional networks. The frontoparietal network integrity decreases with age in pre-symptomatic carriers only. Executive functions of pre-symptomatic carriers dissociated from non-carriers.

Rapid Detection of Single Bacteria Using Filter-Array-Based Hyperspectral Imaging Technology.

Rapid and accurate detection of bacterial pathogens is crucial for preventing widespread public health crises, particularly in the food industry. Traditional methods are often slow and require extensive labeling, which hampers timely responses to potential threats. In response, we introduce a groundbreaking approach using filter-array-based hyperspectral imaging technology, enhanced by a super-resolution demosaicking technique. This innovative technology streamlines the detection process and significantly enhances the resolution of mosaic hyperspectral imaging. By utilizing a snapshot hyperspectral camera with a 15 ms integration time, it facilitates the identification of bacteria at the single-cell level without requiring chemical labels. The integration of a 3D convolutional neural network optimizes the recognition of pathogenic bacteria, achieving an impressive accuracy of 91.7%. Our approach dramatically improves the efficiency and effectiveness of bacterial detection, providing a promising solution for critical applications in public health and the food industry.

Changes in core depressive symptoms over time in peripartum women: A network analysis

Perinatal depression (PND) is a prevalent mental health condition that affects women during pregnancy and after childbirth. Distinct clinical subtypes of PND exist, with the timing of symptom onset being a pivotal element in the classification of these subtypes. However, the specific manifestations of PND across the various stages of pregnancy and the postpartum period remain to be elucidated. This study aimed to explore the changes in depression symptoms with the stage of pregnancy. Women in their second (n = 161) and third trimesters (n = 248) of pregnancy as well as those in their first 6 weeks of the postpartum period (n = 110) were recruited. Each patient was evaluated using the Edinburgh Postnatal Depression Scale. A network analysis approach was used to explore the interconnections among depressive symptoms across the different time periods. Women in the postpartum period exhibited the most pronounced prevalence of PND and severity of depressive symptoms. In the second and third trimesters, “sadness and misery” was the most central symptom. However, its prominence diminished after childbirth. “Fear and panic” was the predominant symptom in the postpartum network. The structural integrity of PND symptom networks was maintained across all three periods, with consistent strength and closeness. This study demonstrates the temporal evolution of PND’s central symptoms in women during and after pregnancy, transitioning from depression-centric to anxiety-centric manifestations. These findings advocate for symptom-specific interventions to enhance the mental well-being of mothers and their offspring. Furthermore, this study offers clinical insights into the biological underpinnings of PND subtypes, facilitating precise diagnosis and targeted treatment strategies.

Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis.

Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML classification rests, agree with established diagnostic approaches based on clinical symptoms. Furthermore, complex network analyses highlight differences in brain network integration and segregation among the three groups. Our findings pave the way for refined, ML-enhanced diagnostics in accordance with established practices, offering a promising avenue for developing trustworthy clinical decision-support systems.

A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building

Short-term load forecasting is critical in managing energy production, domestic consumption, and grid integration. Short-term load forecasting is an essential tool for managing and controlling the processes required to make buildings smarter. Short-term load forecasting at this level of the network is extremely difficult due to the high uncertainty in load demand. This study presents a novel approach for predicting building loads using deep learning and non-intrusive load monitoring (NILM) techniques, aimed at enhancing smart building energy management. The methodology involves the integration of Recurrent Neural Networks (RNNs) to effectively capture temporal patterns in power consumption data, which are critical for accurate load forecasting. The model uses a feature matrix created from power signals to improve its forecast accuracy. The results show that the suggested model greatly outperforms standard models, with an overall accuracy of 97.2 %. The model's robustness is proven through comprehensive experiments, which demonstrate higher performance in short-term load forecasts. Furthermore, a comparison with existing models demonstrates the proposed approach's efficacy in recognizing and predicting load fluctuations, making it a useful tool for smart grid applications.

Structural resilience disparities in regional city networks under the influence of Typhoon Fitow and COVID-19

The integration of regional city networks plays a pivotal role in the regional development. Assessing and analyzing the structural resilience of these networks holds significance in enhancing regional comprehensive disaster prevention. This study focuses on the Yangtze River Delta, examining its regional city networks encompassing information, economic, transportation, and innovation domains. Utilizing the social network analysis, projection pursuit model based on the genetic algorithm, coupling coordination degree model, coefficient of variation and spatial Gini coefficient, we scrutinized the structural resilience and evolution difference of these networks under different disasters. We found that the structural resilience of economic and transportation networks that depend on infrastructure construction are more limited by geographical distance, and information and innovation networks with weaker physical dependence have stronger structural resilience. The impact of Typhoon Fitow on the structural resilience of individual networks was mainly concentrated in coastal affected cities, while the impact after COVID-19 was mainly in marginal cities. The spatial pattern of the structural resilience of multiple networks shifted from low level equilibrium to agglomeration development. Finally, nodes were identified into three types according to the resilience performance. This research offers a scientific foundation for quantifying regional resilience and formulating network optimization strategies.

Optimizing prediction accuracy in dynamic systems through neural network integration with Kalman and alpha-beta filters.

In the realm of dynamic system analysis, the Kalman filter and the alpha-beta filter are widely recognized for their tracking and prediction capabilities. However, their performance is often limited by static parameters that cannot adapt to changing conditions. Addressing this limitation, this paper introduces innovative neural network-based prediction models that enhance the adaptability and accuracy of these conventional filters. Our approach involves the integration of neural networks within the filtering algorithms, enabling the dynamic augmentation of parameters in response to performance feedback. We present two modified filters: a neural network-based Kalman filter and an alpha-beta filter, each augmented to incorporate neural network-driven parameter tuning. The alpha-beta filter is enhanced with neural network outputs for its α and β parameters, while the Kalman filter employs a neural network to optimize its internal parameter R and noise factor F. We assess these advanced models using the root mean square error (RMSE) metric, where our neural network-based alpha-beta filter demonstrates a significant 38.2% improvement in prediction accuracy, and the neural network-based Kalman filter achieves a 53.4% enhancement. Hence, our novel approach of integrating neural networks into filtering algorithms stands out as an effective strategy to significantly enhance their performance in dynamic environments.

Integration of Indian inventor networks in global value chains in pharmaceuticals industry

ABSTRACT The network approach of value chain analysis examines the role and centrality of various actors and their interrelationships with other actors, besides establishing the role of external linkages in this process. This paper tries to combine the effect of research networks on the geographic dispersion of innovative activity in the Indian pharmaceutical industry. We use activity-wise data from USPTO's patents to establish the level of integration of Indian innovative firms in the pharmaceutical global value chain. Using Tobit analysis, we examine the nature of the collaborative pattern of firms and establish whether this pattern of the inventor network level translates to integration into global networks in terms of geographic dispersion. We analyse the geographic dispersion of inventor networks as a function of the characteristics of the innovative actors that coordinate their inventive work. We show the level of integration of Indian innovative firms in the pharmaceuticals global value chain and assess the structure of these research networks. This helped contextualize firms' roles by contrasting them with other institutional types. The level of integration of Indian firms is low in terms of the geographic dispersal of innovation networks, while the private universities and public research centres are more geographically dispersed.

Advancements in Flexible and Stretchable Electronics for Resistive Hydrogen Sensing: A Comprehensive Review.

Flexible and stretchable electronics have emerged as a groundbreaking technology with wide-ranging applications, including wearable devices, medical implants, and environmental monitoring systems. Among their numerous applications, hydrogen sensing represents a critical area of research, particularly due to hydrogen's role as a clean energy carrier and its explosive nature at high concentrations. This review paper provides a comprehensive overview of the recent advancements in flexible and stretchable electronics tailored for resistive hydrogen sensing applications. It begins by introducing the fundamental principles underlying the operation of flexible and stretchable resistive sensors, highlighting the innovative materials and fabrication techniques that enable their exceptional mechanical resilience and adaptability. Following this, the paper delves into the specific strategies employed in the integration of these resistive sensors into hydrogen detection systems, discussing the merits and limitations of various sensor designs, from nanoscale transducers to fully integrated wearable devices. Special attention is paid to the sensitivity, selectivity, and operational stability of these resistive sensors, as well as their performance under real-world conditions. Furthermore, the review explores the challenges and opportunities in this rapidly evolving field, including the scalability of manufacturing processes, the integration of resistive sensor networks, and the development of standards for safety and performance. Finally, the review concludes with a forward-looking perspective on the potential impacts of flexible and stretchable resistive electronics in hydrogen energy systems and safety applications, underscoring the need for interdisciplinary collaboration to realize the full potential of this innovative technology.

Cryptography in Network Security: a Mini Review

Network security and cryptography are crucial components in safeguarding data and communication in the digital age. Cryptography plays a vital role in network security by converting plain text into unreadable formats to protect confidentiality and integrity. As organizations generate vast amounts of data daily, ensuring secure data transfer over the internet becomes increasingly important, with network security challenges growing as society transitions to the digital era. Techniques such as encryption are used to protect enterprise information and communication from cyber threats, ensuring the integrity, confidentiality, and accessibility of computer networks and data. Encryption and network security are employed to protect data stored in systems and exchanged over wireless networks, with cryptography utilizing various algorithms and approaches to secure networks and data effectively.

An endophenotype network strategy uncovers YangXue QingNao Wan suppresses Aβ deposition, improves mitochondrial dysfunction and glucose metabolism

BackgroundAlzheimer's disease (AD), an escalating global health issue, lacks effective treatments due to its complex pathogenesis. YangXue QingNao Wan (YXQNW) is a China Food and Drug Administration (CFDA)- approved TCM formula that has been repurposed in clinical Phase II for the treatment of AD. Identifying YXQNW's active ingredients and their mechanisms is crucial for developing effective AD treatments. PurposeThis study aims to elucidate the anti-AD effects of YXQNW and to explore its potential therapeutic mechanisms employing an endophenotype network strategy. MethodsHerein we present an endophenotype network strategy that combines active ingredient identification in rat serum, network proximity prediction, metabolomics, and in vivo experimental validation in two animal models. Specially, utilizing UPLC-Q-TOF-MS/MS, active ingredients are identified in YXQNW to build a drug-target network. We applied network proximity to identify potential AD pathological mechanisms of YXQNW via integration of drug-target network, AD endophenotype gene sets, and human protein interactome, and validated related mechanisms in two animal models. In a d-galactose-induced senescent rat model, YXQNW was administered at varying doses for cognitive and neuronal assessments through behavioral tests, Nissl staining, and transmission electron microscopy (TEM). Metabolomic analysis with LC-MS revealed YXQNW's influence on brain metabolites, suggesting therapeutic pathways. Levels of key proteins and biochemicals were measured by WB and ELISA, providing insights into YXQNW's neuroprotective mechanisms. In addition, 5×FAD model mice were used and administered YXQNW by gavage for 14 days at two doses. Amyloid-β levels, transporter expression, and cerebral blood flow have been detected by MRI and biochemical assays. ResultsThe network proximity analysis showed that the effect of YXQNW on AD was highly correlated with amyloid β, synaptic function, glucose metabolism and mitochondrial function. The results of metabolomics combined with in vivo experimental validation suggest that YXQNW has the potential to ameliorate glucose transport abnormalities in the brain by upregulating the expression of GLUT1 and GLUT3, while further enhancing glucose metabolism through increased O-GlcNAcylation and mitigating mitochondrial dysfunction via the AMPK/Sirt1 pathway, thereby improving d-galactose-induced cognitive deficits in rats. Additionally, YXQNW treatment significantly decreased Aβ1–42 levels and enhanced cerebral blood flow (CBF) in the hippocampus of 5×FAD mice. while mechanistic findings indicated that YXQNW treatment increased the expression of ABCB1, an Aβ transporter, in 5×FAD model mice to promote the clearance of Aβ from the brain and alleviate AD-like symptoms. ConclusionsThis study reveals that YXQNW may mitigate AD by inhibiting Aβ deposition and ameliorating mitochondrial dysfunction and glucose metabolism, thus offering a promising therapeutic approach for AD.

Adaptive Strategies and Sustainable Innovations of Chinese Contractors in the Belt and Road Initiative: A Social Network and Supply Chain Integration Perspective

As global economic integration and rapid technological advancements transform international business, international engineering contracting has become essential for achieving sustainable development goals (SDGs). This paper investigates the significant impact of China’s strategic initiatives, notably the “Going Global” strategy and the Belt and Road Initiative (BRI), on the operational practices of Chinese enterprises involved in overseas investments. Central to this transformation is the Engineering, Procurement, and Construction (EPC) model, which emphasizes the integration of supply chain management and stakeholder collaboration to enhance performance in international EPC projects and underscores the crucial role of these elements in promoting sustainability. Incorporating insights from social network data analysis, this study reveals that contractors collaborating with various stakeholders—such as owners/consulting engineers, domestic and foreign customs departments, and group headquarters/design parties—exhibit a high degree of similarity in personnel profiles. This suggests that the internal organizational structure and personnel allocation of contractors could be optimized to enhance operational efficiency, aligning with the collaborative patterns identified. This study addresses a critical research gap by exploring how effective supply chain management and collaborative stakeholder engagement within multinational EPC projects contribute to sustainable outcomes. Employing advanced social network analysis software, the research examines the complex interactions among stakeholders and their influence on procurement dynamics. Findings indicate that strong relational networks and strategic collaborations significantly enhance procurement efficiency and project success, underscoring the importance of supply chain integration. Ultimately, integrating supply chain management principles into the EPC model not only offers innovative perspectives for advancing sustainability in international projects but also provides actionable insights for improving project outcomes within the BRI framework. This research underscores the pivotal role of supply chain organization and stakeholder cooperation in achieving sustainability objectives, thereby enriching the discourse on sustainable enterprise operation and supply chain management in the context of global initiatives.