GABA and ABA-mediated mitigation of salt stress-induced oxidative damage by modulating secondary metabolites, defense metabolism, and photosynthetic pathways in wheat.

ABSTRACT Contemporary science and technology diplomacy often operates through leader‐centric, event‐driven, and weakly institutionalized arrangements, which are ill‐suited to domains marked by high interdependence, nonlinearity, and rapidly evolving knowledge. This article proposes Complex Adaptive Science and Technology Diplomacy (CASTD) not as a descriptive label for the field as a whole, but as a conditional analytical and institutional design framework for contexts in which conventional diplomatic instruments face structural limits. Rather than treating diplomacy as an episodic extension of foreign policy, the article suggests reconceiving it, under specific conditions of systemic risk and distributed authority, as a form of strategic infrastructure for governing uncertainty. Drawing on complexity science, polycentric governance, and critical perspectives on science diplomacy, the framework addresses persistent limitations in the literature, including conceptual ambiguity, technocratic depoliticization, and limited evaluability. CASTD identifies three interdependent capacities, adaptive governance, network orchestration, and integrative synthesis, and outlines how these can be operationalized through coordination architectures and structured learning processes. Illustrative cases indicate that durable cooperation depends less on episodic leadership than on reproducible and adaptable forms of coordination.

Background Mobile network demand is increasingly volatile due to collective human activities such as concerts, sports events, and public gatherings. Traditional capacity planning methods, largely based on historical network indicators, struggle to anticipate these transient and localized demand surges. Recent advances in social sensing and artificial intelligence suggest that web and social signals can provide early indicators of real-world events, enabling proactive, event-aware network management in 5G and beyond. Methods This paper presents a combined analysis and empirical study on AI-driven event-aware demand forecasting for mobile networks. We first review methods for extracting event signals from web and social data and analyze prior evidence linking such signals to cellular traffic variations. We then introduce a forecasting-driven orchestration pipeline and evaluate it through a case study using the NetMob’23 dataset, which provides high-resolution, service-level mobile traffic traces from multiple urban areas. Several forecasting models—ranging from naïve baselines and linear regression to Random Forests and LSTM neural networks—are compared. We further investigate the impact of event-related features and introduce an asymmetric loss function designed to penalize traffic underestimation in proactive orchestration scenarios. Results Results show that AI-based sequential models, particularly LSTM architectures, significantly outperform classical approaches in both prediction accuracy and operational effectiveness. Incorporating event-aware features reduces forecasting errors by up to 30% and yields substantial reductions in network overload under constrained capacity. The proposed asymmetric loss further improves robustness, nearly eliminating overload events at the cost of limited over-provisioning. Additional experiments demonstrate graceful degradation under noisy or unreliable event information. Conclusions The study confirms that integrating web-derived event signals into AI-based forecasting pipelines provides a measurable anticipatory advantage for proactive mobile network orchestration. Event-aware forecasting emerges as a key enabler for predictive, self-optimizing 5G/6G infrastructures, bridging social sensing and automated network management.

BackgroundMigraine accounts for most primary headaches in children and adolescents and is related to cortical and connectivity changes. However, the underlying mechanisms remain unclear. Morphometric similarity mapping has not yet been applied to children and adolescents with migraine.MethodsEighty-three patients (6-17 years) with migraine without aura and 81 age- and sex-matched controls were retrospectively included. High-resolution 3D T1-weighted and diffusion-weighted magnetic resonance imaging scans were processed to extract cortical morphometric parameters and compute morphometric similarity networks (MSN). Global and regional MSN differences were assessed between patients and controls, and across subgroups defined by sex, attack frequency and migraine-associated symptoms.ResultsPatients showed significant MSN alterations, particularly in temporal and cingulate regions. Sex emerged as the strongest factor influencing MSN architecture, with additional modulations linked to attack frequency and clinical symptoms. Affected pathways encompassed the executive control, nociceptive and default mode networks.ConclusionsMigraine in children and adolescents is associated with widespread MSN abnormalities, likely reflecting cortical reorganization mechanisms. Male and female patients appear to engage distinct neural "orchestras", each emphasizing different network sections (sensory-affective in males and cognitive-attentive in females) to produce a shared clinical experience. These findings highlight sex as a key determinant of migraine neurobiology in developmental age.

Future 6G technology needs to meet dynamic requirements in diverse scenarios, as recommended by the International Telecommunication Union (ITU). The conventional monolithic radio access network (RAN) equipped with dedicated hardware fails to meet the rapid iteration and dynamic requirements of networks. Consequently, there is a need to reform RAN architecture and management strategies. The paper proposes and demonstrates a dynamic service-based RAN. The flexibility of the RAN is enhanced by decoupling network capabilities, including both functional and state decoupling. RAN comprises services that can be independently deployed. These services exhibit stateless characteristics, enabling them to be registered and discovered by the system. Furthermore, the Data Plane and Digital Plane are introduced to efficiently manage highly flexible services, thereby establishing a functional network. Services can be effectively managed to facilitate network orchestration and scaling through on-demand scheduling. The current research examines the differences between static and dynamic networks in their approaches to handling evolving scenarios. By dynamically scaling services to meet user requirements, the network avoids unnecessary scaling of all functions at the RAN level. The dynamic network has been demonstrated to be more efficient and cost-effective in scenarios where the number of users and data rates fluctuate.

Emerging metaverse experiences, including interactive extended reality (XR) sessions and live holographic telepresence, necessitate motion-to-photon latencies of less than 10 ms. These applications must also manage the continuous streaming of multi-gigabit data volumes to thousands of mobile users. To meet these extreme requirements, an orchestration layer capable of instantly decomposing, placing, and adapting the dependency structures of microservices formally modeled as directed acyclic graphs (DAGs) underlying computationally intensive artificial intelligence (AI)-driven immersive applications is required. We propose an AI-native cloud-edge orchestration framework in which a Large Language Model (LLM) based cloud planner serves as a cognitive conductor. This planner uses Topology-Aware Retrieval-Augmented Generation (TopoRAG) to retrieve and interpret historical deployment traces to create latency-optimized orchestration plans. Trust-weighted logits, semantic cost estimates, and initial node bindings are output as soft priors and streamed to decentralized edge workers powered by deep reinforcement learning (DRL) with multiple agents. These DRL agents integrate global intentions with rapidly changing local conditions to enable real-time context-aware planning. In addition, we introduce a deviation-based reward mechanism that compares actual execution costs with estimates predicted by the LLM, providing dense and informative feedback that effectively halves the DRL convergence time. Simulations in urban-scale 6G networks with real-time volumetric video stitching and multiuser XR gaming workloads show a significant reduction in SLA violations and significantly lower end-to-end latency compared to baseline schedulers, while maintaining optimal motion-to-photon latency.

The current study utilizes the major servitization enablers, including value co-creation, service customization, technology integration and network orchestration, as the core factors influencing customer centricity in the Indian automotive service industry. As the industry has shifted to service-oriented value creation, it is necessary to assess those relationships through the lens of the automotive service providers. This study is grounded in the service-dominant logic (SDL), dynamic capabilities (DC) theory and product-service systems (PSS) approach and investigates the data obtained from 179 Indian automotive service providers. Fuzzy-set qualitative comparative analysis (fsQCA) reveals multiple equifinal configurations that lead to high customer centricity, demonstrating that no single enabler is appropriate. These findings are further substantiated by artificial neural network (ANN) analysis, which reveals that value co-creation plays a vital role in service performance and is the most important enabler of customer centricity (normalized importance = 100%). This dual methodological approach strengthens the results. The research presented feasible observations to automotive service providers, emphasizing the significance of value co-creation, enhancement of co-creation and the creation of flexible service systems that enable customer-oriented decision-making.

The transition to 6G and Open RAN (O-RAN) necessitates intelligent orchestration across multi-operator networks, yet this collaboration introduces severe security and privacy risks. Malicious operators may poison global models through adaptive attacks, while the exchange of raw gradients threatens data sovereignty. In this paper, we propose FORTRESS-FL , a robust and privacy-preserving federated learning framework designed for secure cross-domain orchestration. At its core is the TrustChain protocol, which synergizes a commit-then-reveal scheme to prevent adaptive manipulation, unsupervised spectral clustering for Byzantine detection, and a dynamic reputation system to isolate malicious actors. Furthermore, we integrate an adaptive Differential Privacy (DP) mechanism to rigorously protect operator data. Extensive evaluation on a real-world financial fraud dataset demonstrates that FORTRESS-FL achieves 100% detection accuracy against sign-flip attacks with 30% Byzantine adversaries, preventing the model divergence observed in standard baselines. Scalability tests confirm linear complexity with respect to the number of operators, validating the framework’s feasibility for large-scale, real-time network orchestration.

In recent years, the increasing demand for high-performance, scalable, and adaptable network infrastructures has led to the widespread adoption of virtual networks across data centers, cloud computing environments, and enterprise systems. However, because of their strict and hardwaredependent control mechanisms, traditional network topologies sometime find it difficult to match the performance and adaptability needs of this dynamic environments. In order to improve the performance of virtual networks, this study investigates the use of software-defined networking (SDN),a revolutionary technique that allows for centralized and programmable network management by separating the control and data planes. SDN-enabled network management and conventional network operation without SDN are the two scenarios in which performance metrics are methodically gathered and examined. The findings unequivocally show that SDN significantly enhances bandwidth usage, traffic flow optimization, and dynamic routing modifications particularly in high-load of failure scenarios. Additionally, SDN’s programmable nature enables real-time network adaption which lowers downtime and improves quality of service (QoS). By providing empirical support for the incorporation of SDN into virtual network topologies, the study add to corpus of knowledge. Additionally it offers a platform for implementing SDN based fixes to existing systems’ performance snags. The findings indicate that SDN not only boosts the efficiency of virtual networks but also lays a foundation for incorporating intelligent network automation, paving the way for future innovations such as AI-driven network orchestration.

This study investigates how next-generation digital infrastructures—terahertz (THz) communication and AI-driven network orchestration—shape perceived service quality, luxury perception, and loyalty within the context of luxury hospitality. An empirical survey was conducted among 693 guests at Torre Melina Gran Meliá (Barcelona) between June 2024 and June 2025. Using a refined 38-item Likert-scale instrument, a three-factor structure was validated: (F1) Network Performance (speed, stability, coverage, seamless roaming, and multi-device reliability), (F2) Luxury Perception (modernity, innovation, and brand image), and (F3) Service Loyalty (satisfaction, return intentions, recommendations, and willingness to pay a premium). The results reveal that superior network performance functions both practically and symbolically. Functionally, it enables uninterrupted video calls, smooth streaming, low-latency gaming, and reliable multi-device usage—now considered essential utilities for contemporary travelers. Symbolically, high-performing and intelligently managed connectivity conveys technological leadership and exclusivity, thereby enhancing the hotel’s luxury image. Collectively, these effects create a “virtuous cycle” in which technical excellence reinforces perceptions of luxury, which in turn amplifies satisfaction and loyalty behaviors. From a managerial perspective, advanced connectivity should be viewed as a strategic investment and brand differentiator rather than a cost center. THz-ready, AI-orchestrated networks support personalization, dynamic bandwidth allocation (i.e., real-time adjustment of network capacity in response to fluctuating user demand), and monetizable premium service tiers, directly strengthening guest retention and brand equity. Ultimately, next-generation connectivity emerges not as an ancillary amenity but as a defining pillar of luxury hospitality in the emerging 6G era.

Recent advancements in information and communication technologies necessitate powerful computing power and network capabilities. Fortunately, Computing Power Networks (CPNs) have emerged to seamlessly integrate distributed computing resources via network orchestration, enabling high-throughput and on-demand computing services. However, CPNs consume substantial energy and generate significant carbon emissions when processing massive data. What’s worse, the interplay between CPN nodes and network paths, and spatiotemporal variations in renewable energy complicate energy-efficient task scheduling. To address these issues, we design a joint spatiotemporal task scheduling and hybrid energy coordination mechanism to efficiently manage and allocate computing, network, and energy resources, achieving green orchestra in CPNs. Firstly, we design a novel green CPN framework that synergizes computing resources, network resources, and enhanced energy coordination systems. Then, we propose a spatiotemporal task scheduling scheme with a triple selection of CPN nodes, routing paths, and forwarding time. The scheme can optimize energy consumption and carbon emissions while ensuring delay constraints and load balancing. Lastly, we formulate the problem as a Markov Decision Process (MDP) and design a customized Deep Reinforcement Learning (DRL) approach to solve it. Simulation results validate our scheme outperforms the benchmark schemes in learning efficiency, energy savings, carbon reduction, and renewable energy utilization efficiency.

Service placement in next-generation networks faces challenges in meeting ultra-low latency demands and managing complex multi-tenant infrastructures. Ensuring that service placement meets stringent quality of service (QoS) standards while addressing these complexities is critical. A key gap exists in delivering consistent network slicing (NS) capabilities within multi-tenant networks, particularly in shared physical infrastructures. This paper proposes a resource provisioning framework for network slices in an network function virtualization (NFV)-based framework. By integrating software-defined networking (SDN) with management and network orchestration (MANO), our approach ensures efficient slice management and QoS compliance, even under unpredictable tenant numbers and resource utilization. The proposed framework maximizes energy efficiency (EE) in multi-tenant, multi-domain NS environments under demand uncertainty, considering end-to-end latency and minimum bit rate as service-level agreement (SLA) constraints. We formulate the problem as a non-convex mixed-integer non-linear programming (MINLP) and solve it using a novel multi-agent deep deterministic policy gradient (MADDPG) algorithm. Compared to a baseline that neglects demand uncertainties, our framework achieves near-optimal solutions with competitive execution time, improving performance by up to 66% over existing benchmarks.

The Sixth-Generation (6G) are already in the horizon, owing to advents of communication technologies towards enabling intelligent applications and services. Federated Learning (FL) is a distributed Artificial Intelligence (AI) technology that underpins 6G communication technologies and applications. Interestingly, FL is also a promising contender to enhance 6G security. This paper presents a comprehensive and up-to-date review of FL-enabled 6G security. The paper explores security threats in FL for 6G, threats in FL for 6G, and threats shared across FL and 6G. Subsequently, how FL can be utilized to strengthen 6G security in the Radio Access Network (RAN), Open RAN (O-RAN), network edge, and network orchestration and core is presented. In addition, FL is for 6G application and service security across various emerging applications, ranging from Connected Autonomous Vehicles (CAVs) to the envisaged metaverse applications. The paper then consolidates lessons learned, projects, and proposes future research directions to establish the role of FL in strengthening 6G security.

Supply chains are vital units of analysis of the circular economy (CE), and network orchestrators are central to fostering the CE’s essential interorganizational collaboration. However, orchestration mechanisms remain poorly understood. We explore supply chain orchestration (SCO) as an innovative business model to promote CE. Our analysis of The Lost Food Project—an eco-food bank that rescues surplus food to address hunger in Greater Kuala Lumpur—suggests the need to adapt SCO for circularity (as SCO-C). We highlight the challenges that arise when organizations focus on SCO-C: capturing the value of environmental and social profits, the necessity of supportive public policies, and plausible unintended consequences.

Modern datacenter infrastructure faces significant challenges when hosting diverse workloads on shared computing resources while maintaining performance guarantees and cost efficiency. This article presents a comprehensive framework for running isolated, CPU-pinned, NUMA-aligned workloads alongside unpinned, flexible workloads on shared hypervisor infrastructure through artificial intelligence-driven optimization techniques. The framework addresses fundamental limitations in current virtualization platforms that fail to exploit NUMA topology information for intelligent workload placement, resulting in performance degradation and resource underutilization. Container-based microservices and telecommunications network functions experience substantial performance penalties from cross-NUMA memory access patterns and unpredictable resource allocation decisions. The proposed solution combines systematic workload classification with machine learning algorithms for predictive placement decisions and dynamic rebalancing capabilities. Implementation involves comprehensive hardware topology discovery, resource partitioning strategies, and integration with existing container orchestration platforms. Performance evaluation demonstrates substantial improvements across telecommunications network functions, edge AI systems, high-performance computing applications, multi-tenant cloud platforms, and future 6G network orchestration scenarios. The framework enables organizations to achieve deterministic performance guarantees for critical applications while maximizing infrastructure utilization through intelligent resource sharing, providing economic benefits through reduced hardware requirements and energy consumption.

The implementation of network slicing in fifth-generation (5G) mobile networks enables the logical partitioning of physical infrastructure into multiple virtualized slices tailored for distinct service requirements such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). However, this dynamic virtualization layer expands the system’s attack surface, introducing novel security vulnerabilities including slice isolation breaches, side-channel attacks, rogue slice instantiation, and service orchestration tampering. This review examines these vulnerabilities through a layered security perspective—spanning the radio access network (RAN), transport, and core domains—and analyzes how artificial intelligence (AI)-driven intrusion detection systems (IDS) can mitigate them. The study evaluates deep learning architectures such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Graph Neural Networks (GNN) for detecting anomalous inter-slice traffic and malicious orchestration behaviors within Software-Defined Networking (SDN) and Network Function Virtualization (NFV) environments. Moreover, the paper proposes a hybrid AI-IDS framework leveraging feature extraction from 5G control and user plane packets, unsupervised clustering for zero-day anomaly detection, and reinforcement-learning-based adaptive response. Experimental validation using the 5G-TONIC and Aalto University open datasets demonstrates over 96% detection accuracy with reduced false alarm rates under real-time conditions. The findings contribute to resilient 5G network orchestration and establish a foundation for adaptive threat intelligence in forthcoming 6G architectures.

In light of the rapid advancement of biomedical technologies and intensifying geopolitical competition over technological dominance, international cooperation in this field has garnered significant attention among scholars, policymakers, and industry leaders. This study employs comprehensive patent data spanning from 2002 to 2023 across the global biomedical landscape to systematically investigate the characteristics and evolution patterns of both global and Chinese biomedical inventions. Employing econometric analysis of patents and social network analysis, the research examines three key dimensions: patent output, technological pathways, and collaboration networks. Three research findings are derived. Firstly, China has transitioned from a “catch-up collaborator” to a critical hub facilitating cross-national and cross-disciplinary knowledge flows. Despite challenges posed by U.S. technological decoupling policies, China remains deeply embedded in the global technology system. Amid escalating geopolitical tensions, China-Western cooperation is undergoing strategic rebalancing, as China expands its collaboration with emerging economies and Belt and Road countries to build a more diverse, resilient, and adaptive global biomedical cooperation network. Secondly, China is progressively strengthening its technological leadership within international collaborations. Antibody-based technologies have emerged as a central area of cooperation, reflecting a shift from traditional broad-spectrum formulations to precision-targeted therapies and high-complexity drug delivery systems. This transformation underscores China’s evolving role-from a recipient of general-purpose technologies to a co-creator of frontier innovations-increasingly integrated into the global high-end biomedical innovation ecosystem. However, significant gaps persist compared to developed economies, particularly in upstream capabilities such as platform technologies, fundamental pharmaceutical chemistry, and gene editing. Thirdly, while the U.S. and European countries continue to lead global technological paradigms in foundational and platform technologies-shaping a pattern of strategic complementarity-China-U.S. cooperation remains concentrated in midstream R&D activities within the industrial chain. This collaboration is characterized by a consensus-driven, market-oriented approach, focusing on functional modifications and applied innovations based on existing targets rather than constructing differentiated knowledge assets from the ground up. Consequently, a stable framework for long-term complementarity and platform-level coordination has yet to be established. This study contributes to a deeper understanding of the evolutionary mechanisms underlying China’s transformation from a “technology taker” to a “network orchestrator” within the global biomedical collaboration landscape. From a policy perspective, it offers valuable insights for advancing China's strategy of high-level scientific and technological openness and building a strategically influential and internationally embedded innovation system. Specifically, the study proposes five policy implications for China. (1) Establish strategic international joint R&D platforms to enhance China’s embeddedness in critical innovation nodes. (2) Guide resource allocation toward high-barrier, highly original technological domains to strengthen China’s control within the global value chain. (3) Optimize incentive mechanisms for cross-border collaboration to attract high-quality international cooperation resources to China. (4) Fortify the national bio-data defense system and develop an autonomous and controllable biomedical data ecosystem. (5) Encourage the participation of diverse actors in international collaboration networks to enhance China’s influence in high-quality collaborative patents.

Purpose The masterminds behind covert supply chain operations aim to hide their activities from government agencies and society at large, often for illegal gains or to intentionally cause harm. This conceptual article outlines a research agenda for future studies by framing covert supply chain activities and the countermeasures used to disrupt them. Design/methodology/approach Secondary data were collected from various news sources (observation) and analyzed to understand the nature of covert supply chain operations and efforts to identify and disrupt them (conceptualization). Findings To date, covert supply chain operations and counter-operations categories have been scarcely scrutinized in the supply chain literature, and our framework presents many fruitful avenues for further research. Practical implications Policymakers may aim to enhance the visibility of covert supply chains to achieve strategic objectives. Our framework enables logistics providers, network orchestrators, and shippers to identify vulnerabilities and detect covert infiltration by hostile actors within customer supply networks. Originality/value The mainstream supply chain literature has viewed supply chains of illegal goods and disruptive counter-operations as piecemeal. This conceptual article addresses the topic holistically to create a framework for guiding future research.

This study examines collaborative public management (CPM) in underground water bank projects across three Thai local governments, addressing gaps in CPM research that predominantly focuses on developed countries. Using qualitative methodology with 32 in-depth interviews across five stakeholder groups local government officials (9), government agency representatives (9), academic experts (3), community leaders and citizens (9), and civil society organizations (2) the research investigates how CPM operates in resource-constrained environments. The study explores three key questions: how CPM dimensions manifest in resource-limited contexts, whether technology enhances collaborative governance relationships, and what mechanisms enable knowledge integration in environmental management. Findings reveal that effective CPM requires hybrid leadership combining transformational and network orchestration capabilities, technology-enhanced coordination that builds upon rather than replaces existing social capital, and institutional layering that creates new arrangements while respecting traditional structures. The research contributes eight theoretical advances to collaborative governance understanding, demonstrating that CPM dimensions manifest differently in developing countries and require systematic adaptation rather than direct replication of developed country models. Results highlight the importance of context-sensitive CPM approaches and advance knowledge of technology-social capital integration in developing country environmental governance, particularly in water resource management.

Achieving both a holistic and in-depth understanding of network dynamics through accurate modeling is essential for orchestrating future 6G networks, considering their increasing complexity and service diversity. However, traditional situation-agnostic data collection and network modeling approaches often undermine the efficacy and timeliness of network orchestration in such complex environments. Furthermore, temporal misalignments caused by varying modeling delays across distributed networks further impair centralized decision-making. To address these challenges, this paper proposes a hierarchical digital twin framework with an adaptive layered architecture designed for problem-oriented 6G network modeling and orchestration. At higher layers, we introduce an adaptive attribute selection mechanism that efficiently evaluates network situations and identifies problematic areas. This mechanism prioritizes critical attributes by jointly considering their relevance to current network objectives and modeling complexity. At lower layers, these prioritized attributes and critical users are selectively incorporated into scalable network modeling. More detailed digital twins are then created to deliver targeted solutions for optimizing user association and power allocation. Additionally, we implement a multi-level synchronization mechanism to ensure temporal alignment among the digital twins, thereby enhancing the effectiveness of model-based orchestration. Extensive simulations validate the efficient identification of pressing operational issues and the effective orchestration of complex 6G networks.