Search Results (150)

The rapid economic and urban development in the Yellow River Delta Efficient Ecological Economic Zone (YRDEEZ) has intensified land use changes and aggravated ecological patch fragmentation. Constructing ecological networks (ENs) can reconnect fragmented patches and enhance ecosystem services. This study simulated land use patterns for 2040 under three scenarios: Natural Development (NDS), Ecological Protection (EPS), and Urban Development (UDS). Results indicated a consistent decline in agricultural land and an expansion of urban land across all scenarios, with the most pronounced urban growth under UDS (6.79%) and the largest ecological land area under EPS (5178.96 km²). Since 2000, the number of EN sources and corridors had decreased, with sources mainly concentrated along coastal areas. The source and corridor under UDS exhibited the highest area ratio (20.08%), while NDS showed the lowest (18.72%), with UDS demonstrating the strongest resilience. Through community detection, the UDS EN was divided into five ecological clusters, encompassing 127 intra-cluster corridors (2285.95 km) and 34 inter-cluster corridors (1171.32 km), among which the cluster near the Yellow River estuary was determined to be the most critical (Level 1). These findings will provide valuable insights for managing landscape fragmentation and biological habitat protection in YRDEEZ. Meanwhile, the multi-scenario simulations of ENs could play an important role in constructing ecological security patterns and protecting ecosystems. Full article

Ammopiptanthus mongolicus is a relict species from the ancient Mediterranean of the Tertiary period and the only strong xerophytic evergreen broad-leaved shrub in the central Asian desert. Foliar endophytic and epiphytic bacteria jointly form phyllosphere microorganisms that influence plant health. This study investigated the dynamic changes in foliar endophytic bacterial communities across four leaf growth stages (Young, Mature, Old1, and Old2). Illumina 16S region (V5–V7) amplicon sequencing was used to analyze community composition, function, construction process, and environmental driving factors. The Old1 and Old2 stages were clearly separated from the Young and Mature stages, which demonstrated closer clustering. Community diversity and evenness first increased from the Young to Mature stages, declined at the Old1 stage, and finally reached maximum values at the Old2 stage; richness increased gradually. Total amplicon sequence variant (ASV) numbers, stage-specific ASVs, and their proportion increased with leaf development, whereas the proportion of shared ASVs between adjacent, interval, and all stages decreased. Dominant genera were Rhodococcus (Young), unclassified_f__Comamonadaceae (Mature), Rhodococcus (Old1), and Bacillus (Old2). Co-occurrence networks became progressively simpler, with reduced inter-node and positive connectivity. Functional predictions revealed that chemoheterotrophy and aerobic chemoheterotrophy decreased initially and then increased, with the lowest values at Old1. N, C/P, N/P, and SOD reached maximum at the Old2 stage. P was maximum at the Mature stage. P, C/P, and N/P were significantly positively correlated with the Young stage, N with the Mature stage, and SOD with the Old2 stage (p < 0.05). These findings enhance understanding of the diversity, composition, function, and plant–endophyte relationships in xerophytic relict species, particularly evergreen desert shrubs. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

To address the challenges of improving energy balance and extending the operational lifetime of wireless sensor networks for Automated Railway Track Fastener Maintenance Robots (ARTFMR) along railways, this paper proposes an enhanced LEACH protocol incorporating Particle Swarm Optimization (PSO). Initially, network nodes are deployed, and their energy consumption is calculated to formulate a non-uniform deployment model aimed at improving energy balance, followed by network clustering. Subsequently, a routing protocol is designed, where the cluster head election mechanism integrates two critical factors—dynamic residual energy and distance to the base station—to facilitate dynamic and distributed cluster head rotation. During the communication phase, a Time Division Multiple Access (TDMA) scheduling mechanism is employed in conjunction with an inter-cluster multi-hop routing scheme. Additionally, a joint data-volume and energy optimization strategy is implemented to dynamically adjust the transmission data volume based on the residual energy of each node. Finally, simulations were conducted using MATLAB, and the results indicate that the proposed energy-balanced non-uniform deployment optimization strategy improves network energy utilization, effectively extends network lifetime, and exhibits favorable scalability. Full article

As supply chains expand in scale and the number of participating nodes increases, existing consensus algorithms are increasingly showing limitations in scalability, communication complexity, and handling complex network environments. To address the shortcomings of blockchain consensus mechanisms in master node selection, scalability, and communication complexity in supply chain traceability scenarios, this paper proposes a blockchain hybrid consensus optimization algorithm named Node Rating-Based and Grouping Raft cluster Practical Byzantine Fault Tolerance (NG-RPBFT) for supply chain traceability. This algorithm builds a multi-index comprehensive rating model for nodes to comprehensively evaluate consensus nodes, reasonably groups consensus nodes, adopts an inter-group and intra-group dual consensus mechanism to achieve efficient data synchronization, and introduces Brotli data compression technology to optimize message load, effectively enhancing system performance. Experimental results confirm that this algorithm significantly improves the scalability of the consensus mechanism and performs exceptionally well in consensus efficiency, making it suitable for complex application scenarios such as supply chain traceability under CPS scenarios. Full article

Nowadays, driven by the exponential growth of parameters and training data of AI applications and Large Language Models, a single GPU is no longer sufficient in terms of computing power and storage capacity. Building high-performance multi-GPU systems or a GPU cluster via vertical scaling (scale-up) has thus become an effective approach to break the bottleneck and has further emerged as a key research focus. Given that traditional inter-GPU communication technologies fail to meet the requirement of GPU interconnection in vertical scaling, a variety of high-performance inter-GPU communication protocols tailored for the scale-up domain have been proposed recently. Notably, due to the emerging nature of these demands and technologies, academic research in this field remains scarce, with limited deep participation from the academic community. Inspired by this trend, this article identifies the challenges and requirements of a scale-up network, analyzes the bottlenecks of traditional technologies like PCIe in a scale-up network, and surveys the emerging scale-up targeted technologies, including NVLink, OISA, UALink, SUE, and other X-Links. Then, an in-depth comparison and discussion is conducted, and we express our insights in protocol design and related technologies. We also highlight that existing emerging protocols and technologies still face limitations, with certain technical mechanisms requiring further exploration. Finally, this article presents future research directions and opportunities. As the first review article fully focusing on intra-node GPU interconnection in a scale-up network, this article aims to provide valuable insights and guidance for future research in this emerging field, and we hope to establish a foundation that will inspire and direct subsequent studies. Full article

The rapid expansion of satellite networks for advanced communication and space exploration has ensured that robust cybersecurity for inter-satellite links has become a critical challenge. Traditional security models rely on centralized trust authorities, and node-specific protections are no longer sufficient, particularly when system failures or attacks affect groups of satellites or agent clusters. To address this problem, we propose a blockchain-enabled decentralized zero-trust model based on post-quantum cryptography (BEDZTM-PQC) to improve the security of satellite communications via continuous authentication and anomaly detection. This model introduces a group-based security framework, where satellite teams operate under a zero-trust architecture (ZTA) enforced by blockchain smart contracts and threshold cryptographic mechanisms. Each group shares the responsibility for local anomaly detection and policy enforcement while maintaining decentralized coordination through hierarchical federated learning, allowing for collaborative model training without centralizing sensitive telemetry data. A post-quantum cryptography (PQC) algorithm is employed for future-proof communication and authentication protocols against quantum computing threats. Furthermore, the system enhances network reliability by incorporating redundant communication channels, consensus-based anomaly validation, and group trust scoring, thus eliminating single points of failure at both the node and team levels. The proposed BEDZTM-PQC is implemented in MATLAB, and its performance is evaluated using key metrics, including accuracy, latency, security robustness, trust management, anomaly detection accuracy, performance scalability, and security rate with respect to different numbers of input satellite users. Full article

High-fluoride hot springs serve as a natural laboratory for investigating microbial adaptation and variations in community structure under extreme environments. This study utilized water chemistry analysis and 16S rRNA gene sequencing to investigate the correlation between high-fluoride hot springs and microbial community structure and diversity. The results show that the five hot springs exhibited an average F⁻ content of 15.04 mg/L, with weakly alkaline pH, high total dissolved solids, and Na⁺ as the dominant cation. The hydrochemical type was classified as HCO₃⋅SO₄-Na, consistent with the chemical characteristics of high-fluorine water. Microbial abundance and diversity were significantly reduced in the hot springs as compared to the surface water and groundwater samples. The dominant phyla in the study area included Pseudomonadota, Cyanobacteriota, Bacteroidota, and Actinomycetota. The genus-level composition varied significantly across samples, with no dominant genus observed universally. The specific genera present in different samples exhibit unique functional attributes, such as Tepidimonas, Rhodobacter, Hyphomonas, Parvibaculum, Polynucleobacter and Limnohabitans. Cluster analysis confirmed that dissimilarity coefficients highlight the significant influence of microbial abundance on inter-sample differences among hot springs. Redundancy analysis of the top 11 phyla by abundance in water samples revealed that the presence of F⁻ exerts inhibitory effects on microbial growth. Full article

In multi-UAV cooperative tasks, dynamic communication topologies and resource heterogeneity present significant challenges for distributed task allocation, leading to high communication overhead and poor task-resource matching, which in turn increases computational costs. While the Consensus-Based Bundle Algorithm (CBBA) offers a robust decentralized framework, its scalability and adaptability in heterogeneous, large-scale scenarios are limited. To overcome these issues, this paper introduces a novel Two-Level Clustered CBBA (TLC-CBBA). In the first-layer clustering, UAVs are grouped based on communication topology using graph-theoretic centrality measures to rank node importance, followed by clustering based on shortest-path distances to minimize communication costs. In the second-layer clustering, a resource-balanced and distance-aware K-medoids algorithm is applied within each subgroup obtained from the first-layer clustering, taking into account UAV resource heterogeneity and spatial proximity. This method ensures spatial compactness among UAVs within each subgroup while achieving a more balanced distribution of total resources across clusters. Finally, after completing the two-level clustering, each subgroup executes CBBA for local task bundling and consensus, while the cluster centers coordinate inter-cluster communication to guarantee globally consistent and conflict-free task allocation. Simulations across diverse mission scenarios and UAV team sizes demonstrate that TLC-CBBA substantially outperforms CBBA and its variants (DMCHBA, G-CBBA, and Clustering-CBBA) in terms of communication efficiency, total task score, runtime, and significance analysis. The proposed TLC-CBBA demonstrates strong robustness and scalability for heterogeneous multi-UAV task allocation in dynamic environments. Full article

The Hamiltonian Monte Carlo (HMC) method is effective for Bayesian inference but suffers from synchronization overhead in distributed settings. We propose two variants: a distributed HMC (DHMC) baseline with synchronized, globally exact gradient evaluations and a communication-avoiding leapfrog HMC (CALF-HMC) method that interleaves local surrogate micro-steps with a single–global Metropolis–Hastings correction per trajectory. Implemented on Apache Spark/PySpark and evaluated on a large synthetic logistic regression ($N={10}^7$, $d=100$, workers $J\in \{4,8,16,32\}$), DHMC attained an average acceptance of $0.986$, mean ESS of 1200, and wall-clock of $64.1$ s per evaluation run, yielding $\approx 18.7$ ESS/s; CALF-HMC achieved an acceptance of $0.942$, mean ESS of $5.1$, and $14.8$ s, i.e., ≈0.34 ESS/s under the tested surrogate configuration. While DHMC delivered higher ESS/s due to robust mixing under conservative integration, CALF-HMC reduced the per-trajectory runtime and exhibited more favorable scaling as inter-worker latency increased. The study contributes (i) a systems-oriented communication cost model for distributed HMC, (ii) an exact, communication-avoiding leapfrog variant, and (iii) practical guidance for ESS/s-optimized tuning on clusters. Full article

The deployment of Federated Learning (FL) in edge environments is often impeded by system heterogeneity, non-independent and identically distributed (non-IID) data, and constrained communication resources, which collectively hinder training efficiency and scalability. To address these challenges, this paper presents FLEX-SFL, a flexible and efficient split federated learning framework that jointly optimizes model partitioning, client selection, and communication scheduling. FLEX-SFL incorporates three coordinated mechanisms: a device-aware adaptive segmentation strategy that dynamically adjusts model partition points based on client computational capacity to mitigate straggler effects; an entropy-driven client selection algorithm that promotes data representativeness by leveraging label distribution entropy; and a hierarchical local asynchronous aggregation scheme that enables asynchronous intra-cluster and inter-cluster model updates to improve training throughput and reduce communication latency. We theoretically establish the convergence properties of FLEX-SFL under convex settings and analyze the influence of local update frequency and client participation on convergence bounds. Extensive experiments on benchmark datasets including FMNIST, CIFAR-10, and CIFAR-100 demonstrate that FLEX-SFL consistently outperforms state-of-the-art FL and split FL baselines in terms of model accuracy, convergence speed, and resource efficiency, particularly under high degrees of statistical and system heterogeneity. These results validate the effectiveness and practicality of FLEX-SFL for real-world edge intelligent systems. Full article

Information announcement is the process of propagating and synchronizing the information of Computing Resource Nodes (CRNs) within the system of the Computing Networks. Accurate and timely acquisition of information is crucial to ensuring the efficiency and quality of subsequent task scheduling. However, existing announcement mechanisms primarily focus on reducing communication overhead, often neglecting the direct impact of information freshness on scheduling accuracy and service quality. To address this issue, this paper proposes a hierarchical and clustering-based announcement mechanism for the wide-area Computing Networks. The mechanism first categorizes the Computing Network Nodes (CNNs) into different layers based on the type of CRNs they interconnect to, and a top-down cross-layer announcement strategy is introduced during this process; within each layer, CNNs are further divided into several domains according to the round-trip time (RTT) to each other; and in each domain, inspired by the “Six Degrees of Separation” concept from social propagation, a RTT-aware fast clustering algorithm canopy is employed to partition CNNs into multiple overlap clusters. Intra-cluster announcements are modeled as a Traveling Salesman Problem (TSP) and optimized to accelerate updates, while inter-cluster propagation leverages overlapping nodes for global dissemination. Experimental results demonstrate that, by exploiting shortest path optimization within clusters and overlapping-node-based inter-cluster transmission, the mechanism is significantly superior to the comparison scheme in key indicators such as convergence time, Age of Information (AoI), and communication data volume per hop. The mechanism exhibits strong scalability and adaptability in large-scale network environments, providing robust support for efficient and rapid information synchronization in the Computing Networks. Full article

The power system’s dynamic frequency stability is affected by common-mode ultra-low-frequency oscillation and differential-mode low-frequency oscillation. Traditional frequency control based on generators is facing the problem of capacity reduction. It is urgent to explore new regulation resources such as photovoltaics. To address this issue, this paper proposes a distributed active support method based on photovoltaic systems via state–disturbance observation and dynamic surface consensus control. A three-layer distributed control framework is constructed to suppress low-frequency oscillations and ultra-low-frequency oscillations. To solve the high-order problem of the regional grid model and to obtain its unmeasurable variables, a regional observer estimating both system states and external disturbances is designed. Furthermore, a distributed dynamic frequency stability control method is proposed for wide-area photovoltaic clusters based on the dynamic surface control theory. In addition, the stability of the proposed distributed active support method has been proven. Moreover, a parameter tuning algorithm is proposed based on improved chaos game theory. Finally, simulation results demonstrate that, even under a 0–2.5 s time-varying communication delay, the proposed method can restrict the frequency deviation and the inter-area frequency difference index to 0.17 Hz and 0.014, respectively. Moreover, under weak communication conditions, the controller can also maintain dynamic frequency stability. Compared with centralized control and decentralized control, the proposed method reduces the frequency deviation by 26.1% and 17.1%, respectively, and shortens the settling time by 76.3% and 42.9%, respectively. The proposed method can effectively maintain dynamic frequency stability using photovoltaics, demonstrating excellent application potential in renewable-rich power systems. Full article

Over the past decade, many software enterprises have migrated from monolithic to microservice architectures to enhance scalability, maintainability, and performance. However, this transition presents significant challenges, requiring considerable development efforts, research, customization, and resource allocation over extended periods. Furthermore, the success of migration is not guaranteed, highlighting the complexities organizations face in modernizing their software systems. To address these challenges, this study introduces Mono2Micro, a comprehensive framework designed to automate the migration process while preserving structural integrity and optimizing service boundaries. The framework focuses on three core patterns: database patterns, service decomposition, and communication patterns. It leverages machine learning algorithms, including Random Forest and Louvain clustering, to analyze database query patterns along with static and dynamic database model analysis, which enables the identification of relationships between models, facilitating the systematic decomposition of microservices while ensuring efficient inter-service communication. To validate its effectiveness, Mono2Micro was applied to a student information system for faculty management, demonstrating its ability to streamline the migration process while maintaining functional integrity. The proposed framework offers a systematic and scalable solution for organizations and researchers seeking efficient migration from monolithic systems to microservices. Full article

The proliferation of wireless sensor networks in remote and inaccessible areas demands efficient data collection approaches that minimize energy consumption while ensuring comprehensive coverage. Traditional data retrieval methods face significant challenges when sensors are sparsely distributed across extensive areas, particularly in scenarios where direct sensor access is impractical due to terrain constraints or operational limitations. This research addresses these challenges through a novel hybrid optimization framework that combines integer linear programming (ILP) with multiple traveling salesperson problem (mTSP) algorithms for drone-based data collection in wireless sensor networks (WSNs). The methodology employs a two-phase approach, where ILP optimally determines strategic access point locations for sensor clustering based on communication capabilities, followed by mTSP optimization to generate efficient inter-AP flight trajectories rather than individual sensor visits. Comprehensive simulations across diverse network configurations and drone quantities demonstrate consistent performance improvements, with travel distance reductions reaching 32% compared to conventional mTSP implementations. Comparative evaluation against established clustering algorithms including Voronoi, DBSCAN, Constrained K-Means, Graph-Based clustering, and Greedy Circle Packing confirms that ILP consistently achieves optimal access point allocation while maintaining superior routing efficiency. Additionally, a novel quality assessment metric quantifies sensor grouping effectiveness, revealing that ILP-based clustering advantages become increasingly pronounced with higher sensor densities, providing substantial operational benefits for large-scale wireless sensor network deployments. Full article