Federated Learning (FL) in the Internet of Vehicles (IoV) faces a trade-off between the inefficiency of synchronous methods due to stragglers and the performance degradation of asynchronous methods from gradient staleness and security risks. To resolve this trade-off, we propose Semi-Asynchronous Federated Learning with Adaptive Thresholds (SAFLT), a framework with a novel adaptive aggregation mechanism. The core of SAFLT is a Proximal Policy Optimization (PPO) agent that learns to dynamically control the aggregation threshold by jointly considering real-time network conditions, data quality heterogeneity, and client computational performance. To further enhance performance, the system integrates gradient alignment to mitigate non-IID data and Top-k compression to reduce communication overhead. For security, SAFLT introduces Proof-of-Dynamic-Contribution (PoDC), a lightweight blockchain-based consensus protocol that offloads validation overhead to trusted edge infrastructure and ensures the integrity of model updates. Extensive evaluations demonstrate that SAFLT significantly outperforms state-of-the-art baselines, improving model accuracy by up to 10% compared to architecturally similar methods on the complex VeReMi dataset while maintaining robustness against both poisoning and severe DDoS attacks.

The utilization of Named Data Network (NDN) in Vehicle Autonomous Network (VANET) has emerged as a prominent research area, aiming to enhance data transmission and distribution. However, effectively addressing the mobility issues in NDN-based VANET poses significant challenges. Existing Q-learning-based geographic routing methods suffer from slow convergence and heavy reliance on dynamic Q-value tables. Moreover, local information-based next-hop selection does not always prioritize optimal global routing forwarding. To overcome these limitations, this study presents a novel routing strategy for in-vehicle named data networks, employing the Deep Prioritized Sarsa algorithm. The proposed approach incorporates fuzzy logic techniques and depth-first search algorithms to obtain and utilize comprehensive global road information. Additionally, it maintains an adaptive fixed-size Q-value table with a customized reward function for vehicle node selection. Simulation results showcase substantial improvements in terms of packet hit rate and end-to-end delay. This research contributes to the advancement of efficient and effective routing strategies for NDN-based VANET, addressing the challenges associated with mobility and data transmission in vehicular networks.

Vehicular Ad Hoc Networks (VANETs), with their rapid deployment and highly dynamic characteristics, have become an essential component of intelligent transportation systems. Efficient and secure communication between vehicles relies on VANET technology, with identity authentication serving as the core safeguard for secure vehicle communications. However, traditional authentication methods typically employ a centralized architecture, which may introduce risks of single points of failure and require road-side units (RSUs) to manage vast computational and storage resources to handle high-speed, large-scale vehicle communications, increasing system maintenance costs and communication delays. For these issues, this paper proposes a blockchain-based distributed vehicle batch authentication scheme. Initially, we designed a highly secure and efficiency-optimized elliptic curve digital signature algorithm (ECDSA) that eliminates the modular inversion operations in the message signing and verification process, and also supports batch verification, therefore, significantly enhancing the efficiency and speed of ECDSA while ensuring security, thus reduces the burden on RSUs when dealing with high-volume vehicle validation. Secondly, to ensure the anonymity and traceability of vehicles, we propose a rapid vehicle revocation mechanism based on pseudonyms that does not rely on a Trusted Authority (TA), speeding up the revocation process and enhancing the system’s flexibility and security. Furthermore, a decoupled architecture is introduced to support seamless cross-domain roaming without bottlenecking real-time communications. This paper analyzes the scheme against a variety of known attacks and conducts formal security validation using the Scyther tool. Empirical validations, including physical hardware prototyping and PBFT consensus simulations, further prove that the protocol is robust and practically deployable. Through detailed comparisons, the proposed scheme shows superior performance in computational overhead, communication delay, and energy consumption.

The Internet of Vehicles (IoV) relies on seamless cross-domain data sharing to enable cooperative perception, real-time traffic management, and inter-regional service handover. However, challenges such as cross-regional ledger inconsistency, verification delays in resource-based edge nodes, and lack of flexible scheduling under dynamic workloads hinder data sharing. In order to solve these problems, we propose an integrated framework for asynchronous validation and adaptive scheduling. First, we proposed a dual-layer blockchain architecture, in which the regional shard chain achieves efficient intra-domain consensus, and the backbone chain ensures the consistency of cross-domain ledgers. Second, we design a cross-layer asynchronous verification (CLAV) mechanism to significantly reduce the verification delay by decoupling the local and global verification processes. CLAV also maintains high security with minimal computing overhead by combining lightweight elliptic curve cryptography (ECC) and smart contracts. Finally, we deploy a scheduler based on reinforcement learning in the urban data center (UDC) to realize the dynamic allocation of tasks and resources. Experimental evaluation shows that compared with the baseline method, our method can reduce the verification delay by up to 50%, increase the throughput by 15%, and reduce the average task delay by 20%. These improvements make it possible to have a scalable and secure Internet of Vehicles system for the smart city environment.

The Internet of Vehicles (IoV) collects real-time data on traffic, environmental conditions, and vehicle behavior through vehicle interconnection and interaction with infrastructure, providing support for the of Machine Learning (ML) in intelligent decision-making. However, centralized learning approaches suffer from issues like privacy leakage and high communication costs. Federated Learning (FL) addresses these issues by sharing local model updates, but in IoV environments, challenges such as data heterogeneity result in slow convergence, limited communication resources, and security threats like gradient leakage. To tackle these challenges, this paper proposes Adaptive Blockchain-based Hierarchical Federated Learning with Gradient Alignment (ABHFL). ABHFL groups vehicle nodes and RSUs into a hierarchical structure to perform local training, gradient alignment, and model aggregation at different levels. The proposed Adaptive Gradient Alignment (AGA) mechanism aligns the update directions of nodes towards the global optimal direction through multiple rounds of alignment after local gradient computation, accelerating model convergence and ensuring that the gradients uploaded contribute positively to global optimization. In addition, a lightweight Proof-of-Gradient-Alignment (PoGA) consensus mechanism is designed, which performs two-stage verification of the uploaded gradients and integrates reputation scores and blockchain storage to guarantee gradient reliability and protect against attacks. Extensive experiments demonstrate that ABHFL significantly improves model convergence, communication efficiency, and security reliability, providing an effective and robust solution for FL in IoV scenarios.

With the increasing reliance on cloud services, establishing secure and reliable authentication for terminal devices to remotely access and control data has become a critical challenge. Existing solutions often suffer from limitations such as concentrated computational load, privacy infringements, and dependence on centralized architectures that introduce single points of failure (SPOF). To address these issues, this paper proposes DTAP, a blockchain-based dual-terminal collaborative anonymous authentication protocol. DTAP introduces a novel architecture that splits the user terminal into a U-Shield and a smartphone: the U-Shield securely hosts the master key offline, providing a robust security anchor, while the smartphone leverages BBS+ signatures and zero-knowledge proofs to achieve privacy-preserving authentication and Fine-Grained Access Control (FGAC). Furthermore, the protocol employs blockchain technology to eliminate SPOF, with smart contracts ensuring tamper-proof logging and transactional transparency. Security analysis confirms that the protocol meets the required security properties. Performance evaluation shows that DTAP maintains competitive computational and communication efficiency despite its enhanced functionality, and its deployment on the Ethereum testnet demonstrates practical feasibility.

Network scanning is crucial for both network management and cybersecurity. However, due to the vast address space of IPv6, brute-force scanning is infeasible. Seed-based target generation algorithms have recently attracted considerable research attention. However, existing target generation algorithms lack a deeper exploration of patterns, leading to poor capture of dense regions and consequently low hitrate. To address this issue, we propose 6Global, a dynamic IPv6 active address scanning method assisted by global perspective. 6Global first performs rapid clustering of seed addresses based on their descriptive attributes. Then, for each cluster, patterns are generated in a bottom-up manner based on entropy, using subranges to represent patterns and resulting in denser patterns. Finally, dynamic scanning is conducted using these patterns. During scanning, the reward of each pattern is dynamically adjusted based on its active density and global statistics, which enhances the capability in capturing dense regions. Experimental results on six seed datasets show that 6Global overall outperforms seven baseline methods and demonstrates significant advantages across multiple datasets.

The rapid expansion of mobile communication networks has led to a surge in cellular traffic, highlighting the need for advanced prediction models to improve network performance. Federated learning (FL) offers a promising solution by enabling distributed model training across multiple nodes, aligning well with the decentralized nature of modern networks. However, applying FL to spatiotemporal cellular traffic prediction is challenging due to the substantial communication overhead in distributed learning. To address this, we propose LFedSG, a lightweight FL framework incorporating subgraph partitioning for spatiotemporal traffic prediction. LFedSG supports collaborative training while preserving inter-client dependencies critical for accurate prediction. Communication efficiency is achieved by focusing on essential model parameters, while subgraph partitioning and spatiotemporal graph convolutional networks (STGCN) enhance spatial and temporal correlation modeling. An adaptive transmission weight pruning strategy further reduces communication and computation costs. Extensive experiments on the Telecom Italia and Pems07 datasets demonstrate that LFedSG achieves higher predictive accuracy than traditional methods, with significant reductions in communication overhead and training time, validating its effectiveness and scalability for large-scale mobile network environments.

The Wulonggou gold district in the Eastern Kunlun region of Qinghai Province, China, hosts several gold deposits, yet the origin and nature of the ore-forming fluids remain debated. This study focuses on the Shenshuitan deposit in the Wulonggou gold district, integrating field observations with petrographic and geochemical analyses to constrain its genesis. Geological observations indicate that the deposit preserves three types alteration (skarn, propylitic and phyllic alteration). LA-ICP-MS U-Pb dating of hydrothermal garnet and magmatic zircon from the mineralization-related quartz diorite porphyry yielded ages of 220 +/- 2 Ma and 218 +/- 2 Ma, respectively. These overlapping ages indicate a close temporal relationship between magmatism and gold mineralization during the Late Triassic. The close spatial and temporal association among porphyry intrusion, alteration, and mineralization points to a magmatic-hydrothermal origin for the Shenshuitan deposit. The quartz diorite porphyry exhibits adakitic geochemical signatures and epsilon Hf(t) values from -6 to -0.9, with two-stage model ages (TDM2) ranging from 0.89 to 1.09 Ga. These features suggest the magma was derived from the partial melting of thickened, juvenile lower crust of Mesoproterozoic age. This magmatic event likely occurred in the postcollisional extensional setting following the closure of the Paleo-Tethys Ocean.

Blockchain, as a distributed ledger, inherently possesses tamper-resistant capabilities, creating a natural channel for covert communication. However, the immutable nature of data storage might introduce challenges to communication security. This study introduces a blockchain-based covert communication model utilizing dynamic Base-K encoding. The proposed encoding scheme utilizes the input address sequence to determine K to encode the secret message and determines the order of transactions based on K, thus ensuring effective concealment of the message. The dynamic encoding parameters enhance flexibility and address issues related to identical transaction amounts for the same secret message. Experimental results demonstrate that the proposed method maintains smooth communication and low susceptibility to tampering, achieving commendable concealment and embedding rates.

Vehicular Ad-hoc Networks (VANETs) face significant security and privacy challenges in modern intelligent transportation systems. This paper analyzes vulnerabilities in Al-Shareeda et al.'s vehicle authentication protocol (doi: 10.1109/TDSC.2025.3553868) and proposes an enhanced ECC-based scheme using short-lived pseudonymous certificates. We identify two critical weaknesses in Al-Shareeda et al.'s protocol-a desynchronization attack causing potential denial-of-service and an identity linking attack compromising vehicle privacy. Our protocol establishes mutual authentication between vehicles and roadside units, ensuring message integrity, anonymity, and perfect forward secrecy. Unlike existing approaches, it eliminates the need for online third-party authenticators. Formal security proofs demonstrate that the scheme's security is reducible to the hardness of the ECDLP and CDH problems. Performance analysis shows our approach achieves an optimal security-efficiency balance with competitive communication overhead (4608 bits) and computation costs (5.02 ms) compared to state-of-the-art alternatives, while uniquely satisfying all twelve evaluated security properties.

With the rapid advancement of Internet of Vehicles (IoV) technologies, traditional authentication mechanisms have exhibited diminishing efficacy. They struggle to meet strict requirements for security and performance, especially in large-scale, heterogeneous IoV environments. These conventional approaches typically suffer from high authentication delays, limited scalability, and insufficient resistance to security threats. To address these limitations, this paper proposes PBAS — a Blockchain-Assisted Priority-Based Batch Authentication Scheme that aims to enhance both authentication responsiveness and security guarantees in dynamic vehicular networks.PBAS introduces a hierarchical authentication framework capable of distinguishing among multiple role levels, employing end-to-end identity verification to ensure compatibility and mutual trust among heterogeneous nodes. Furthermore, by integrating environment-aware data, PBAS enables an adaptive authentication strategy that dynamically responds to fluctuating network conditions. The proposed architecture combines off-chain pre-authentication with on-chain grouped consensus verification in a dual-layered structure. This design significantly reduces authentication delay and computational overhead while improving scalability and resilience against network-based threats. Formal security analysis and theorem-based proofs demonstrate that PBAS can withstand various common attacks, including replay, impersonation, and collusion. Experimental results indicate that PBAS achieves substantial improvements in communication efficiency, authentication throughput, and system robustness. These advantages render it particularly suitable for high-density, role-diverse, and fast-changing vehicular environments, highlighting its strong potential for real-world deployment.

This study focuses on the increasingly critical privacy requirements of sensitive real-number data in modern and practical multiparty applications such as collaborative data analysis,privacy-preserving machine learning,and secure financial computations.It proposes a novel and efficient scheme for fixed-point privacy-preserving computation based on integer homomorphic encryption.Specifically,the scheme is designed to ensure both accuracy and security in scenarios where real-number data must be shared and processed among distributed parties without revealing private values.The proposed scheme works by first converting real-number data,typically represented in signed fixed-point formats,into integers through a domain translation mechanism.This translation maps real numbers into integer spaces while preserving the relative magnitude and sign information.Once this translation is performed,the scheme applies multiparty fully homomorphic encryption over integers to perform the necessary computations on the encrypted data.This approach enables secure and collaborative computation without exposing raw numerical values,making it suitable for privacy-sensitive scenarios such as healthcare or finance.A technical challenge in fixed-point computation is the problem of decimal point drift,which arises because arithmetic multiplication operations can cause uncontrolled expansion or contraction of decimal digits.To address this issue,this paper introduces a new algorithm for random decimal digit generation,which injects controlled errors to maintain semantic security,as well as an algorithm for decimal digit truncation,which ensures that results remain bounded and interpretable after computation.These algorithms are accompanied by rigorous theoretical analysis,including correctness proofs and a formal demonstration of their security properties.More importantly,this study defines and proves a new security concept called INDistinguishability under Chosen-Plaintext Attacks for TRUNCated ciphertexts(IND-CPA-TRUNC).This security definition extends the classical IND-CPA model by incorporating the characteristics of fixed-point truncation operations,which are essential for practical homomorphic computations of real-valued data.Furthermore,it is proven that under the assumption of an underlying multiparty integer fully homomorphic encryption scheme that satisfies IND-CPA security,the proposed scheme guarantees privacy in the semi-honest adversarial model.The proposed method is also scalable and efficient.For a system with n participants,the communication and computational complexities remains at O(n2)and O(n3),respectively,regardless of the decimal place length,which is denoted as γ.This is in contrast to the widely cited scheme by Catrina et al.,in which both complexities scaled linearly with γ,resulting in O(n2γ)communication and O(n3γ)computation.Therefore,the proposed scheme exhibits constant complexity with respect to γ,making it particularly suitable for applications that require high numerical accuracy.The study includes comprehensive experimental validation,demonstrating that the proposed scheme not only achieves higher computational efficiency but also offers better practical performance in real-world settings.This empirical evidence confirms that the scheme can be implemented feasibly in multiparty systems without significant performance degradation.In conclusion,this study makes several key contributions to the field of secure multiparty computation by combining integer homomorphic encryption with novel fixed-point arithmetic techniques and by establishing provable security guarantees.This offers a significant step forward for privacy-preserving real-number computations,particularly in settings where performance and scalability are crucial.

Vehicular named data networking (VNDN) is a network architecture that supports various content-oriented applications in high-dynamic topology environments. Its inherent in-network caching helps with content delivery and communication efficiency in vehicular networks. Due to the variability in vehicle mobility, the change of relay nodes makes data packet transmission difficult. Additionally, frequent link interruptions and packet retransmissions lead to increased network load and reduced quality of service for users. To address these issues, this article proposes a novel vehicle clustering-based cooperative caching strategy. The strategy first utilizes recurrent neural networks (RNN) to predict vehicle connectivity and clusters vehicles into different groups based on these predictions. Concurrently, we construct a popularity prediction model based on the multihead attention mechanism to forecast content trends. Furthermore, we introduce the concept of importance levels, allowing nodes of different levels to cache the content of corresponding popularity levels. Performance evaluation results show that the proposed scheme outperforms the existing strategies in terms of cache hit rate, latency, and link load.

Ethereum, as one of the most active cryptocurrency trading platforms, has garnered significant academic interest due to its transparent and accessible transaction data. In recent years, phishing scams have emerged as a serious criminal activity on Ethereum. Although most studies model Ethereum account transactions as networks and analyze them using traditional machine learning or network representation learning techniques, these approaches often rely solely on the latest static transaction records or use manually designed features while neglecting transaction histories, thus failing to fully capture the dynamic interactions and potential trading patterns between accounts. This article introduces an innovative multiperspective cascaded dynamic graph neural network model named DMPCG, which extracts phishing transaction data from authoritative databases like blockchain explorers to construct transaction network graphs. The model elevates the analysis from the microscopic features of nodes to the macroscopic dynamics of the entire network, integrating the attributes of static snapshot graphs with the evolution of dynamic trading networks, significantly enhancing the accuracy of phishing detection. Experimental results demonstrate that the DMPCG method achieves an impressive precision of 92.6% and an F1-score of 90.9%, outperforming existing baseline models and traditional subgraph sampling techniques.

Knowledge graph is a structured knowledge representation method that can be used to integrate knowledge in the field of echinococcosis, providing strong support for the prevention and treatment of echinococcosis. Echinococcosis, a zoonotic disease, is caused by the larval stage of tapeworms. It is of significant importance in terms of prevention, control strategies, and reducing the impact of the disease. In recent years, the study of RNA, genes, proteins, and therapeutic drugs related to echinococcosis has been undertaken by researchers. However, this research information has been widely disseminated across various repositories, dispersed data makes it difficult for researchers to understand the research status of echinococcosis. To address this issue, the EKGEA model has been proposed, which incorporates entity alignment technology into the field of echinococcosis to facilitate the fusion of echinococcosis knowledge graphs. A large amount of echinococcosis information is collected from multiple sources, including attribute information and relationship information. Based on these two types of data, two different language knowledge graphs for echinococcosis have been constructed. Additionally, the EKGEA model utilizes BioBert to construct attribute embeddings and makes use of BiLSTM to capture relation embeddings. Through graph alignment, a more complete echinococcosis knowledge graph containing English information can be obtained. Compared with four state-of-the-art entity alignment algorithms, EKGEA demonstrates superior performance on the echinococcosis dataset. The information of the knowledge graph can be obtained from the Echinococcosis Database (EchiDB).

The Yulekenhalasu porphyry Cu-Mo deposit (0.2 Mt Cu at 1.04 wt% and 0.012 Mt Mo at 0.06 wt%) is located in the Devonian Halasu copper belt, East Junggar block, northwest China. At Yulekenhalasu, Cu and Mo mineralization commonly occurs as disseminated sulfides or veinlets in porphyry-related alteration zones. Five alteration stages have been identified, including porphyry-type alteration, i.e., sodic-calcic (stage I), potassic (stage IIa), propylitic (stage IIb), and phyllic (stage III) alteration, and widespread late Cu sulfide-bearing veins (stage IV) cross-cutting porphyry-type alteration, plus a post-ore fault-controlled argillic alteration (stage V). Stages IV and V have overprinted porphyry-type alteration (stages I-III). Anomalous concentrations of trace elements in stage IIb pyrite (e.g., Ti, Zr, Gd, and Hf) are due to the presence of micro-inclusions (e.g., zircon and rutile) in the low-temperature (similar to 200 degrees C) propylitic zone. Cu, Ag, Co, and Mn, occurring as stoichiometric substitutions or as tetrahedrite inclusions in overprinting stage IV pyrite, were sourced directly from the primary hydrothermal fluid. The enrichment of distal pathfinder elements (e.g., Cr, Au, and Tl) in overprinting stage V pyrite was caused by a low-temperature (similar to 160 degrees C) hydrothermal event related to regional orogenic Au mineralization. The spatial variation of Se/S in pyrite among various paragenetic stages were influenced by changes in the hydrothermal fluid composition and temperature, with the latter having the effect of decreasing pyrite Se/S. Lower Se concentrations in pyrite of stages IIb and III close to the orebody are explained by relatively higher temperatures in the locus of mineralization. This may provide a potential vectoring tool to mineralization using pyrite geochemistry in porphyry deposits. Systematic thermodynamic calibrations were applied to pyrite compositions to fingerprint the corresponding Se/S and Co/Ni ratios of fluids and further to develop a complete metallogenic model for Yulekenhalasu. The Devonian diorite porphyry generated fluids that produced the early porphyry-type alteration. High Co/Fe (average similar to 1 x 10(-4)) and Ni/Fe (average similar to 3 x 10(-6)) ratios of fluid for late Cu sulfide-bearing veins, combined with higher Se/S (average similar to 6 x 10(-7)) than orogenic Au deposits (average similar to 3 x 10(-8)), indicate that the fluids possibly derived from a Late Devonian-Carboniferous mafic intrusion. Argillic alteration assemblages forming at ca. 280 Ma host pyrite relatively enriched in Au (average 0.1 ppm, with native gold inclusions). Therefore it is likely related to a regional orogenic gold mineralizing event in the Early Permian that overprinted Devonian mineralization. Although spatially contiguous, hydrothermal alteration and hypogene mineralizing stages identified herein represent discrete episodes of hydrothermal activities at Yulekenhalasu. The multi-stage alteration features observed at Yulekenhalasu may provide insights into the complete evolutionary history of Paleozoic porphyry Cu deposit systems in the Central Asian orogenic belt. This study contributes to a better understanding of the metallogenic and exploration models of porphyry Cu deposits overprinted by multi-stage hydrothermal events, which is economically important in Phanerozoic orogenic belts.

The Kateba'asu gold deposit, situated in the Western Tianshan of China, is one of the most significant discoveries in the world-class Tianshan gold belt. The deposit features two distinct mineralization styles. The early, subordinate skarn-type copper-gold mineralization occurs in the contact zone between monzogranite, diorite, and Silurian limestone, composed of garnet, diopside, epidote, chalcopyrite, pyrite, and gold. The later, primary lode- gold mineralization is hosted in the altered monzogranite characterized by pervasive quartz-pyrite-sericitechlorite-K-feldspar alteration and a well-develped veining systems. Pyrite is the dominant sulfide mineral related to gold mineralization in the Kateba'asu gold deposit, with four types identified: Py0 from the early skarn copper-gold mineralization, and Py1 to Py3 from the later lode-gold mineralization. All types of pyrite are homogeneous and contain very low levels of lattice-bound gold. Py0 is euhedral and fine-grained, with relatively high Cu, Au, Co, and Ni contents, and displays a magmatic sulfur isotopic signature with S34S ranging from 0.8 to 4.3 %o . Py1 occurs as euhedral to subhedral, coarse-grained crystals within pyrite-quartz veins with higher concentrations of Co and Ni. Py2, which develops in the quartz-pyrite veins, is medium to coarse-grained and contains elevated levels of As, Cu, Zn, and Bi relative to Py1. Py3, found in polymetallic sulfide veins of the main lode-gold stage, is anhedral and medium to fine-grained with higher contents of As, Ag, Cu, Zn, Se, Te and lowest Co and Ni concentrations compared to Py1 and Py2. The positive correlations between Au-Te, Au-Bi, Au-Cu, and Pb-Bi across all pyrite types, along with the presence of visible gold in Py3, indicate that most gold occurs as micro-/nano-sized inclusions and as fissure gold. The S34S values of Py1, Py2, and Py3 (7.6 to 11.8 %o , 10.1 to 12.6 %o , and 9.8 to 12.4 %o , respectively) were attributed to an initial magmatic source and mixed with external sulfur subsequently from the wall rocks. The H and O isotopic compositions (SDH2O =-84.1 to-93.5 %o ; S 18 O H2O = 1.8 to 6.6 %o ) of quartz from the lode-gold mineralization imply that ore-forming fluids were predominantly of magmatic origin, with a additional contribution from meteoric water. Taken together, a two-episode mineralization model was proposed for the formation of the Kateba'asu gold deposit. The early skarn mineralization stage is associated with the emplacement of diorite during the Early Carboniferous. In contrast, the subsequent lode-gold mineralization, occurring between the Late Carboniferous and Permian periods, represents a overprinted magmatic-hydrothermal gold system potentially linked to a deep-seated magmatic intrusion.