Search Results (4,623)

Blockchain sharding has emerged as a promising solution to address scalability and performance challenges in distributed ledger systems. In the sharded blockchain, yanking can reduce the communication overhead of smart contracts between shards. However, the existing smart contract yanking methods are inefficient, increasing the latency and reducing the throughput. In this paper, we propose a novel DRL-Based Cross-Shard Smart Contract Yanking (DCSCY) framework which intelligently balances three critical factors: the number of smart contracts processed, node waiting time, and yanking costs. The proposed framework dynamically optimizes the relocation trajectory of smart contracts across shards. This reduces the communication overhead and enables adaptive, function-level migrations to enhance the execution efficiency. The experimental results demonstrate that the proposed approach reduces the cross-shard transaction latency and enhances smart contract utilization. Compared to random-based and order-based methods, the DCSCY approach achieves a performance improvement of more than 95%. Full article

Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting brand development. This study proposes a comprehensive traceability system tailored to the full lifecycle of Hymenopellis raphanipes, addressing the operational needs of producers and regulators alike. Through detailed analysis of the entire supply chain, from raw material intake, cultivation, and processing to logistics and sales, the system defines standardized traceability granularity and a unique hierarchical coding scheme. A multi-layered system architecture is designed, comprising a data acquisition layer, network transmission layer, storage management layer, service orchestration layer, business logic layer, and user interaction layer, ensuring modularity, scalability, and maintainability. To address performance bottlenecks in traditional systems, a multi-chain collaborative traceability model is introduced, integrating a mainchain–sidechain storage mechanism with an on-chain/off-chain hybrid management strategy. This approach effectively mitigates storage overhead and enhances response efficiency. Furthermore, data integrity is verified through hash-based validation, supporting high-throughput queries and reliable traceability. Experimental results from its real-world deployment demonstrate that the proposed system significantly outperforms traditional single-chain models in terms of query latency and throughput. The solution enhances data transparency and regulatory efficiency, promotes sustainable practices in green agricultural production, and offers a scalable reference model for the traceability of other high-value agricultural products. Full article

Unmanned Aerial Vehicles (UAVs) face collision risks due to Beyond Visual Line of Sight operations. Therefore, UAV Traffic Management (UTM) systems are used to manage and monitor UAV flight paths. However, centralized UTM systems are susceptible to various security attacks and are inefficient in managing flight data from different service providers. It further fails to provide low-latency communication required for UAV real-time operations. Thus, this paper proposes to integrate Drone-to-Drone (D2D) communication protocol into a secure public blockchain-based UTM system to enable direct communication between UAVs for efficient collision avoidance. The D2D protocol is designed using SHA256 hash function and bitwise XOR operations. A proof of concept has been built to verify that the UTM system is secure by enabling authorized service providers to view sensitive flight data only using legitimate secret keys. The security of the protocol has been analyzed and has been proven to be secure from key disclosure, adversary-in-the-middle, replay, and tracking attacks. Its performance has been evaluated and is proven to outperform existing studies by having the lowest computation cost of 0.01 ms and storage costs of 544–800 bits. Full article

As global energy systems transition towards greater reliance on renewable energy sources, the integration of energy storage systems (ESSs) becomes increasingly critical to managing the intermittency and variability associated with renewable generation. This paper provides a comprehensive review of the application of evolutionary game theory (EGT) to optimize ESSs, emphasizing its role in enhancing decision-making processes, operation scheduling, and multi-agent coordination within dynamic, decentralized energy environments. A significant contribution of this paper is the incorporation of negotiation mechanisms and collaborative decision-making frameworks, which are essential for effective multi-agent coordination in complex systems. Unlike traditional game-theoretic models, EGT accounts for bounded rationality and strategic adaptation, offering a robust tool for modeling the interactions among stakeholders such as energy producers, consumers, and storage operators. The paper first addresses the key challenges in integrating ESS into modern power grids, particularly with high penetration of intermittent renewable energy. It then introduces the foundational principles of EGT and compares its advantages over classical game theory in capturing the evolving strategies of agents within these complex environments. A key innovation explored in this review is the hybridization of game-theoretic models, combining the stability of classical game theory with the adaptability of EGT, providing a comprehensive approach to resource allocation and coordination. Furthermore, this paper highlights the importance of deliberative democracy and process-based negotiation decision-making mechanisms in optimizing ESS operations, proposing a shift towards more inclusive, transparent, and consensus-driven decision-making. The review also examines several case studies where EGT has been successfully applied to optimize both local and large-scale ESSs, demonstrating its potential to enhance system efficiency, reduce operational costs, and improve reliability. Additionally, hybrid models incorporating evolutionary algorithms and particle swarm optimization have shown superior performance compared to traditional methods. The future directions for EGT in ESS optimization are discussed, emphasizing the integration of artificial intelligence, quantum computing, and blockchain technologies to address current challenges such as data scarcity, computational complexity, and scalability. These interdisciplinary innovations are expected to drive the development of more resilient, efficient, and flexible energy systems capable of supporting a decarbonized energy future. Full article

Public transport agencies must triage thousands of multilingual complaints every day, yet the cost of training and serving fine-grained sentiment analysis models limits real-time deployment. The proposed “one encoder, any facet” framework therefore offers a reproducible, resource-efficient alternative to heavy fine-tuning for domain-specific sentiment analysis or opinion mining tasks on digital service data. To the best of our knowledge, we are the first to test this paradigm on operational multilingual complaints, where public transport agencies must prioritize thousands of Russian- and Kazakh-language messages each day. A human-labelled corpus of 2400 complaints is embedded with five open-source universal models. Obtained embeddings are matched to semantic “anchor” queries that describe three distinct facets: service aspect (eight classes), implicit frustration, and explicit customer request. In the strict zero-shot setting, the best encoder reaches 77% accuracy for aspect detection, 74% for frustration, and 80% for request; taken together, these signals reproduce human four-level priority in 60% of cases. Attaching a single-layer logistic probe on top of the frozen embeddings boosts performance to 89% for aspect, 83–87% for the binary facets, and 72% for end-to-end triage. Compared with recent fine-tuned sentiment analysis systems, our pipeline cuts memory demands by two orders of magnitude and eliminates task-specific training yet narrows the accuracy gap to under five percentage points. These findings indicate that a single frozen encoder, guided by handcrafted anchors and an ultra-light head, can deliver near-human triage quality across multiple pragmatic dimensions, opening the door to low-cost, language-agnostic monitoring of digital-service feedback. Full article

This paper proposes and evaluates a novel real-time cybersecurity framework integrating artificial intelligence (AI) and blockchain technology to enhance the detection and auditability of cyber threats. Traditional cybersecurity approaches often lack transparency and robustness in logging and verifying AI-generated decisions, hindering forensic investigations and regulatory compliance. To address these challenges, we developed an integrated solution combining a convolutional neural network (CNN)-based anomaly detection module with a permissioned Ethereum blockchain to securely log and immutably store AI-generated alerts and relevant metadata. The proposed system employs smart contracts to automatically validate AI alerts and ensure data integrity and transparency, significantly enhancing auditability and forensic analysis capabilities. To rigorously test and validate our solution, we conducted comprehensive experiments using the CICIDS2017 dataset and evaluated the system’s detection accuracy, precision, recall, and real-time responsiveness. Additionally, we performed penetration testing and security assessments to verify system resilience against common cybersecurity threats. Results demonstrate that our AI-blockchain integrated solution achieves superior detection performance while ensuring real-time logging, transparency, and auditability. The integration significantly strengthens system robustness, reduces false positives, and provides clear benefits for cybersecurity management, especially in regulated environments. This paper concludes by outlining potential avenues for future research, particularly extending blockchain scalability, privacy enhancements, and optimizing performance for high-throughput cybersecurity applications. Full article

The construction industry, a cornerstone of global economic growth, continues to struggle with entrenched inefficiencies, including low productivity, cost overruns, and fragmented project delivery. Addressing these persistent challenges requires more than incremental improvements, it demands a strategic unification of Lean Construction, Building Information Modeling (BIM), and Emerging Technologies. This systematic review synthesizes evidence from 64 academic studies to identify the most influential tools, techniques, and methodologies across these domains, revealing both their individual strengths and untapped synergies. The analysis highlights widely adopted Lean practices such as the Last Planner System (LPS) and Just-In-Time (JIT); BIM capabilities across 3D, 4D, 5D, 6D, and 7D dimensions; and a spectrum of digital innovations including Digital Twins, AR/VR/MR, AI, IoT, robotics, and blockchain. Crucially, the review demonstrates that despite rapid advancements, integration remains sporadic and unstructured, representing a critical research and industry gap. By moving beyond descriptive mapping, this study establishes an essential foundation for the development of robust, adaptable integration frameworks capable of bridging theory and practice. Such frameworks are urgently needed to optimize efficiency, enhance sustainability, and enable innovation in large-scale and complex construction projects, positioning this work as both a scholarly contribution and a practical roadmap for future research and implementation. Full article

Deep-learning-based multiple label chest X-ray classification has achieved significant success, but existing models still have three main issues: fixed-scale convolutions fail to capture both large and small lesions, standard pooling is lacking in the lack of attention to important regions, and linear classification lacks the capacity to model complex dependency between features. To circumvent these obstacles, we propose CONVFCMAE, a lightweight yet powerful framework that is built on a backbone that is partially frozen (77.08 % of the initial layers are fixed) in order to preserve complex, multi-scale features while decreasing the number of trainable parameters. Our architecture adds (1) an intelligent global pooling module that is learnable, with $1\times 1$ convolutions that are dynamically weighted by their spatial location, and (2) a multi-head attention block that is dedicated to channel re-calibration, along with (3) a two-layer MLP that has been enhanced with ReLU, batch normalization, and dropout. This module is used to enhance the non-linearity of the feature space. To further reduce the noise associated with labels and the imbalance in class distribution inherent to the NIH ChestXray14 dataset, we utilize a combined loss that combines BCEWithLogits and Focal Loss as well as extensive data augmentation. On ChestXray14, the average ROC–AUC of CONVFCMAE is 0.852, which is 3.97 percent greater than the state of the art. Ablation experiments demonstrate the individual and collective effectiveness of each component. Grad-CAM visualizations have a superior capacity to localize the pathological regions, and this increases the interpretability of the model. Overall, CONVFCMAE provides a practical, generalizable solution to the problem of extracting features from medical images in a practical manner. Full article

Smart contracts have become integral to decentralized applications, yet their programmability introduces critical security risks, exemplified by high-profile exploits such as the DAO and Parity Wallet incidents. Existing vulnerability detection methods, including static and dynamic analysis, as well as machine learning-based approaches, often struggle with emerging threats and rely heavily on large, labeled datasets. This study investigates the effectiveness of open-source, lightweight large language models (LLMs) fine-tuned using parameter-efficient techniques, including Quantized Low-Rank Adaptation (QLoRA), for smart contract vulnerability detection. We introduce the EVuLLM dataset to address the scarcity of diverse evaluation resources and demonstrate that our fine-tuned models achieve up to 94.78% accuracy, surpassing the performance of larger proprietary models, while significantly reducing computational requirements. Moreover, we emphasize the advantages of lightweight models deployable on local hardware, such as enhanced data privacy, reduced reliance on internet connectivity, lower infrastructure costs, and improved control over model behavior, factors that are especially critical in security-sensitive blockchain applications. We also explore Retrieval-Augmented Generation (RAG) as a complementary strategy, achieving competitive results with minimal training. Our findings highlight the practicality of using locally hosted LLMs for secure, efficient, and reproducible smart contract analysis, paving the way for broader adoption of AI-driven security in blockchain ecosystems. Full article

Sustainable aviation fuel (SAF) has demonstrated significant potential to reduce carbon emissions in the aviation industry. Multiple national and international initiatives have been launched to accelerate SAF adoption, yet large-scale commercialization continues to face technological, operational, and regulatory barriers. Industry 4.0 provides a suite of advanced technologies that can address these challenges and improve SAF operations across the supply chain. This study conducts an integrative literature review to identify and synthesize research on the application of Industry 4.0 technologies in the production and distribution of SAF. The findings highlight that technologies such as artificial intelligence (AI), Internet of Things (IoT), blockchain, digital twins, and 3D printing can enhance feedstock logistics, optimize conversion pathways, improve certification and compliance processes, and strengthen overall supply chain transparency and resilience. By mapping these applications to the six key workstreams of the SAF Grand Challenge, this study presents a practical framework linking technological innovation to both strategic and operational aspects of SAF commercialization. Integrating Industry 4.0 solutions into SAF production and supply chains contributes to reducing life cycle greenhouse gas (GHG) emissions, strengthens low-carbon energy systems, and supports the United Nations Sustainable Development Goal 13 (SDG 13). The findings from this research offer practical guidance to policymakers, industry practitioners, investors, and technology developers seeking to accelerate the global shift toward carbon neutrality in aviation. Full article

With the rapid development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) is emerging as a new network architecture for the modern Internet. SDN separates the control plane from the data plane, allowing a central controller, the SDN controller, to quickly direct the routing devices within the topology to forward data packets, thus providing flexible traffic management for communication between information sources. However, traditional Distributed Denial of Service (DDoS) attacks still significantly impact SDN systems. This paper proposes a novel dual-layer strategy capable of detecting and mitigating DDoS attacks in an SDN network environment. The first layer of the strategy enhances security by using blockchain technology to replace the SDN flow table storage container in the northbound interface of the SDN controller. Smart contracts are then used to process the stored flow table information. We employ the time window algorithm and the token bucket algorithm to construct the first layer strategy to defend against obvious DDoS attacks. To detect and mitigate less obvious DDoS attacks, we design a second-layer strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm from unsupervised learning techniques to perform binary classification, thereby identifying abnormal traffic. We conduct experimental validation using the publicly available DDoS dataset CIC-DDoS2019. The results show that using this strategy in the SDN network reduces the average deviation of round-trip time (RTT) by approximately 38.86% compared with the original SDN network without this strategy. Furthermore, the accuracy of DDoS attack detection reaches 97.66% and an F1 score of 92.2%. Compared with other similar methods, under comparable detection accuracy, the deployment of our strategy in small-scale SDN network topologies provides faster detection speeds for DDoS attacks and exhibits less fluctuation in detection time. This indicates that implementing this strategy can effectively identify DDoS attacks without affecting the stability of data transmission in the SDN network environment. Full article

Digital cash is coming, and it could be programmed to behave in novel ways. In 2020, the People’s Bank of China ran an experiment during which they distributed free digital cash to 50,000 citizens. But it had a twist: they programmed that digital cash to expire if not spent within a few days. This fascinating and somewhat paradoxical experiment opens many questions. If the cash expires, why would anyone accept it as payment? If it is intended to expire, can the recipient find ways to make it not expire? We explore a variety of possible attacks on expiring cash, countermeasures to those attacks, and alternative implementations, one based on CBDC and another on a public blockchain. We also discuss the more philosophical question of whether expiring cash is still cash: we argue it cannot be. Full article

This study examines how Business Intelligence (BI) capabilities influence environmental performance (EP) in manufacturaing supply chains, with a focus on the mediating roles of Green Supply Chain Management (GSCM) and Supply Chain Integration (SCI) and the moderating role of Blockchain Integration (BCI). Addressing a critical research gap in digital sustainability, particularly in emerging markets, this study integrates the Resource-Based View (RBV) theory, Natural Resource-Based View (NRBV) theory, and Dynamic Capabilities View (DCV) theory to develop a theoretically grounded framework. Data were collected via a cross-sectional survey of 231 managers in 65 firms in Jordan and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings reveal that while BI does not directly enhance EP, it significantly improves GSCM and SCI, which in turn mediate its influence on EP. GSCM fully mediates this relationship, while SCI provides partial mediation. BCI did not demonstrate a significant moderating effect. These results suggest that BI must be embedded within green and integrative operational systems to drive sustainability outcomes. This study contributes novel insights into how digital capabilities translate into environmental gains in underrepresented contexts and provides actionable guidance for firms and policymakers aiming to align digital transformation with environmental objectives. Full article

In this paper, I propose Yul2Vec, a novel method for representing Yul programs as distributed embeddings in continuous space. Yul serves as an intermediate language between Solidity and Ethereum Virtual Machine (EVM) bytecode, designed to enable more efficient optimization of smart contract execution compared to direct Solidity-to-bytecode compilation. The vectorization of a program is achieved by aggregating the embeddings of its constituent code elements from the bottom to the top of the program structure. The representation of the smallest construction units, known as opcodes (operation codes), along with their types and arguments, is generated using knowledge graph relationships to construct a seed vocabulary, which forms the foundation for this approach. This research is important for enabling future enhancements to the Solidity compiler, paving the way for advanced optimizations of Yul and, consequently, EVM code. Optimizing the EVM bytecode is essential not only for improving performance but also for minimizing the operational costs of smart contracts—a key concern for decentralized applications. By introducing Yul2Vec, this paper aims to provide a foundation for further research into compiler optimization techniques and cost-efficient smart contract execution on Ethereum. The proposed method is not only fast in learning embeddings but also efficient in calculating the final vector representation of Yul code, making it feasible to integrate this step into the future compilation process of Solidity-based smart contracts. Full article

This paper investigates the integration of Internet of things (IoT) technology with blockchain to enhance transparency, accountability, and operational efficiency in smart contract execution for IoT ecosystems. The proposed approach extends the Three-Phase Methodology (TpM) by introducing an innovative entity, the IoT Operator, which acts as a custody caretaker, contract enforcer, and mediator. By leveraging blockchain’s secure and immutable ledger, the IoT Operator ensures the reliable monitoring and governance of IoT applications. A PoC implementation conducted at the Eldorado Research Institute demonstrates the methodology’s effectiveness, realizing a significant reduction of 95.83% in equipment search time. This work highlights the practical advantages of integrating blockchain and IoT within a structured framework, emphasizing the need for tailored, application-specific solutions rather than generic decentralization. The findings offer actionable guidelines for implementing blockchain in IoT systems, paving the way for more secure, efficient, and resilient IoT applications. Full article