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Blockchain helps to give a sense of security as there is only one history of transactions visible to all the involved parties. Smart contracts enable users to manage significant asset amounts of finances on the blockchain without the involvement of any intermediaries. The conditions and checks that have been written in smart contract and executed to the application cannot be changed again. However, these unique features pose some other risks to the smart contract. Smart contracts have several flaws in its programmable language and methods of execution, despite being a developing technology. To build smart contracts and implement numerous complicated business logics, high-level languages are used by the developers to code smart contracts. Thus, blockchain smart contract is the most important element of any decentralized application, posing the risk for it to be attacked. So, the presence of vulnerabilities are to be taken care of on a priority basis. It is important for detection of vulnerabilities in a smart contract and only then implement and connect it with applications to ensure security of funds. The motive of the paper is to discuss how deep learning may be utilized to deliver bug-free secure smart contracts. Objective of the paper is to detect three kinds of vulnerabilities- reentrancy, timestamp and infinite loop. A deep learning model has been created for detection of smart contract vulnerabilities using graph neural networks. The performance of this model has been compared to the present automated tools and other independent methods. It has been shown that this model has greater accuracy than other methods while comparing the prediction of smart contract vulnerabilities in existing models.

As emphasized in multiple United Nations (UN) reports, sustainable agriculture, a key goal in the UN Sustainable Development Goals (SDGs), calls for dedicated efforts and innovative solutions. In this study, greenhouse gas (GHG) emissions in the groundnut supply chain from the region of Diourbel & Niakhar, Senegal, to the port of Dakar are investigated. The groundnut supply chain is divided into three steps: cultivation, harvesting, and processing/shipping. This work adheres to UN guidelines, addressing the imperative for sustainable agriculture by applying machine learning-based predictive modeling (MLPMs) utilizing the FAOSTAT and EDGAR databases. Additionally, it provides a novel approach using blockchain-enabled off-chain machine learning through smart contracts built on Hyperledger Fabric to secure GHG emissions storage and machine learning’s predictive analytics from fraud and enhance transparency and data security. This study also develops a decision-making dashboard to provide actionable insights for GHG emissions reduction strategies across the groundnut supply chain.

Previous research has shown that implementing blockchain technology in businesses can lead to more secure and efficient processes in various organizational areas, including human resource management. This review paper examines the use of blockchain in human resources departments from the perspective of transaction cost economics theory, which identifies several fundamental variables that increase transaction costs for firms. These variables include bounded rationality, the pervasive possibility of opportunistic behavior, and uncertainty. The paper explores how blockchain implementation, including blockchain-enabled smart contracts, can mitigate these challenges. The paper also identifies some limitations to using blockchain and smart contracts that may increase transaction costs and thus reduce transaction cost savings.

Blockchain technology has transformed modern digital ecosystems by enabling secure, transparent, and automated transactions through smart contracts. However, the increasing complexity of these contracts introduces significant challenges, including high computational costs, scalability limitations, and difficulties in detecting anomalous behavior. In this study, we propose an AI-based optimization framework that enhances the efficiency and security of blockchain smart contracts. The framework integrates Neural Architecture Search (NAS) to automatically design optimal Convolutional Neural Network (CNN) architectures tailored to blockchain data, enabling effective anomaly detection. To address the challenge of limited labeled data, transfer learning is employed to adapt pre-trained CNN models to smart contract patterns, improving model generalization and reducing training time. Furthermore, Model Compression techniques, including filter pruning and quantization, are applied to minimize the computational load, making the framework suitable for deployment in resource-constrained blockchain environments. Experimental results on Ethereum transaction datasets demonstrate that the proposed method achieves significant improvements in anomaly detection accuracy and computational efficiency compared to conventional approaches, offering a practical and scalable solution for smart contract monitoring and optimization.

Multi-robot systems are increasingly deployed in critical applications such as search and rescue, precision agriculture, and autonomous transportation. However, the presence of Byzantine robots—agents that intentionally transmit false or misleading information—can severely compromise mission success and system safety, highlighting the urgent need for robust fault-tolerant coordination mechanisms. To address the challenge of Byzantine faults in multi-robot systems, we propose a novel approach utilizing a blockchain-based framework, termed RobotOBchain (Robot Observation Blockchain). RobotOBchain permanently records each robot’s own state information and its observed neighboring robots’ states at every time step. By leveraging smart contracts encoded within the blockchain, our method automatically detects state inconsistencies or conflicts among recorded observations, enabling early identification of intentionally deceptive Byzantine robots. Experimental validation demonstrates that RobotOBchain achieves 100% consistent Byzantine identification across all robots, maintains estimation errors within 3% of ground-truth, and exhibits robust tolerance to up to 50% malicious agents. These results significantly surpass the performance of classical W-MSR algorithms, while eliminating the dependency on predefined fault bounds. The framework’s demonstrated capabilities indicate strong potential for practical deployment in dynamic and safety-critical multi-robot applications.

The advancement in blockchain technology has enabled smart contracts to automate the execution of tenancy obligations, known as \"smart tenancies\". This paper analyses the legal issues on the adoption of smart tenancies within Malaysia using legal doctrinal research method. We seek to answer these questions: (1) whether smart tenancies are enforceable in Malaysia; (2) whether parties to a smart tenancy can apply for an endorsement of tenancy under the National Land Code; (3) whether the legal profession can claim exclusivity in offering and maintaining smart tenancies services; and (4) whether there is room for self-help in resolving tenancy disputes using smart tenancies in Malaysia. The key findings are as follows: (1)(a) smart tenancies can and should be stamped when the user interface stipulates the information required for calculation of stamp duty; (1)(b) smart tenancies service provider have to comply with the Electronic Commerce Act 2006 to ensure that the system is reliable to attribute the electronic signatures to the contracting parties; (2) once the print-out of a smart tenancy is stamped, the tenant and landlord have an option to apply for endorsement of tenancy with the land registry under the National Land Code (Revised 2020); (3) the Legal Profession Act 1976 does not restrict the marketing, operation and maintenance of smart tenancies services to be done by law firms exclusively; and (4) there is no room for self-help eviction of a tenant in Malaysia, and the eviction process ought to be enforced with a court order.

In the modern era with an advanced technology, electric vehicles play a key role in transporting all kinds of products due to more carbon emissions. Among all products, agricultural products and commodities from their by-products are in substantial demand from all customers. Intercropping large-volume versatile plants such as sugarcane, wheat, and corn can be more sustainable and profitable for farmers and industry owners. Bagasse also contributes to the co-generation process of generating power, which reduces holding costs and production expenses for the industry. To transport products, this study suggests to use 100% bioethanol as an alternative renewable energy source for vehicles equipped with solid oxide fuel cells (SOFCs) instead of blending bioethanol with petrol. The primary aim of this study is to maximize integrated total profit while achieving zero waste in production and ensuring carbon neutrality in the industry through the use of ethanol trucks for transportation.

In the era of digital transformation, transaction-intensive e-business applications—such as high-frequency trading (HFT), e-monetary services and decentralized marketplaces—require infrastructures that are not only fast and secure but also sustainable. Current solutions often prioritize short-term performance over long-term resilience, leading to inefficiencies in energy use and system reliability. This paper introduces a conceptual framework for sustainable transaction processing, leveraging energy-efficient hardware accelerators, real-time communication protocols inspired by industrial automation and lightweight authentication mechanisms. By integrating associative memory-based matching engines and optimized network architectures, the proposed approach ensures predictable latency, robust security and scalability without compromising sustainability. The framework aligns with the United Nations Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure) by reducing resource consumption, enhancing operational resilience and supporting future-ready digital ecosystems.

Edge computing has low transmission delay and unites more agile interconnected devices spread across geographies, which enables cloud-edge storage more suitable for distributed data sharing. This paper proposes a trustworthy and reliable multi-keyword search (TRMS) in blockchain-assisted cloud-edge storage, where data users can choose a faster search based on edge servers or a wider search based on cloud servers. To acquire trustworthy search results and find reliable servers, the blockchain-based smart contract is introduced in our scheme, which will execute the search algorithm and update the score-based trust management model. In this way, search results and trust scores will be published and recorded on the blockchain. By checking search results, data users can judge whether the returned documents are top-k documents. Based on the trust management model, we can punish the malicious behavior of search servers, while data users can choose more reliable servers based on trust scores. To improve efficiency, we design a threshold-based depth-first search algorithm. Extensive experiments are simulated on Hyperledger Fabric v2.4.1, which demonstrate our scheme (with 16 threads) can reduce the time cost of index construction by 92% and the time cost of search by 82%, approximately. Security analysis and extensive experiments can prove the security and efficiency of the proposed scheme.

This paper reviews and synthesizes corporate finance and financial intermediation literature by highlighting transaction costs as a determining factor in their evolution. It then introduces blockchain as a potentially powerful technology that can significantly reduce transaction costs and therefore affect the structure of corporations and financial intermediaries. Modern corporations strive to coordinate functions internally to minimize transaction costs. And financial intermediaries attempt to resolve the information asymmetry problem among transacting parties. Over time, however, corporations and financial intermediation have been settled with the remaining incentive problems and their associated costs. For example, some financial intermediaries have committed fraud by timing their trades ahead of their customers, others by misquoting interest rates, and a few by fleecing customers by issuing unwanted credit cards. Regulators protecting customers often appear to be one step behind in preempting these intermediaries from wrongdoing. After introducing blockchain technology and explaining how it works, this paper examines the application of blockchain in real-estate finance, demonstrating how it can reduce or eliminate the role of multiple intermediaries in executing transactions.