Aiming at insufficient situation to express and operate assets in smart contracts, in this article we attempt to add a new asset model into smart contract language (such as SPESC) through combing method of asset's expressions and transactions in real-world contracts. Moreover, a translation mechanism can be set up to accomplish a conversion from the asset model to an executable contract program. On this basis, we propose a new language design toward asset-driven specific smart contracts, called TA-SPESC. This language complies with the structure of real-world contracts and supports a formal definition composed of four modules: Party, asset, term, and contract attribute. This asset model on it can be used to define various types of rights (including the right of ownership, use, possession, usufruct, and disposition of assets), as well as five asset operations (including asset registration, deposit, withdrawal, transfer, and cancellation) to effectively support asset transaction. More important, a series of generation rules are proposed to translate the TA-SPESC contract to an executable contract program. Moreover, taking house rental contract as an example, we provide a TA-SPESC instance and its specific description of translation process according to the generation rules, which supports a semiautomatic generation to executable programs. Finally, the Solidity codes derived from TA-SPESC contracts are run and tested, and the experiment and comparison results indicate that TA-SPESC contracts have high abstraction and low complexity, as well as versatility and convenience of asset transaction, which lead to more reliable software with less errors and fewer misunderstanding.

In recent years, advanced smart contract languages (ASCLs) have been proposed to solve the problem of difficult reading, comprehension, and collaboration when writing smart legal contracts among people in different fields. However, this kind of languages are still hard to put into practice due to the lack of an effective conversion method from the ASCLs to executable smart contract programs. Aiming at this problem, we take SPESC as example to explore how to design conversion rules from the contract in it to the target programming language in Solidity, and to propose a three-layer smart contract framework, including advanced smart-contract layer, general smart-contract layer, and executable machine-code layer. These rules provide an approach to convert the definition of SPESC contracting parties into party-contracts on target language, as well as to produce SPESC contract terms into main-contract on target language. Moreover, the proposed framework specifies not only program architecture and storage structure on general smart-contract layer, but also important mechanisms, including personnel management, timing control, exception handling, etc., which can assist programmers to write smart contract programs. Furthermore, taking four SPESC contracts as testing objects, we provide the whole process of converting from SPESC contracts to Solidity programs by the SPESC-Translator, and verify the efficiency and security of the conversion process, including coding, deploying, running, and testing through Ethereum. The instance results show that the conversion rules and the three-layer framework can simplify the writing of smart contracts, standardize the program structure, and help programmers to verify the correctness of the contract programs.

The decentralization mechanism provides manufacturers and distributors with greater customization and flexibility they need through Internet of Things (IoT)-based industrial collaboration systems (IoT-ICS), but it has brought forward security concerns about the shared data-processing tasks and IoT-based access to services and resources. To address them, we propose a practical blockchain solution to achieve decentralized policy management and evaluation on attribute-based access control (ABAC). By offloading the responsibility of ABAC policy administration and decision making to blockchain nodes, a blockchain-based access control framework, called Policychain, is presented to ensure policy with high availability, autonomy, and traceability. To deliver a solid design, we first present a transaction-oriented policy expression scheme with a well-defined syntax and semantics. The scheme can translate ABAC policies into the blockchain transactions with JavaScript object notation (JSON) syntax and script-based logical expression. We further realize a script-driven policy evaluation by extending blockchain inherent scripting instructions to support attribute acquisition of ABAC entities. Furthermore, we propose a policy lifecycle management scheme from policy creation, renovation, to revocation, in which policies are verified by three validation principles at the transaction level. Finally, we provide sophisticated analysis and experiments to show that our framework is secure and practical for decentralized policy management on ABAC in IoT-ICS.

Traditional Medicine (TM) has played a crucial role in global healthcare due to its long history and holistic approach. Artificial Intelligence (AI) has emerged as a revolutionary technology, offering exceptional capabilities in areas such as data mining, pattern recognition, and decision-making. The integration of Artificial Intelligence for Traditional Medicine (AITM) presents a promising frontier in advancing medicine and healthcare. In this review, we explore AITM from two perspectives: recent AI techniques and TM applications. Specifically, we investigate how Machine Learning, Deep Learning, and Large Language Models are applied to TM, covering applications such as diagnosis (before, during, after) and research (drug research, structured knowledge, data analysis). By leveraging advanced algorithms and models, AI can improve decision-making efficiency, optimize diagnosis accuracy, enhance patient experience, and reduce costs. We anticipate this review can bridge the gap between AI and TM communities. And the goal is to foster collaboration and innovation between both communities, enabling them to exploit the state-of-the-art AI techniques to advance TM diagnosis and research, ultimately contributing to the enhancement of human health.

This article aims to establish a cryptographic solution to improve security and reliability of the National Institute of Standards and Technology’s attribute-based access control (ABAC) model. By breaking down the existing structure of attribute-based encryption, we propose a new cryptographic ABAC (C-ABAC) framework with dynamic policy authorization and real-time attribute credentials. Moreover, a practical C-ABAC construction is proposed to support provable policy decision making and verifiable attribute Tokens among multiple distributed authorities. In this construction, we develop a concrete approach of generating a cryptographic policy from access control markup language. We also prove that attribute Token has existential unforgeability under chosen-attribute and chosen-nonce attacks, and the cryptographic policy is existentially unforgeable under chosen-object attack. In addition, our C-ABAC construction provides semantic security against chosen-plaintext attack with Token and policy queries under the extended general Diffie–Hellman exponent assumption. Finally, we evaluate the performance of the C-ABAC system according to complexity analysis and experimental results. The results show that the C-ABAC system is reliable and easy to implement.