Electronic health records (EHRs) support digital healthcare, precision medicine, and predictive analytics. Traditionally, EHRs’ distribution in centralized databases limits sharing across organizations. Also, the poor integration and limited patient control of EHRs obstruct a comprehensive view of health conditions, to the detriment of both patients and doctors. Blockchain technology promises decentralized patient-centric EHR management. However, decentralized solutions still face challenges in efficiently tracking and sharing scattered and correlated medical records. Patients struggle to construct and maintain the lineage of their overall medical records, so these solutions fail to efficiently and securely share correlated EHRs in a lineage with trusted doctors. Doctors from various departments struggle to access the most relevant EHRs necessary for a precise understanding of patients’ medical histories. This study introduces a blockchain-based e-healthcare system with provenance awareness, collecting data provenance to document traceable patient histories, facilitate rapid authorizations for correlated EHRs in a lineage, and conduct post-authorization audits. Designing a DAG-like data structure, the system efficiently stores the EHR provenance on blockchain and optimizes provenance-specific search. The system introduces a dedicated EHR authorization layer to facilitate dynamic authorization propagation along with the EHR provenance path. Harnessing the principles of Nash equilibrium, the system establishes a rigorous, honesty-driven auditing mechanism. This is done to scrutinize doctors’ data access provenance and the intricate journey of patient health records. Our model combines data provenance with blockchain, ensuring prompt, reliable EHR access. We developed a prototype using Ethereum blockchain smart contracts, finding that our model’s feasibility and performance align with expectations.

The digital health field's pursuit of massive, personalized healthcare continuously faces constraints from doctors' resources and capacity limitations. Recently, the emergence of large language models (LLMs), with their remarkable comprehension and processing abilities, has revolutionized digital health and enhanced massive, personalized healthcare. Although these LLMs have achieved significant advancements, they have also introduced inevitable hallucinations, which impact patient safety when used in massive applications. To address these challenges, this study proposes a digital hospital for a massive, personalized, reliable healthcare service named the Chat Chain-Brain-based Doctor (CHATCBD). In addition, this study transforms the LLM-based diagnostic process into a digital hospital architecture, designs a controllable AI agents framework, and develops a self-audit mechanism to enhance their reliability. The proposed CHATCBD uses blockchain technology to decentralize external regulation of the LLMs' personalized diagnoses. It introduces a blockchain-based personalized routing management mechanism to improve patient-centered decision-making and designs a blockchain-based audit framework based on a proposed mathematical model that ensures both the professionalism and honesty of audits, serving as a safety net for addressing LLM hallucinations. The results of extensive experiments conducted on 13 datasets from multiple perspectives demonstrate that the proposed CHATCBD system can significantly enhance the capabilities of LLMs in personalized healthcare. The code used in this study can be found at https://github.com/LDDLQ/ChatCBD.

Access control is widely used technology for securing sensitive resources of information systems, such as personal data managed by cloud-based data store and sensitive data stream collected by smart devices. Existing access control systems mainly adopt centralized architecture and static access control models, including Access Control List, Role-based Access Control and Attribute-based Access Control. However, these systems fail to meet the increasing requirements of behavior based dynamic access control or requirements of owner initiated autonomous access control without relying on trustworthy third parties and suffer inherent drawbacks of single point of failure or dishonesty. To this end, a novel blockchain-based and provenance enabled dynamic access control scheme called BPDAC is proposed. Specifically, it collects and stores data provenance on blockchain to enable behavior-based dynamic access control; in particular, the quick lookup table structure is designed to speed up access control evaluation based on provenance with increasing complexity. It also provides specifications for formulating access control policies based on provenance. It utilizes a set of smart contracts on blockchain to enable decentralized and reliable autonomous access control. A prototype system is implemented on the Hyperledger Fabric and experiments are conducted to show that the proposed scheme is practically feasible and scalable in terms of the performance metrics of throughput and latency.

Many traditional ceramic techniques are inscribed on UNESCO’s Intangible Cultural Heritage lists; yet, expert scarcity, long training cycles, and stylistic homogenization impede intergenerational transmission and innovation. Although large language models offer new opportunities, research tailored to ceramics remains limited. To address this gap, we first construct EvalCera, the first open-source domain large language model evaluation dataset for ceramic knowledge, image analysis, and generation, and conduct large-scale assessments of existing general large language models on ceramic tasks, revealing their limitations. We then release the first ceramics-focused training corpus for large language models and, using it, develop CeramicGPT, the first domain-specific large language model for ceramics. Finally, we built ChatCAS, a workflow multi-agent system built on CeramicGPT and GPT-4o. Experiments show that our model and agents achieve the best performance on EvalCera (A) and (B) text tasks as well as (C) image generation. The code is publicly available.

Genetic algorithms are machine learning algorithms inspired by the theory of natural evolution. By mimicking the process of natural selection and reproduction, genetic algorithms can provide high-quality solutions to a wide range of problems involving search, optimization, and learning. It can overcome some of the obstacles encountered by traditional search and optimization algorithms, especially for problems with many parameters and complex mathematical representations. To extend its application, this paper applies it to more subjective judging sessions. Firstly, after in-depth analysis, collation, and classification of judging data from several innovation competitions, genetic algorithms are subsequently used to optimize the allocation of works and judging experts, and the current standard score judging scheme is improved by non-linear correction of parameters. The results of the final data experiment show that the model greatly improves the concentration of scores and judging power and provides a reference for similar subjective scenario modelling optimization.