The Industrial Internet of Things (IIoT) has been widely deployed to enable real-time monitoring and automation. Within IIoT-driven production, supply chain management plays a critical role, necessitating verifiable provenance to ensure the authenticity and traceability of goods across multi-stakeholder networks. While blockchain provides a tamper-proof foundation, traditional storage structures suffer from unsecured data integrity, poor query efficiency, and scalability over provenance data. To address these challenges, we propose vProChain, an efficient provenance verification system to support verifiable and parallel queries over graph-structured provenance data. First, we design an Adaptive DAG Verkle Tree (ADVT) that deterministically maps supply chain dependencies into a graph-native authenticated data structure, enabling constant-size proofs and low-overhead verification. Second, we introduce the Merkle Inverted Patricia Trie (MIPT) to facilitate fast, verifiable multi-dimensional Boolean queries. Third, we develop a parallel provenance query algorithm that accelerates multi-hop path retrieval via consistent hashing and weighted bipartite matching. Finally, formal security analysis and extensive empirical evaluations demonstrate that vProChain can provide provable cryptographic guarantees for the soundness of provenance proofs and the completeness of query retrievals, while achieving high query efficiency in a large-scale IIoT environment.

The manufacturing environment and activities with a large volume and variety of product data have put forward higher requirements for the proof and verification of identity information. Achieving decentralized digital identity management in the Industrial Internet of Things (IIoT) helps to improve the performance of relevant proofs and authentication. The Decentralized Identity (DID) system serves as a bridge between the physical and digital worlds, assigning digital identities to physical entities to facilitate their participation in online activities. However, faced with the huge number of manufacturing entities accessing the DID system, the number of DID documents in the system has proliferated. It is still a big challenge to improve the scalability of the system while ensuring the efficiency of information access and verification. In this paper, we propose a blockchain-based verifiable decentralized identity system for IIoT. First, we propose a blockchain-based system architecture with a specially designed storage structure for DID documents. Specifically, we design a structure based on Merkle Tree that visually summarises the physical associations of manufacturing entities and reduces access overhead. Second, we design a multiblock storage structure within the blockchain, which establishes inter-block jumps based on the associated DID, effectively improving the query efficiency of the system. Finally, we design a verification scheme that enables users to verify the integrity of the identity data of the proof provider. We implemented the system framework and conducted experiments to evaluate the performance of our system. The experimental results proved the effectiveness of the system.

Web 3.0 is viewed as the next generation of the Internet, with the aim of establishing a decentralized network where users can control their digital identities and data. Due to its decentralization feature, blockchain has become a promising solution for secure data storage and retrieval for abundant de-centralized applications in Web 3.0. In this context, decentralized identifiers (DIDs) are rapidly emerging as a key infrastructure for blockchain-based Web 3.0. However, with more and more DIDs generated and stored on the blockchain, it is challenging to support efficient retrieval of DIDs with data integrity assurance. In this paper, we propose a novel authenticated DID retrieval system for blockchain-based Web 3.0. Specifically, a new authenticated data structure (ADS) with the corresponding data verification algorithm is designed to enable efficient retrieval and verification for both DID records and their historical updates. Theoretical analysis has been performed to prove the security and efficiency of our proposed system. We implement our system and conduct experiments to evaluate the performance. Experimental results demonstrate that our proposed scheme exhibits higher system efficiency compared to the baseline solution.

The permanent magnet synchronous motor (PMSM) is widely adopted in the industry. However, current testing systems for PMSM drives exhibit several limitations. This article proposes a quasi-Z source inverter (qZSI) based PMSM emulator, which can emulate a PMSM with different test schemes under different working conditions. The motor emulator (ME) proposed in this paper is able to solve the inherent defects of the traditional inverter. Improve the quality of output voltage and the reliability of inverter system. In this paper, the parameterized model of PMSM and mathematical model of the qZSI are established firstly. Based on that, the PMSM emulator is developed and tested for different operating conditions. Matlab simulations have been used to verify the viability and efficiency of the system.

Web 3.0 has been proposed as a new generation of the Internet, which shifts towards system decentralization, improved data security, and self-sovereign identity. With the proliferation of networked entities, the proper management and verification of their identities play a vital role in Web 3.0. Decentralized identity is a promising paradigm to enhance data security and restore sovereignty over personal data to users. However, the data security in existing centralized solutions is often severely limited. In this paper, we propose vDID, a novel blockchain-enabled verifiable decentralized identity management system for Web 3.0. First, we design a generic verifiable DID structure, which is capable of capturing and expressing the inherent relationships between different entities with high granularity. Second, we develop an identity verification scheme to support efficient integrity verification for identities and their relationships in the decentralized framework. We implement vDID and conduct experiments to evaluate the system performance. Experimental results demonstrate the effectiveness of our proposed system.

Pathogen timetrees are phylogenies scaled to time. They reveal the temporal history of a pathogen spread through the populations as captured in the evolutionary history of strains. These timetrees are inferred by using molecular sequences of pathogenic strains sampled at different times. That is, temporally sampled sequences enable the inference of sequence divergence times. Here, we present a new approach (RelTime with Dated Tips [RTDT]) to estimating pathogen timetrees based on a relative rate framework underlying the RelTime approach that is algebraic in nature and distinct from all other current methods. RTDT does not require many of the priors demanded by Bayesian approaches, and it has light computing requirements. In analyses of an extensive collection of computer-simulated datasets, we found the accuracy of RTDT time estimates and the coverage probabilities of their confidence intervals (CIs) to be excellent. In analyses of empirical datasets, RTDT produced dates that were similar to those reported in the literature. In comparative benchmarking with Bayesian and non-Bayesian methods (LSD, TreeTime, and treedater), we found that no method performed the best in every scenario. So, we provide a brief guideline for users to select the most appropriate method in empirical data analysis. RTDT is implemented for use via a graphical user interface and in high-throughput settings in the newest release of cross-platform MEGA X software, freely available from http://www.megasoftware.net.