Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

The latest study sheds light on distributed ledger technologies (DLTs) outside blockchain systems. The first section of this article introduces DLTs, focusing on blockchain as the main paradigm. It highlights three critical characteristics of blockchain: decentralisation, transparency, and security, and emphasises how blockchain is transforming various industries, including supply chain management and finance. Subsequently, the discussion shifts to new developments and approaches in the DLT space. It introduces next-generation ledgers designed to address traditional blockchains' scalability, energy efficiency, and interoperability challenges. The study delves into modern innovations that achieve higher transaction speeds and greater flexibility, such as hybrid models and directed acyclic graphs (DAGs). A significant portion is dedicated to how these advanced DLTs are used to transform sectors like healthcare government, secure patient data management, and enhance transparency and citizen participation. The article also addresses the challenges and ethical considerations of using these technologies. Finally, the paper predicts that DLTs will improve efficiency and innovation in industries outside blockchain technology. To maximise these new technologies' potential, research and interdisciplinary collaboration are essential.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

The rapid and reliable restoration of urban medium-and low-voltage distribution networks is paramount for sustaining economic activities and social well-being.However, conventional centralised restoration methods are increasingly inadequate due to their inherent limitations in handling communication delays, heterogeneous real-time data integration, and the high computational complexity of stochastic optimisation, leading to prolonged outages and reduced service reliability.To address these challenges, this research proposes a fast power recovery method based on distributed edge computing for urban medium-and low-voltage distribution networks.The method enhances restoration efficiency through localised data processing, improved temporal performance, and the integration of heterogeneous data sources.Employing Box-Cox-combined Z-scale conversion for non-Gaussian temporal datasets and principal component-enhanced Dempster-Shafer deduction for information amalgamation, the approach transmutes multi-criteria recovery into singular-goal optimisation via decision matrices.Probabilistic voltage restrictions are reformulated as definitive quadratic mixed-integer constraints through sample average approximation, while second-degree conical relaxation manages non-linear current equations to establish a tractable mixed-integer quadratically constrained programming framework.Experimental outcomes demonstrate 2.89-minute recovery intervals, success probabilities exceeding 95%, and 0.82-0.91load distribution equilibrium, exhibiting superior performance relative to comparative methodologies.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.