Adoption of peer-to-peer (P2P) trading is very challenging, mainly due to numerous issues and limitations such as lack of trust in the concept and awareness of the technical, economic and social benefits. This paper aims to understand how blockchain technology can address the current issues/limitations of P2P distributed solar energy (DSE) trading. A series of semi-structured interviews were conducted with 23 community energy stakeholders to confirm and expand the stakeholder issues identified in the literature review. A case representing community energy projects was selected to (1) develop and implement a blockchain system and (2) evaluate its ability to eliminate (or reduce) stakeholder issues and meet stakeholder expectations. A blockchain-enabled P2P trading platform was developed using an Ethereum backend. The system clearly demonstrated its ability to deliver full or partial solutions to 12 stakeholder issues. Two stakeholder issues are unable to be addressed via the blockchain platform since they uncovered the weaknesses of blockchain technology. The P2P trading platform has also demonstrated its ability to facilitate decentralized trading and data management. The outcome of this study indicates the areas of P2P trading projects that can be improved by the application of blockchain technology.
C. Gunarathna, S. Jayasuriya, Kaige Wang et al.· Energies· 0 citations
Enterprises fine-tune language models on proprietary data that may later require removal due to privacy, contractual, or compliance obligations. Selective unlearning removes requested knowledge while preserving model utility, offering a practical alternative to full retraining, but existing methods treat the explicitly identified forget examples as the complete deletion scope. This is insufficient when target knowledge remains recoverable through paraphrases, aliases, or neighboring training examples. We propose GRAPHSU, a graph-guided controller that expands the deletion scope beyond forget seeds by constructing a weighted support-route graph, propagating deletion pressure through it, and applying graded forgetting strengths to high-risk neighbors. On the Task of Fictitious Unlearning (TOFU), a synthetic author-profile question-answering benchmark, and PISTOL, a structural-unlearning benchmark built around interconnected factual samples, with GPT-2 Medium and Llama-3.2-3B-Instruct, GRAPHSU achieves the lowest utility-feasible soft leakage across all deletion settings, reducing leakage by up to 49.5 percentage points over a matched seed-only baseline, demonstrating that effective enterprise unlearning requires controlling support routes, not just forget seeds.
Waqas Khan, Tabinda Sarwar, Jingyue Cong et al.· 0 citations
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