【Abstract】Distrust is the main obstacle to the green transformation of the supply chain. Blockchain can rebuild trust among multiple participants by employing distributed information technologies. Based on complex network evolutionary game models, this paper tries to reveal the transformative potential of blockchain in promoting the green transformation of the supply chain. Simulation experiments with real electric vehicle data show that if adopting blockchain technology: i) the greenness of the supply chain can increase by 200% even without government policy intervention; by 75% when jointly optimizing the intensity of rewards and penalties; by 40% when exclusively adjusting the reward or penalty intensity; ii) the profits of both suppliers and manufacturers can be improved when they adopt green investment strategies, creating a "win-win" scenario. Besides, optimal government rewards and penalties are provided to accomplish various high levels of greenness in a blockchain supply chain network. This study not only quantifies the significant impact of blockchain technology on enhancing the greenness of supply chains, but also reveals the decision-making mechanisms of enterprises and government under varying degrees of greenness, providing theoretical guidance for enterprises and government to manage green blockchain supply chains.
【Abstract】Almost all major cloud providers offer virtual machines running on servers with 64-bit ARM CPUs. For example, Amazon Web Services (AWS) designed custom ARM-based CPUs named Graviton2 and Graviton3. Other cloud providers, such as Microsoft Azure and Google Cloud Platform (GCP), employ servers with Ampere Altra CPUs. In this context, we conduct a comprehensive experimental study covering in-memory key-value stores, relational databases, enterprise blockchains, and Machine Learning inference. We cover all the available types of ARM cloud processors, including Graviton2 (AWS), Graviton3 (AWS), Ampere Altra (Azure and GCP), Yitian 710 (Alibaba Cloud), and Kunpeng 920 (Huawei Cloud). Our analysis shows that Yitian and Graviton3 are serious competitors for servers with Intel Xeon CPUs, achieving similar or better results with in-memory workloads. However, the performance of OLAP, ML inference, and blockchain on ARM-based servers is below that of Xeon. The reasons are mainly threefold 1) un-optimized software, 2) lower clock frequency, and 3) lower performance at core level. Surprisingly, ARM servers spend 2X more time in Linux kernel system calls compared to Xeon servers. Nonetheless, ARM-based servers show great potential. Given their lower cloud computing price, ARM servers could be the ideal choice when the performance is not critical.
【Abstract】The proposed idea is to give all the agricultural stakeholders secure storage. We must automate several processes utilizing brilliant codes to reduce risks and errors. The suggested schema applies Blockchain, source codes, and IoT on a farm network to enhance the analysis of agrarian datasets and tracking products to raise the productivity of agro-based supply chains. The application's architecture will fix the faults found in earlier research. In the suggested method, sensors give us information about the environment. The Blockchain ledger stores our data in blocks. We create special agricultural automated codes in the treatment layer to automate task decisions. (c) 2024 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
【Abstract】Today, blockchain ledgers utilize concurrent deterministic execution schemes to scale up. However, ordering fairness is not preserved in these schemes: although they ensure all replicas achieve the same serial order, this order does not always align with the fair, consensus-established order when executing smart contracts with runtime-determined accesses. To preserve ordering fairness, an intuitive method is to concurrently execute transactions and re-execute any order-violating ones. This in turn increases unforeseen conflicts, leading to scaling bottlenecks caused by numerous costly aborts under contention. To address these issues, we propose Spectrum, a novel deterministic execution scheme for smart contract execution on blockchain ledgers. Spectrum preserves the consensus-established serial order (so-called strict determinism) with high performance. Specifically, we leverage a speculative deterministic concurrency control to execute transactions in speculation and enforce an agreed-upon serial order by aborting and re-executing any mis-speculated ones. To overcome the scaling bottleneck, we present two key optimizations based on speculative processing: operation-level rollback and predictive scheduling, for reducing both the overhead and the number of mis-speculations. We evaluate Spectrum by executing EVM-based smart contracts on popular benchmarks, showing that it realizes fair smart contract execution by preserving ordering fairness and outperforms competitive schemes in contended workloads by 1.4x to 4.1x.
【Abstract】Peer-to-Peer (P2P) energy trading is a new financial mechanism that can be adopted to incentivize the development of distributed energy resources (DERs), by promoting the selling of excess energy to other peers on the network at a negotiated rate. Current incentive programs, such as net metering (NEM) and Feed-in-Tariff (FiT), operate according to a centralized policy framework, where energy is only traded with the utility, the state-owned grid authority, the service provider, or the power generation/distribution company, who also have the upper hand in deciding on the rates for buying the excess energy. This study presents a comparative analysis of three energy trading mechanisms, P2P energy trading, NEM, and FiT, within a rural microgrid consisting of two prosumers and four consumers. The microgrid serves as a practical testbed for evaluating the economic impacts of these mechanisms, through simulations considering various factors such as energy demand, production variability, and energy rates, and using key metrics such as economic savings, annual energy bill, and wasted excess energy. Results indicate that while net metering and FiT offer stable financial returns for prosumers, P2P trading demonstrates superior flexibility and potentially higher economic benefits for both prosumers and consumers by aligning energy trading with real-time market conditions. The findings offer valuable insights for policymakers and stakeholders seeking to optimize rural energy systems through innovative trading mechanisms.
【Abstract】Related party transactions (RPTs) can serve as channels for the spread of credit risk events among blockchain firms. However, current credit risk-assessment models typically only consider a firm's individual characteristics, overlooking the impact of related parties in the blockchain. We suggest incorporating RPT network analysis to improve credit risk evaluation. Our approach begins by representing an RPT network using a weighted adjacency matrix. We then apply DANE, a deep network embedding algorithm, to generate condensed vector representations of the firms within the network. These representations are subsequently used as inputs for credit risk-evaluation models to predict the default distance. Following this, we employ SHAP (Shapley Additive Explanations) to analyze how the network information contributes to the prediction. Lastly, this study demonstrates the enhancing effect of using DANE-based integrated features in credit risk assessment.
