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51 pages, 554 KiB  
Article
Mean-Field-Type Transformers
by Hamidou Tembine, Manzoor Ahmed Khan and Issa Bamia
Mathematics 2024, 12(22), 3506; https://doi.org/10.3390/math12223506 - 9 Nov 2024
Viewed by 453
Abstract
In this article, we present the mathematical foundations of generative machine intelligence and link them with mean-field-type game theory. The key interaction mechanism is self-attention, which exhibits aggregative properties similar to those found in mean-field-type game theory. It is not necessary to have [...] Read more.
In this article, we present the mathematical foundations of generative machine intelligence and link them with mean-field-type game theory. The key interaction mechanism is self-attention, which exhibits aggregative properties similar to those found in mean-field-type game theory. It is not necessary to have an infinite number of neural units to handle mean-field-type terms. For instance, the variance reduction in error within generative machine intelligence is a mean-field-type problem and does not involve an infinite number of decision-makers. Based on this insight, we construct mean-field-type transformers that operate on data that are not necessarily identically distributed and evolve over several layers using mean-field-type transition kernels. We demonstrate that the outcomes of these mean-field-type transformers correspond exactly to the mean-field-type equilibria of a hierarchical mean-field-type game. Due to the non-convexity of the operators’ composition, gradient-based methods alone are insufficient. To distinguish a global minimum from other extrema—such as local minima, local maxima, global maxima, and saddle points—alternative methods that exploit hidden convexities of anti-derivatives of activation functions are required. We also discuss the integration of blockchain technologies into machine intelligence, facilitating an incentive design loop for all contributors and enabling blockchain token economics for each system participant. This feature is especially relevant to ensuring the integrity of factual data, legislative information, medical records, and scientifically published references that should remain immutable after the application of generative machine intelligence. Full article
(This article belongs to the Section Mathematics and Computer Science)
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<p>Attention mechanism at each layer: it involves a pairwise interaction between <inline-formula><mml:math id="mm1027"><mml:semantics><mml:msub><mml:mi>X</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:semantics></mml:math></inline-formula> and <inline-formula><mml:math id="mm1028"><mml:semantics><mml:msub><mml:mi>X</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:semantics></mml:math></inline-formula> via <inline-formula><mml:math id="mm1029"><mml:semantics><mml:mrow><mml:mo>〈</mml:mo><mml:mi>Q</mml:mi><mml:msub><mml:mi>X</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>K</mml:mi><mml:msub><mml:mi>X</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>〉</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula>, where <italic>Q</italic> is the query linear operator, and <italic>K</italic> is the key linear operator.</p>
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<p>The overall architecture of a classical transformer that follows the encoder–decoder paradigm. <italic>H</italic> is the number of heads of the attention. <inline-formula><mml:math id="mm1030"><mml:semantics><mml:mrow><mml:mi>H</mml:mi><mml:mi>M</mml:mi></mml:mrow></mml:semantics></mml:math></inline-formula> is the masked version of the multi-head attention. Enc-Dec denotes encoder–decoder. QoIs stands for quantities of interest. It takes a sequence of <italic>D</italic> tensors and transform them into another sequence of <italic>D</italic> tensors.</p>
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<p>Mean-field-type transformers. PF describes the push-forward operator. There is no <inline-formula><mml:math id="mm1031"><mml:semantics><mml:mrow><mml:mi>D</mml:mi><mml:mo>.</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula> Only one tensor and a distribution. Input: <inline-formula><mml:math id="mm1032"><mml:semantics><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>μ</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula>; output: <inline-formula><mml:math id="mm1033"><mml:semantics><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mi>Y</mml:mi><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>L</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mi>ν</mml:mi><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>L</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:semantics></mml:math></inline-formula>.</p>
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<p>Mean-field-type transformer block.</p>
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19 pages, 1185 KiB  
Article
Formalizing and Simulating the Token Aspects of Blockchain-Based Research Collaboration Platform Using Game Theory
by Chibuzor Udokwu
Mathematics 2024, 12(20), 3252; https://doi.org/10.3390/math12203252 - 17 Oct 2024
Viewed by 570
Abstract
Small and medium-scale enterprises (SMEs) need a platform that actively enables collaboration with research institutions and consultants as SMEs lack the financial resources to conduct independent research. Such a platform will require a verifiable manipulation-free system to enable, execute, and record collaboration activities [...] Read more.
Small and medium-scale enterprises (SMEs) need a platform that actively enables collaboration with research institutions and consultants as SMEs lack the financial resources to conduct independent research. Such a platform will require a verifiable manipulation-free system to enable, execute, and record collaboration activities and to track reputations among the organizations and individuals that use the platform. Blockchain provides an opportunity to build such a collaborative platform by enabling the verifiable recording of the results of the collaborations, aggregating the resulting reputation of the collaborating parties, and offering tokenized incentives to reward positive contributions to the platform. Cryptocurrencies from which blockchain tokens are derived are volatile, thereby reducing business organizations’ interest in blockchain applications. Hence, there is a need to design a self-sustaining valuable token model that incentivizes user behaviours that positively contribute to the platform. This paper explores the application of game theory in analyzing token-based economic interactions between various groups of users in an implemented blockchain-based collaboration platform to design and simulate a token distribution system that provides a fair reward mechanism for users while also providing a dynamic pricing model for the utility value provided by platform tokens. To achieve this objective, we adopted the design science research method, a running case of a blockchain collaboration platform that enables research collaboration, and extensive form games in game theory, first to analyze and simulate token outcomes of users of the collaboration platform. Secondly, the research used a logarithmic model to show the dynamic utility pricing property of the developed token model where the self-sustainability of the token is backed by the availability of an internal resource within the platform. Thirdly, we applied a qualitative approach to analyze potential risks in the designed token model and proposed risk mitigation strategies. Thus, the resulting models and their simulations, such as token distribution models and a dynamic token utility model, as well as the identified token risks and their mitigation strategies, represent the main contributions of this work. Full article
(This article belongs to the Special Issue Modeling and Simulation Analysis of Blockchain System)
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<p>Blockchain-based Collaboration Platform.</p>
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<p>Design Science Research methodology, adapted from [<a href="#B24-mathematics-12-03252" class="html-bibr">24</a>].</p>
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<p>SME-Consultant Interactions.</p>
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<p>SME–research Institution Interactions.</p>
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<p>Scenerio-based simulations of <span class="html-italic">G</span>1.</p>
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<p>Scenerio-based simulations of <span class="html-italic">G</span>2.</p>
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<p>Boundaries of possible tokens earned and minted.</p>
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<p>Dynamic token utility pricing.</p>
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37 pages, 8950 KiB  
Article
Blockchain Technology Adoption by Critical Stakeholders in Prefabricated Construction Supply Chain Based on Evolutionary Game and System Dynamics
by Rui Zhou, Jin Wang and Dongli Zhu
Buildings 2024, 14(9), 3034; https://doi.org/10.3390/buildings14093034 - 23 Sep 2024
Viewed by 1142
Abstract
Blockchain technology (BT) is a promising solution to address information asymmetry and trust issues in the prefabricated construction supply chain (PCSC). However, its practical application in PCSC remains limited under the influence of stakeholders’ adoption strategies. While previous studies have analyzed drivers and [...] Read more.
Blockchain technology (BT) is a promising solution to address information asymmetry and trust issues in the prefabricated construction supply chain (PCSC). However, its practical application in PCSC remains limited under the influence of stakeholders’ adoption strategies. While previous studies have analyzed drivers and barriers to BT adoption, they often take a static view, neglecting the long-term dynamic decision-making interactions between stakeholders. This study addresses this gap by examining the interests of owners, general contractors, and subcontractors, and by developing a tripartite evolutionary game model to analyze the interaction mechanism of the strategy of adopting BT in PCSC. Additionally, a system dynamics simulation validates the evolution of stabilization strategies and examines the impact of key parameters. The results indicate that successful BT adoption requires technology maturity to surpass a threshold between 0.5 and 0.7, along with a fair revenue and cost-sharing coefficient between general contractors and subcontractors, ranging from 0.3 to 0.5 at the lower limit and 0.7 to 0.9 at the upper limit. Notably, general contractors play a pivotal role in driving BT adoption, acting as potential leaders. Furthermore, appropriate incentives, default compensation, and government subsidies can promote optimal adoption strategies, although overly high incentives may reduce owners’ willingness to mandate BT adoption. This study provides practical insights and policy recommendations for critical stakeholders to facilitate the widespread adoption of BT in PCSC. Full article
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<p>Blockchain technology adoption relationship between the three stakeholders.</p>
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<p>Research methodology.</p>
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<p>Dynamic evolutionary phase diagram. (<b>a</b>) Dynamic evolutionary phase diagram of owners; (<b>b</b>) Dynamic evolutionary phase diagram of general contractors; (<b>c</b>) Dynamic evolutionary phase diagram of subcontractors when <math display="inline"><semantics> <mrow> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>−</mo> <mi>e</mi> </mrow> <mrow> <mo>−</mo> <mi>φ</mi> </mrow> </msup> <mo>)</mo> <mfenced separators="|"> <mrow> <mn>1</mn> <mo>−</mo> <mi>α</mi> </mrow> </mfenced> <mo>∆</mo> <mi>B</mi> <mo>−</mo> <msup> <mrow> <mi>e</mi> </mrow> <mrow> <mo>−</mo> <mi>φ</mi> </mrow> </msup> <mfenced separators="|"> <mrow> <mn>1</mn> <mo>−</mo> <mi>β</mi> </mrow> </mfenced> <mo>∆</mo> <mi>C</mi> <mo>&gt;</mo> <mfenced open="[" close="]" separators="|"> <mrow> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>−</mo> <mi>e</mi> </mrow> <mrow> <mo>−</mo> <mi>φ</mi> </mrow> </msup> <mo>)</mo> <msub> <mrow> <mi>B</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>−</mo> <msup> <mrow> <mi>e</mi> </mrow> <mrow> <mo>−</mo> <mi>φ</mi> </mrow> </msup> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </mfenced> <mo>−</mo> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>&gt;</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>d</b>) Dynamic evolutionary phase diagram of subcontractors when <math display="inline"><semantics> <mrow> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>−</mo> <mi>e</mi> </mrow> <mrow> <mo>−</mo> <mi>φ</mi> </mrow> </msup> <mo>)</mo> <mfenced separators="|"> <mrow> <mn>1</mn> <mo>−</mo> <mi>α</mi> </mrow> </mfenced> <mo>∆</mo> <mi>B</mi> <mo>−</mo> <msup> <mrow> <mi>e</mi> </mrow> <mrow> <mo>−</mo> <mi>φ</mi> </mrow> </msup> <mfenced separators="|"> <mrow> <mn>1</mn> <mo>−</mo> <mi>β</mi> </mrow> </mfenced> <mo>∆</mo> <mi>C</mi> <mo>&lt;</mo> <mfenced open="[" close="]" separators="|"> <mrow> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>−</mo> <mi>e</mi> </mrow> <mrow> <mo>−</mo> <mi>φ</mi> </mrow> </msup> <mo>)</mo> <msub> <mrow> <mi>B</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>−</mo> <msup> <mrow> <mi>e</mi> </mrow> <mrow> <mo>−</mo> <mi>φ</mi> </mrow> </msup> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </mfenced> <mo>−</mo> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>System dynamics model of BT adoption by three critical stakeholders.</p>
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<p>Evolutionary paths of strategies of the tripartite game in the four stages. (<b>a</b>) the initial stage; (<b>b</b>) the budding stage; (<b>c</b>) the growth stage; (<b>d</b>) the maturity stage.</p>
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<p>Extreme condition test of SD model.</p>
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<p>Effect of initial strategies changes on system evolution. (<b>a</b>) Effect of changes in x on y; (<b>b</b>) Effect of changes in x on z; (<b>c</b>) Effect of changes in y on x; (<b>d</b>) Effect of changes in y on z; (<b>e</b>) Effect of changes in z on x; (<b>f</b>) Effect of changes in z on y.</p>
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<p>Effect of <math display="inline"><semantics> <mrow> <mi>φ</mi> </mrow> </semantics></math> on system evolution. (<b>a</b>) Effect of <math display="inline"><semantics> <mrow> <mi>φ</mi> </mrow> </semantics></math> on owners’ strategy; (<b>b</b>) Effect of <math display="inline"><semantics> <mrow> <mi>φ</mi> </mrow> </semantics></math> on general contractors’ strategy; (<b>c</b>) Effect of <math display="inline"><semantics> <mrow> <mi>φ</mi> </mrow> </semantics></math> on subcontractors’ strategy.</p>
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<p>Effect of <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math> on system evolution. (<b>a</b>) Effect of <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math> on owners’ strategy; (<b>b</b>) Effect of <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math> on general contractors’ strategy; (<b>c</b>) Effect of <math display="inline"><semantics> <mrow> <mi>α</mi> </mrow> </semantics></math> on subcontractors’ strategy.</p>
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<p>Effect of <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> on system evolution. (<b>a</b>) Effect of <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> on owners’ strategy; (<b>b</b>) Effect of <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> on general contractors’ strategy; (<b>c</b>) Effect of <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> on subcontractors’ strategy.</p>
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<p>Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on system evolution. (<b>a</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on owners’ strategy; (<b>b</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on general contractors’ strategy; (<b>c</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on subcontractors’ strategy.</p>
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<p>Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on system evolution. (<b>a</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on owners’ strategy; (<b>b</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on general contractors’ strategy; (<b>c</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on subcontractors’ strategy.</p>
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<p>Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> on system evolution. (<b>a</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mo> </mo> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> on owners’ strategy; (<b>b</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> on general contractors’ strategy; (<b>c</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> on subcontractors’ strategy.</p>
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<p>Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math> on system evolution. (<b>a</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> on owners’ strategy; (<b>b</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on general contractors’ strategy; (<b>c</b>) Effect of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> on subcontractors’ strategy.</p>
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19 pages, 1367 KiB  
Article
Blockchain-Assisted Secure Energy Trading in Electricity Markets: A Tiny Deep Reinforcement Learning-Based Stackelberg Game Approach
by Yong Xiao, Xiaoming Lin, Yiyong Lei, Yanzhang Gu, Jianlin Tang, Fan Zhang and Bin Qian
Electronics 2024, 13(18), 3647; https://doi.org/10.3390/electronics13183647 - 13 Sep 2024
Viewed by 1013
Abstract
Electricity markets are intricate systems that facilitate efficient energy exchange within interconnected grids. With the rise of low-carbon transportation driven by environmental policies and tech advancements, energy trading has become crucial. This trend towards Electric Vehicles (EVs) is bolstered by the pivotal role [...] Read more.
Electricity markets are intricate systems that facilitate efficient energy exchange within interconnected grids. With the rise of low-carbon transportation driven by environmental policies and tech advancements, energy trading has become crucial. This trend towards Electric Vehicles (EVs) is bolstered by the pivotal role played by EV charging operators in providing essential charging infrastructure and services for widespread EV adoption. This paper introduces a blockchain-assisted secure electricity trading framework between EV charging operators and the electricity market with renewable energy sources. We propose a single-leader, multi-follower Stackelberg game between the electricity market and EV charging operators. In the two-stage Stackelberg game, the electricity market acts as the leader, deciding the price of electric energy. The EV charging aggregator leverages blockchain technology to record and verify energy trading transactions securely. The EV charging operators, acting as followers, then decide their demand for electric energy based on the set price. To find the Stackelberg equilibrium, we employ a Deep Reinforcement Learning (DRL) algorithm that tackles non-stationary challenges through policy, action space, and reward function formulation. To optimize efficiency, we propose the integration of pruning techniques into DRL, referred to as Tiny DRL. Numerical results demonstrate that our proposed schemes outperform traditional approaches. Full article
(This article belongs to the Special Issue Network Security Management in Heterogeneous Networks)
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Graphical abstract
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<p>A blockchain-assisted secure electricity trading framework between EV charging operators and the electricity market.</p>
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<p>Performance comparison between the proposed Tiny PPO algorithm and the PPO algorithm.</p>
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<p>Utilities and strategies of the electricity market and EVs under different costs, with <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> corresponding to the number of EV charging operators and a unit profit of <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>.</p>
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<p>Utilities and strategies of the electricity market and EV charging operators under different numbers of EV charging operators with cost of <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and unit profit of <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>.</p>
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<p>Utilities and strategies of the electricity market and EV charging operators under different unit profits (<math display="inline"><semantics> <mi>α</mi> </semantics></math>), with <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> corresponding to the number of EV charging operators and cost of <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p>
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<p>Security probability under different numbers of miners.</p>
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23 pages, 2076 KiB  
Article
Blockchain-Based Spectrum Sharing Algorithm for UAV-Assisted Relay System
by Fukang Huang and Qi Zhu
Electronics 2024, 13(18), 3600; https://doi.org/10.3390/electronics13183600 - 10 Sep 2024
Viewed by 654
Abstract
Unmanned aerial vehicles (UAVs) are promising tools in mobile communication due to their flexibility. However, the rapid development of mobile communications further intensifies the challenge of spectrum scarcity, necessitating spectrum sharing with other systems. We suggest a Spectrum Sharing Algorithm for a UAV-Assisted [...] Read more.
Unmanned aerial vehicles (UAVs) are promising tools in mobile communication due to their flexibility. However, the rapid development of mobile communications further intensifies the challenge of spectrum scarcity, necessitating spectrum sharing with other systems. We suggest a Spectrum Sharing Algorithm for a UAV-Assisted Relay System. The utility function of secondary users (SUs) is defined by their communication rate, rewards from relay primary users (PUs), and spectrum leasing expenses. The utility function of PUs consists of their communication rate and revenue from spectrum leasing. We propose a joint optimization algorithm for the positioning and power allocation of UAVs, maximizing the frequency spectrum utilization rate of users. Spectrum trading between PUs and SUs is modeled as a Stackelberg game, and the problem is solved by using Lagrange multipliers and KKT conditions. To enhance the security of spectrum trading, a reputation-based spectrum sharing blockchain consensus algorithm is designed. We utilize Shamir’s secret sharing method to reduce computational complexity. Additionally, we design a smart contract to optimize the functionality of transaction transfers. The findings demonstrate that the proposed algorithm enhances the benefits for both participants in spectrum sharing, while safeguarding the security of spectrum transactions. Full article
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<p>System Model.</p>
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<p>Block Structure.</p>
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<p>Comparison of the utility of different algorithms: (<b>a</b>) Description of utility of UAVs; (<b>b</b>) Description of utility of BS.</p>
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<p>Comparison of utility of different bandwidths: (<b>a</b>) Description of utility of UAVs; (<b>b</b>) Description of utility of BS.</p>
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<p>Comparison of utility between different transmission power of BS: (<b>a</b>) Description of utility of UAVs; (<b>b</b>) Description of utility of BS.</p>
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<p>Spectrum trading.</p>
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<p>Reputation value update and related information.</p>
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<p>Comparison of average mining time.</p>
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23 pages, 2655 KiB  
Article
Research on the Game Strategy of Mutual Safety Risk Prevention and Control of Industrial Park Enterprises under Blockchain Technology
by Chang Su, Jun Deng, Xiaoyang Li, Fangming Cheng, Wenhong Huang, Caiping Wang, Wangbo He and Xinping Wang
Systems 2024, 12(9), 351; https://doi.org/10.3390/systems12090351 - 6 Sep 2024
Cited by 7 | Viewed by 839
Abstract
Systematic management of corporate safety risks in industrial parks has become a hot topic. And risk prevention and control mutual aid is a brand-new model in the risk and emergency management of the park. In the context of blockchain, how to incentivize enterprises [...] Read more.
Systematic management of corporate safety risks in industrial parks has become a hot topic. And risk prevention and control mutual aid is a brand-new model in the risk and emergency management of the park. In the context of blockchain, how to incentivize enterprises to actively invest in safety risk prevention and control mutual aid has become a series of key issues facing government regulators. This paper innovatively combines Prospect Theory, Mental Accounting, and Evolutionary Game Theory to create a hypothetical model of limited rationality for the behavior of key stakeholders (core enterprises, supporting enterprises, and government regulatory departments) in mutual aid for safety risk prevention and control. Under the static prize punishment mechanism and dynamic punishment mechanism, the evolutionary stabilization strategy of stakeholders was analyzed, and numerical simulation analysis was performed through examples. The results show: (1) Mutual aid for risk prevention and control among park enterprises is influenced by various factors, including external and subjective elements, and evolves through complex evolutionary paths (e.g., reference points, value perception). (2) Government departments are increasingly implementing dynamic reward and punishment measures to address the shortcomings of static mechanisms. Government departments should dynamically adjust reward and punishment strategies, determine clearly the highest standards for rewards and punishments, and the combination of various incentives and penalties can significantly improve the effectiveness of investment decisions in mutual aid for safety risk prevention and control. (3) Continuously optimizing the design of reward and punishment mechanisms, integrating blockchain technology with management strategies to motivate enterprise participation, and leveraging participant feedback are strategies and recommendations that provide new insights for promoting active enterprise investment in mutual aid for safety risk prevention and control. The marginal contribution of this paper is to reveal the evolutionary pattern of mutual safety risk prevention and control behaviors of enterprises in chemical parks in the context of blockchain. Full article
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<p>This is a figure. Summary figure.</p>
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<p>Tripartite logic relationship diagram.</p>
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<p>System evolution path diagrams for various reward and punishment mechanisms. (<b>a</b>) Evolutionary path map under the static punishment mechanism. (<b>b</b>) Evolutionary path map in dynamic incentives and static punishment mechanism. (<b>c</b>) Evolutionary route diagram in static encouragement and dynamic punishment mechanism. (<b>d</b>) Dynamic prank punishment mechanism evolution route map.</p>
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<p>Influence of efficacy reference point on system evolution in dynamic reward/punishment mechanism.</p>
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<p>Impact of cost reference point on system evolution in dynamic reward/punishment mechanism.</p>
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<p>Dynamic punishment Punishment impact on the evolution of the institution of the company’s spontaneous investment revenue in mechanism.</p>
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52 pages, 2867 KiB  
Article
A Blockchain and PKI-Based Secure Vehicle-to-Vehicle Energy-Trading Protocol
by Md Sahabul Hossain, Craig Rodine and Eirini Eleni Tsiropoulou
Energies 2024, 17(17), 4245; https://doi.org/10.3390/en17174245 - 25 Aug 2024
Viewed by 821
Abstract
With the increasing awareness for sustainable future and green energy, the demand for electric vehicles (EVs) is growing rapidly, thus placing immense pressure on the energy grid. To alleviate this, local trading between EVs should be encouraged. In this paper, we propose a [...] Read more.
With the increasing awareness for sustainable future and green energy, the demand for electric vehicles (EVs) is growing rapidly, thus placing immense pressure on the energy grid. To alleviate this, local trading between EVs should be encouraged. In this paper, we propose a blockchain and public key infrastructure (PKI)-based secure vehicle-to-vehicle (V2V) energy-trading protocol. A permissioned blockchain utilizing the proof of authority (PoA) consensus and smart contracts is used to securely store data. Encrypted communication is ensured through transport layer security (TLS), with PKI managing the necessary digital certificates and keys. A multi-leader, multi-follower Stackelberg game-based trade algorithm is formulated to determine the optimal energy demands, supplies, and prices. Finally, we propose a detailed communication protocol that ties all the components together, enabling smooth interaction between them. Key findings, such as system behavior and performance, scalability of the trade algorithm and the blockchain, smart contract execution costs, etc., are presented through numerical results by implementing and simulating the protocol in various scenarios. This work not only enhances local energy trading among EVs, encouraging efficient energy usage and reducing burden on the power grid, but also paves a way for future research in sustainable energy management. Full article
(This article belongs to the Section K: State-of-the-Art Energy Related Technologies)
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<p>V2V charging system architecture.</p>
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<p>V2V charging PKI architecture.</p>
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<p>New user enrollment process.</p>
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<p>Overview of the charging communication.</p>
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<p>Phase I: Trade request submission.</p>
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<p>Phase II: Optimal price and energy determination.</p>
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<p>Phase III: Trade completion and payment processing.</p>
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<p>Pure performance characteristics of the trade algorithm with 5 CEVs and 5 DEVs.</p>
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<p>Pure performance sensitivity analysis with respect to various algorithm-specific parameters.</p>
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<p>Trade algorithm characteristics for varying numbers of CEVs and DEVs.</p>
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<p>Trade algorithm characteristics for varying maximum demand and supply parameters.</p>
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<p>Trade algorithm characteristics for varying maximum price parameters.</p>
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<p>Timing diagram of the first charging EV in a 2 charging and 2 discharging EV scenario.</p>
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<p>Transmission and processing times for messages between the EVs and the CS in a 2 charging and 2 discharging EV scenario.</p>
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<p>Transmission and processing times for messages between the CS and the CSMS in a 2 charging and 2 discharging EV scenario.</p>
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<p>Data sizes for messages between the network entities in a 2 charging and 2 discharging EV scenario.</p>
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<p>Gas cost for executing smart contracts in a 2 charging and 2 discharging EV scenario.</p>
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<p>Variation of message data size with respect to varying numbers of DEVs and CEVs. (<b>a</b>) Data size for ChargingReq and ChargingRes messages; (<b>b</b>) Data size for DischargingReq and DischargingRes messages.</p>
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<p>Variation of message processing times for messages processed by the CSMSs with respect to varying numbers of DEVs and CEVs. (<b>a</b>) Processing time for OrderVerifyReq-OrderVerifyRes pairs; (<b>b</b>) Processing time for TradeVerifyReq-TradeVerifyRes pairs.</p>
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<p>Variation of gas costs with respect to varying numbers of DEVs and CEVs.</p>
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<p>Variation of message processing times for messages processed by the EVs and the CSs with respect to varying numbers of DEVs and CEVs.</p>
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<p>Blockchain characteristics with varying numbers of sealer and signer nodes.</p>
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<p>Blockchain characteristics with varying workloads and numbers of signer nodes.</p>
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<p>Blockchain characteristics with varying block periods and numbers of signer nodes.</p>
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<p>Blockchain characteristics with varying block periods and numbers of signer nodes.</p>
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25 pages, 1606 KiB  
Article
Financing a Capital-Constrained Supply Chain under Risk Regulations: Traditional Finance versus Platform Finance
by Jun Wu, Liyuan Yue, Na Li and Qianqian Zhang
Sustainability 2024, 16(17), 7268; https://doi.org/10.3390/su16177268 - 23 Aug 2024
Viewed by 912
Abstract
Small- and medium-sized enterprises (SMEs) frequently face challenges in obtaining financial assistance from traditional banks. Platform Supply Chain Finance (PSCF) has emerged as a promising solution for financing issues among SMEs, with an added focus on integrating sustainability aspects. This study focused on [...] Read more.
Small- and medium-sized enterprises (SMEs) frequently face challenges in obtaining financial assistance from traditional banks. Platform Supply Chain Finance (PSCF) has emerged as a promising solution for financing issues among SMEs, with an added focus on integrating sustainability aspects. This study focused on a two-tier supply chain as its primary research topic to find strategies to enhance supplier financial viability and improve the efficiency and profitability of the main manufacturing enterprise. In this study, we establish three distinct hypotheses corresponding to the three models involving supplier and manufacturer participation, encompassing parameters such as production batch size, pricing, and supply chain profit. First, it examined financing decisions through the lens of core enterprise-led platform finance. Second, it applied the Stackelberg game theory to investigate financing decisions in three distinct modes: traditional finance, platform internal finance, and external platform finance. Suppliers, manufacturers, and banks can be seen as participants in a Stackelberg game. In this game, suppliers act as leaders, making production and procurement decisions first, while manufacturers and banks act as followers, adjusting their behavior based on the suppliers’ decisions. Finally, it performed a comparative analysis of decisions and supply chain efficiency across these modes. When the risk regulation cost coefficient falls below a certain threshold, suppliers are willing to set up their own PSCF and there is an optimal level of risk regulation effort within the interval (0, 1). We compare platform finance with traditional finance and find that the traditional finance model maximizes profits for suppliers, while the external financing model maximizes profits for manufacturers and the overall supply chain profit. Findings provide insights for platforms, suppliers, manufacturers, and banks to implement optimal financing and channel structures to increase their profits and promote the sustainable development of the financial supply chain. In addition, future research on blockchain platform models would be highly meaningful. Full article
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<p>The timeline of supply chain events.</p>
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<p>The behavior relationship between suppliers, manufacturers, and banks.</p>
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<p>The relationship between manufacturer revenue and platform regulatory effort.</p>
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<p>The impact of different influencing factors on supplier decision making.</p>
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<p>Optimal decision and maximum revenue.</p>
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32 pages, 1896 KiB  
Article
Considering Blockchain Technology and Fairness Concerns for Supply Chain Pricing Decisions under Carbon Cap-and-Trade Mechanism
by Yande Gong, Xinze Jiang, Zhe Wang and Jizhou Zhan
Mathematics 2024, 12(16), 2550; https://doi.org/10.3390/math12162550 - 18 Aug 2024
Viewed by 651
Abstract
To address the growing demand for green development, governments worldwide have introduced policies to promote a green economy. Among these policies, the carbon cap-and-trade mechanism is adopted as an effective approach to control carbon emissions. Additionally, blockchain may increase transparency in the industrial [...] Read more.
To address the growing demand for green development, governments worldwide have introduced policies to promote a green economy. Among these policies, the carbon cap-and-trade mechanism is adopted as an effective approach to control carbon emissions. Additionally, blockchain may increase transparency in the industrial process. Despite focusing on improving its own green standards, the supply chain needs to establish stable cooperative relationship. Thus, we focus on a supply chain consisting of a dominant manufacturer and a retailer, where the manufacturer opts for implementing blockchain and the retailer selects their stance on fairness. We construct a Stackelberg game model and use backward induction to obtain the equilibrium solutions. In the supply chain, the highest profits can be achieved when the manufacturer adopts blockchain technology, provided that the cost of application is relatively low. For manufacturer and retailer, when the cost of applying blockchain is relatively low, they can both obtain maximized profits without applying blockchain and the retailer does not have fairness concerns. However, as the cost of inducing blockchain and the product’s reduction in carbon emission increase, the optimal strategies for manufacturer and retailer begin to diverge, which may affect the stability of the supply chain. Full article
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<p>Changes in optimal decisions with b. (<b>a</b>) Changes in product’s carbon emission reduction with b (<math display="inline"><semantics> <mi>k</mi> </semantics></math> = 2.5). (<b>b</b>) Changes in the demand quantity of low-carbon products with b (<math display="inline"><semantics> <mi>k</mi> </semantics></math> = 2.5).</p>
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<p>Effects of <math display="inline"><semantics> <mi>λ</mi> </semantics></math> and <math display="inline"><semantics> <mi>k</mi> </semantics></math> on the supply chain’s profit (<math display="inline"><semantics> <mi>k</mi> </semantics></math> = 2.5).</p>
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<p>Effects of <math display="inline"><semantics> <mi>λ</mi> </semantics></math> and <math display="inline"><semantics> <mi>k</mi> </semantics></math> on the decision profit (<math display="inline"><semantics> <mi>k</mi> </semantics></math> = 2.5): (<b>a</b>) Effects of <math display="inline"><semantics> <mi>λ</mi> </semantics></math> and <math display="inline"><semantics> <mi>k</mi> </semantics></math> on the manufacturer’s profit. (<b>b</b>) Effects of <math display="inline"><semantics> <mi>λ</mi> </semantics></math> and <math display="inline"><semantics> <mi>k</mi> </semantics></math> on the retailer’s profit.</p>
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34 pages, 2789 KiB  
Article
How to Choose Recycling Mode between Monopoly and Competition by Considering Blockchain Technology?
by Xuemei Zhang, Haodong Zheng, Tao Hang and Qiang Meng
Sustainability 2024, 16(15), 6296; https://doi.org/10.3390/su16156296 - 23 Jul 2024
Viewed by 770
Abstract
Enterprises adopting a circular economy approach can effectively solve the severe situation of resources and the environment, and recycling is considered an effective means to solve environmental issues. Simultaneously, blockchain technology (BT) has been used to enhance product quality trust. However, there is [...] Read more.
Enterprises adopting a circular economy approach can effectively solve the severe situation of resources and the environment, and recycling is considered an effective means to solve environmental issues. Simultaneously, blockchain technology (BT) has been used to enhance product quality trust. However, there is limited literature on how to choose between monopolistic and competitive recycling modes by considering BT. This paper uses a game involving a manufacturer, a retailer, and a third-party recycler (TPR) in a closed-loop supply chain (CLSC). The retailer can recycle on itself and compete with the TPR for recycling used products. The results show that BT adoption could increase the recycling rate and demand for remanufactured products. BT benefits the firms in the CLSC when they control usage costs, regardless of whether competitive recycling mode is used or not. In addition, whether BT is adopted or not, CLSC firms prefer competitive recycling mode only when the competitive intensity exceeds a specific threshold. Moreover, choosing an appropriate recycling mode can alleviate the negative impact of BT on the environment, then an all-win result can be obtained for CLSC firms, consumers, and society. These results can give suggestions for managers to optimize their supply chains regarding adopting BT and implementing recycling mode. In the future, we can expand our research on the transfer price of used products, the positive and negative effects of BT, and BT cost-sharing strategies. Full article
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<p>Four models in the CLSC.</p>
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<p>Sequence of events under four models.</p>
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<p>Impact of competitive intensity, BT usage cost, and value discount on manufacturer’s profit.</p>
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<p>Impact of competitive intensity, BT usage cost, and value discount on retailer’s profit.</p>
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<p>Consistent choice for CLSC members.</p>
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<p>Effect of BT usage cost and competitive intensity on consumer surplus.</p>
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<p>Effect of BT usage cost and competitive intensity on consumer surplus.</p>
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<p>Effect of BT usage cost and competitive intensity on environmental impact.</p>
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<p>Effect of BT usage cost and competitive intensity on social welfare.</p>
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28 pages, 4519 KiB  
Article
Evaluating Stakeholders’ Decisions in a Blockchain-Based Recycling Construction Waste Project: A Hybrid Evolutionary Game and System Dynamics Approach
by Yi-Hsin Lin, Jian Wang, Deshuang Niu and Zilefac Ebenezer Nwetlawung
Buildings 2024, 14(7), 2205; https://doi.org/10.3390/buildings14072205 - 17 Jul 2024
Viewed by 711
Abstract
To promote efficient construction waste recycling and reuse, a novel waste management approach based on blockchain technology was introduced to the industry. However, adopting blockchain platforms in construction waste recycling and reuse may impact the behavioral strategies of stakeholders and impede the prediction [...] Read more.
To promote efficient construction waste recycling and reuse, a novel waste management approach based on blockchain technology was introduced to the industry. However, adopting blockchain platforms in construction waste recycling and reuse may impact the behavioral strategies of stakeholders and impede the prediction of the specific impacts of stakeholders’ decisions. Accordingly, this study addresses two primary questions: (1) What are the collaborative framework and the behavioral evolution trends of multiple stakeholders within the context of blockchain? (2) How can the behavioral strategies of multiple stakeholders be systematically coordinated to achieve efficient construction waste recycling and reuse driven by blockchain? To answer these questions, a tripartite game model combined with system dynamics was constructed. In this model, we aimed to elucidate the internal organizational framework, analyze the dynamic evolution process, and assess the influence of decisions made by multiple stakeholders at the individual level. It also offers corresponding policy recommendations for efficient construction waste recycling and reuse driven by blockchain at the system level. This study offers three innovations. First, it considers the decision-making of multiple stakeholders as an interdependent and coevolutionary process to overcome the defects of analyzing only one type of participant. Second, in contrast to the static analysis method, it employs a dynamic system approach to deeply analyze the evolving structures of blockchain-based projects. Third, it provides a theoretical framework for the practical implementation of blockchain-driven platforms in managing construction waste recycling and reuse, thus fostering effective policy development and management practices. This framework aims to promote sustainable development in construction waste recycling and reuse projects in China as well as globally. Full article
(This article belongs to the Section Construction Management, and Computers & Digitization)
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<p>Research flow.</p>
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<p>Functions of the blockchain-based framework construction waste recycling and reuse (CWRR) projects.</p>
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<p>The tripartite game tree between the government, construction enterprises (CEs), and recycling enterprises (REs).</p>
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<p>Tripartite evolutionary game system dynamics (SD) model.</p>
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<p>Simulation diagram of tripartite evolutionary game.</p>
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<p>The simulation of the influence of the intermediate variable L and D<sub>2</sub> on the evolution result.</p>
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<p>The simulation of the influence of the intermediate variable L and D<sub>2</sub> on the evolution result.</p>
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<p>The simulation of the influence of strategies of one party on the evolution result. Panels (<b>a</b>,<b>b</b>) show the simulation of the influence of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> on the probability of government selecting <span class="html-italic">strict supervision</span> strategy <span class="html-italic">x</span>. Panels (<b>c</b>–<b>e</b>) show the simulation of the influence of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on the probability of CEs selecting <span class="html-italic">active recycling</span> strategy <span class="html-italic">y</span>. Panels (<b>f</b>–<b>h</b>) show the simulation of the influence of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo> </mo> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mo> </mo> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">F</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> on the probability of REs selecting <span class="html-italic">participation</span> strategy <span class="html-italic">z</span>.</p>
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<p>Framework for efficient blockchain-based CWRR projects.</p>
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28 pages, 3561 KiB  
Article
How to Reshape the Selection Boundaries between Traditional and Digital Supply Chain Finance Based on the Pledge Rate and Default Loss: Two Tripartite Game Models
by Xiang Sun, Yue Wang, Yinzi Huang and Yue Zhang
Systems 2024, 12(7), 253; https://doi.org/10.3390/systems12070253 - 13 Jul 2024
Cited by 1 | Viewed by 818
Abstract
The development of digital technologies such as blockchain has provided new possibilities for solving the financing difficulties of small and medium-sized enterprises (SMEs). In order to explore the mutual influence of the participants in the supply chain, this paper constructs two static tripartite [...] Read more.
The development of digital technologies such as blockchain has provided new possibilities for solving the financing difficulties of small and medium-sized enterprises (SMEs). In order to explore the mutual influence of the participants in the supply chain, this paper constructs two static tripartite game models for traditional and digital supply chain finance, including a small and medium-sized enterprise (SME), a core enterprise (CE), and a financial institution (FI). The conditions for SME, CE, and FI to participate in digital supply chain finance, and the equilibrium strategy (repayment, repayment, loan) after participating in digital supply chain finance, are figured out. It is found that compared with the traditional supply chain, the digital supply chain expands the probability range of repayment for SME and CE by the change of pledge rate and default loss and broadens the probability range of repayment for CE by the change of default loss. Further, compared with the traditional supply chain finance, the greater the pledge rate of digital supply chain finance and the smaller the default loss, the stronger the willingness of the SME and CE to participate in the digital supply chain and the lower the willingness of the FI. After the three parties participate in the digital supply chain, however, the conclusion is the opposite. The smaller the pledge rate and the greater the default loss, the stronger the repayment willingness for the SME and CE and the stronger the loan willingness of the FI. Therefore, it is suggested to find the critical values of pledge rate and default loss and raise these two variables to an appropriate range to encourage all parties to voluntarily and consistently participate in digital supply chain financing. Full article
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<p>Business flow chart of accounts receivable under digital supply chain.</p>
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<p>Mixed strategy equilibrium between SME and CE in the traditional supply chain.</p>
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<p>Mixed strategy equilibrium between SME and CE in the digital supply chain.</p>
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<p>Impact of the difference in the pledge rate on the willingness of SME to engage in the digital supply chain.</p>
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<p>Impact of the difference in the pledge rate on the willingness of FI to engage in the digital supply chain.</p>
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<p>Impact of the pledge rate on the trustworthiness of SME.</p>
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<p>Impact of the difference in the default losses on the willingness of SME to engage in the digital supply chain.</p>
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<p>Impact of the difference in the default losses on the willingness of CE to engage in the digital supply chain.</p>
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<p>Impact of the default losses on the repayment of CE.</p>
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15 pages, 3658 KiB  
Article
Dynamic Credible Spectrum Sharing Based on Smart Contract in Vehicular Networks
by Qinchi Li, Qin Wang, Haitao Zhao, Tianshui Chang, Yuting Yang and Sisi Xia
Mathematics 2024, 12(13), 1929; https://doi.org/10.3390/math12131929 - 21 Jun 2024
Viewed by 577
Abstract
With the rapid development of the Internet of Vehicles (IoV), the demand for wireless spectrum resources has significantly increased. Dynamic spectrum sharing technology is regarded as a key solution to alleviate the shortage of spectrum resources. However, during the spectrum sharing process, security [...] Read more.
With the rapid development of the Internet of Vehicles (IoV), the demand for wireless spectrum resources has significantly increased. Dynamic spectrum sharing technology is regarded as a key solution to alleviate the shortage of spectrum resources. However, during the spectrum sharing process, security issues and a low utilization of the shared spectrum may arise. This study designs a consortium blockchain for trustworthy dynamic spectrum sharing in IoV environments. An improved asynchronous byzantine fault-tolerant algorithm is also designed to address the instability of signals in this scenario, and the allocation and management of spectrum resources between vehicles and base stations are further optimized using the Stackelberg game, ultimately deployed automatically through smart contracts. Simulation results show that our method not only significantly improves the system’s response time but also ensures communication quality and can maintain efficient operation under high network delay and complex scenarios. Full article
(This article belongs to the Special Issue Advances in Communication Systems, IoT and Blockchain)
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<p>System model diagram in vehicle networking scenario.</p>
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<p>Stackelberg gaming process in spectrum trading.</p>
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<p>Consensus time comparison with different delay and node count.</p>
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<p>Node failure consensus time comparison with different node count and failure rate.</p>
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<p>Bandwidth allocation for different channels with iteration times in Stackelberg game.</p>
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<p>Profit for seller/buyer with iteration times in Stackelberg game.</p>
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<p>Bandwidth allocation under different noise power in Stackelberg game.</p>
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<p>Bandwidth allocation under different disturbance in Stackelberg game.</p>
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20 pages, 14864 KiB  
Article
Uncovering the Research Hotspots in Supply Chain Risk Management from 2004 to 2023: A Bibliometric Analysis
by Tianyi Ding and Zongsheng Huang
Sustainability 2024, 16(12), 5261; https://doi.org/10.3390/su16125261 - 20 Jun 2024
Viewed by 3106
Abstract
As globalization deepens, factors such as the COVID-19 pandemic and geopolitical tensions have intricately complexified supply chain risks, underscoring the escalating significance of adept risk management. This study elucidates the evolution, pivotal research foci, and emergent trends in supply chain risk management over [...] Read more.
As globalization deepens, factors such as the COVID-19 pandemic and geopolitical tensions have intricately complexified supply chain risks, underscoring the escalating significance of adept risk management. This study elucidates the evolution, pivotal research foci, and emergent trends in supply chain risk management over the past two decades through a multifaceted lens. Utilizing bibliometric tools CiteSpace and HistCite, we dissected the historical contours, dynamic topics, and novel trends within this domain. Our findings reveal a sustained fervor in research activity, marked by extensive scientific collaboration over the past 20 years. Distinct research hotspots have surfaced intermittently, featuring 20 domains, 62 keywords, and 60 citation bursts. Keyword clustering identified seven nascent research subfields, including stochastic planning, game theory, and risk management strategies. Furthermore, reference clustering pinpointed five contemporary focal areas: robust optimization, supply chain resilience, blockchain technology, supply chain finance, and Industry 4.0. This review delineates the scholarly landscape from 2004 to 2023, uncovering the latest research hotspots and developmental trajectories in supply chain risk management through a bibliometric analysis. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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<p>The number of publications by year and journal. (Note: In (<b>a</b>) the red line represents the yearly publication number and the black line represents the deming linear fitting curve; (<b>b</b>) The blue bar represents the number of journal publications).</p>
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<p>Publication citation chart (color bar from right (white) to left (red) represents 2004 to 2023). (Note: Nodes represent different co-cited literature, larger nodes represent higher number of co-citations, the color from white to red represents the time from 2004 to 2023, the line segments between the nodes represent collaborations, and the purple color in the outer circle represents the high centrality).</p>
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<p>(<b>a</b>) National cooperation network. (<b>b</b>) Institutional cooperation network. (<b>c</b>) Author cooperation network. (Note: In subfigures (<b>a</b>,<b>b</b>) nodes represent different countries/institutions, larger nodes represent higher number of publications, the color from white to red represents the time from 2004 to 2023, the line segments between the nodes represent collaborative relationships, and the purple color in the outer circle represents high centrality; In (<b>c</b>) different nodes represent different authors, the size of the dots represents the number of publications, the line connecting the dots represents the collaboration, the width of the line represents the intensity of the collaboration, and different colors represent different clusters).</p>
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<p>Top 50 most cited subject categories. (The blue line indicates the timeline, the red part of the blue timeline indicates the interval between the discovery of the outbreak period, indicating the start year, the end year and the period of the outbreak, and the name on the left indicates the subject category).</p>
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<p>Top 50 keyword emergence. (The blue line indicates the timeline, the red part of the blue timeline indicates the interval between the discovery of the outbreak period, indicating the start year, the end year and the period of the outbreak, and the name on the left indicates the keyword bursts).</p>
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<p>Keyword clustering. (Note: from (<b>a</b>–<b>d</b>) represent the time period from 2004–2023, different colors represent different clusters, and colors from red to pink represent clusters from 0 to 7).</p>
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<p>(<b>a</b>) The timeline chart of cited documents. (<b>b</b>) The citation dynamics of these 8 publications. (Note: (<b>a</b>) Nodes represent different co-cited literature, larger nodes represent more co-citations, the color from white to red represents the time from 2004 to 2023, the line segments between the nodes represent the co-citation relationship, the purple color in the outer circle represents high centrality, and the labels on the right hand side of the graph represent different clusters; (<b>b</b>) The different lines represent the most co-cited documents in clusters 0, 1, 4, 6, 7, 8, 10, and 11, respectively, and the change in the line represents the change in the co-citation count per year since the publication of the document).</p>
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21 pages, 3071 KiB  
Article
Blockchain and Supply-Chain Financing: An Evolutionary Game Approach with Guarantee Considerations
by Jizhou Zhan, Gewei Zhang, Heap-Yih Chong and Xiangfeng Chen
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1616-1636; https://doi.org/10.3390/jtaer19020079 - 19 Jun 2024
Viewed by 1216
Abstract
Blockchain technology enables innovative financing models in supply-chain finance. This research constructs a tripartite evolutionary game model that includes core enterprises as employers, small- and medium-sized enterprises (SMEs) as contractors, and banks as financial institutions, where they have been simulated for their impact [...] Read more.
Blockchain technology enables innovative financing models in supply-chain finance. This research constructs a tripartite evolutionary game model that includes core enterprises as employers, small- and medium-sized enterprises (SMEs) as contractors, and banks as financial institutions, where they have been simulated for their impact on blockchain technology, especially on the strategic choices of supply-chain financing behavior and the system’s evolutionary path under core enterprises’ guarantee mechanism. The findings show the application of blockchain technology can effectively reduce the regulatory and review costs for financial institutions, thereby enhancing the efficiency of supply-chain financing. Particularly, blockchain technology provides a more reliable credit endorsement platform for SMEs in reducing their tendency to default. The guarantee mechanism of core enterprises is more effective with the support of blockchain technology, which helps to build more solid supply-chain financial cooperation relationships. The research contributes to the theoretical research on the integration of blockchain technology into supply-chain finance, especially for improving the operational efficiency of financial services. It also highlights the need for blockchain-backed guarantees from core enterprises in optimizing supply-chain financial services. Full article
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<p>The financing process of blockchain-enabled SCF.</p>
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<p>Tri-party game tree.</p>
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<p>The phase diagram of the core enterprise’s strategy evolution.</p>
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<p>The phase diagram of the SME’s strategy evolution.</p>
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<p>The phase diagram of financial institution’s strategy evolution.</p>
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<p>The evolutionary path of equilibrium point <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>(</mo> <mn>0,0</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>The evolutionary path of the equilibrium point <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mn>5</mn> </mrow> </msub> <mo>(</mo> <mn>1,1</mn> <mo>,</mo> <mn>0</mn> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>The evolutionary path of equilibrium point <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mn>8</mn> </mrow> </msub> <mo>(</mo> <mn>1,1</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math>.</p>
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<p>The impact of blockchain construction cost on evolutionary game strategies.</p>
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<p>The impact of SME’s default benefits on evolutionary game strategies.</p>
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<p>The impact of core enterprises’ guarantee benefits on evolutionary game strategies.</p>
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