Search Results (418)

This paper aims to investigate a Nash equilibrium (NE)-seeking approach for the aggregative game problem of second-order multi-agent systems (MAS) with uncontrollable malicious players, which may cause the decisions of global players to become uncontrollable, thereby hindering the ability of normal players to reach the NE. To mitigate the influence of malicious players on the system, a malicious player detection and disconnection (MPDD) algorithm is proposed, based on the fixed-time convergence method. Subsequently, a predefined-time distributed NE-seeking algorithm is presented, utilizing a time-varying, time-based generator (TBG) and state-feedback scheme, ensuring that all normal players complete the game problem within the predefined time. The convergence properties of the algorithms are analyzed using Lyapunov stability theory. Theoretically, the aggregative game problem with malicious players can be solved using the proposed algorithms within any user-defined time. Finally, a numerical simulation of electricity market bidding verifies the effectiveness of the proposed algorithm. Full article

The COVID-19 pandemic fundamentally disrupted assumptions about entrepreneurship, career planning, and professional development. This study explored how the pandemic influenced the entrepreneurial intentions of undergraduate business students in the United States. Using a qualitative methodology based on in-depth interviews with 31 students at a public Midwestern university, the research interpreted student narratives through the lenses of effectuation theory, resilience theory, and the theory of planned behavior. Findings revealed that many participants reframed entrepreneurship as a strategy for navigating economic uncertainty and enhancing personal agency. Students reported valuing adaptability, resourcefulness, and opportunity recognition, often experimenting with side hustles during the pandemic as a means of resilience. Their entrepreneurial thinking shifted from purely economic motivations toward aspirations for flexibility, self-fulfillment, and purposeful work. The study highlights the formative role of crisis contexts in shaping entrepreneurial identity among emerging professionals. It suggests that entrepreneurship education should move beyond traditional models, fostering skills for navigating complexity and building resilience. In doing so, the findings contribute to broader conversations about youth entrepreneurship, post-pandemic career development, and the evolving demands of the labor market in times of disruption. Full article

The rapid rise of the silver economy in the new era has become a new driving force for socio-economic development. From the perspective of Marxist political economy theory, this paper analyzes the intrinsic logic of the silver economy’s development through three dimensions: surplus value, labor market, and capital. The study finds that the silver economy in the new era faces challenges such as insufficient supply of high-quality elderly care services, simultaneous shortages in both total talent quantity and structural imbalances, and contradictions between capital’s profit-seeking nature and social welfare. By introducing the multiple streams model, the paper elucidates the coupling process of these three streams and the timing of policy window openings. It proposes targeted strategies, including strengthening technological innovation, deepening labor market reforms, and optimizing capital allocation, to promote the robust development of China’s silver economy and inject strong momentum into sustainable and high-quality economic growth. Full article

Grounded in coupling theory, this study investigates the interplay among three key elements of economic growth, namely the digital economy, carbon emissions efficiency, and high-quality economic development. Drawing on data from 30 Chinese provinces from 2000 to 2023, we employ exploratory spatiotemporal data analysis and the GeoDetector model to examine the spatial–temporal evolution and underlying driving forces of coupling coordination. This research enriches the theoretical framework of multi-system synergistic development in a green transition context and offers empirical insights and policy recommendations for fostering regional coordination and sustainable development. The results reveal that (1) both the digital economy and high-quality economic development show a steady upward trend, while carbon emissions efficiency has a “U-shaped” curve pattern; (2) at the national level, the degree of coupling coordination has evolved over time from “mild disorder” to “on the verge of disorder” to “barely coordinated,” while at the regional level, this pattern of coupling coordination shifts over time from “Eastern–Northeastern–Central–Western” to “Eastern–Central–Northeastern–Western”; (3) although spatial polarization in coupling coordination has improved, disparities fluctuate in a “decline–rise” pattern, with interregional differences being the main source of that variation; (4) the degree of coupling coordination has a positive spatial correlation, but with a declining trend with fluctuations; and (5) improvements in the level of economic development, human capital, industrial structure, green technological innovation, and market development capacity all contribute positively to coupling coordination. Among them, green technological innovation and market development capacity are the most influential drivers, and the interactions among all driving factors further enhance their collective impact. Full article

As China’s economy grows and travel demand increases, its aviation market has evolved to become the second-largest in the world. This study presents a pioneering analysis of China’s aviation network evolution (1990–2024) by integrating temporal dynamics into a network density matrix theory, addressing critical gaps in prior static network analyses. Unlike conventional studies focusing on isolated topological metrics, we introduce a triangulated methodology: ① a network sequence analysis capturing structural shifts in degree distribution, clustering coefficient, and path length; ② novel redundancy–entropy coupling quantifying complexity evolution beyond traditional efficiency metrics; and ③ economic-network coordination modeling with spatial autocorrelation validation. Key innovations reveal previously unrecognized dynamics: ① Time-embedded density matrices ($\rho$) demonstrate how sparsity balances information propagation efficiency ($\eta$) and response diversity, resolving the paradox of functional yet sparse connectivity. ② Redundancy–entropy synergy exposes adaptive trade-offs. Entropy ($H$) rises 18% (2000–2024), while redundancy ($R$) rebounds post-2010 (0.25→0.33), reflecting the strategic resilience enhancement after early efficiency-focused phases. ③ Economic-network coupling exhibits strong spatial autocorrelation ($Moran’sI>0.16$, $p<0.05$), with eastern China achieving “primary coordination”, while western regions lag due to geographical constraints. The empirical results confirm structural self-organization. Power-law strengthening, route growth exponentially outpacing cities, and clustering ($C$) rising 16% as the path length ($L$) increases, validating the hierarchical hub formation. These findings establish aviation networks as dynamically optimized systems where economic policies and topological laws interactively drive evolution, offering a paradigm shift from descriptive to predictive network management. Full article

Accurate carbon market price prediction is crucial for promoting a low-carbon economy and sustainable engineering. Traditional models often face challenges in effectively capturing the multifractality inherent in carbon market prices. Inspired by the self-similarity and scale invariance inherent in fractal structures, this study proposes a novel multifractal-aware model, MF-Transformer-DEC, for carbon market price prediction. The multi-scale convolution (MSC) module employs multi-layer dilated convolutions constrained by shared convolution kernel weights to construct a scale-invariant convolutional network. By projecting and reconstructing time series data within a multi-scale fractal space, MSC enhances the model’s ability to adapt to complex nonlinear fluctuations while significantly suppressing noise interference. The fractal attention (FA) module calculates similarity matrices within a multi-scale feature space through multi-head attention, adaptively integrating multifractal market dynamics and implicit associations. The dynamic error correction (DEC) module models error commonality through variational autoencoder (VAE), and uncertainty-guided dynamic weighting achieves robust error correction. The proposed model achieved an average R² of 0.9777 and 0.9942 for 7-step ahead predictions on the Shanghai and Guangdong carbon price datasets, respectively. This study pioneers the interdisciplinary integration of fractal theory and artificial intelligence methods for complex engineering analysis, enhancing the accuracy of carbon market price prediction. The proposed technical pathway of “multi-scale deconstruction and similarity mining” offers a valuable reference for AI-driven fractal modeling. Full article

From a space-time perspective, this paper constructs a CA-SHIRS model to study the psychosocial resonance diffusion of food safety risk, using complex network and cellular automata theory. The CA-SHIRS model is a framework that combines cellular automata with SHIRS (Susceptible–Hidden–Infected–Recovered–Susceptible) epidemic modeling. This methodological integration can effectively reflect local interactions and spatial distribution among consumers. Furthermore, this paper analyzes the diffusion mechanism and spatial–temporal evolution of the psychosocial resonance of food safety risk, considering the interaction between consumer heterogeneity and media communication strategies. The primary conclusions are outlined as follows: (1) An increase in infection probability, conversion probability, and immune failure probability causes the psychosocial resonance of food safety risk to spread rapidly across different regions and populations. In contrast, an increase in immune probability helps control the psychosocial resonance of food safety risk. (2) The diffusion threshold of the psychosocial resonance of food safety risk is negatively related to the consumer risk perception level, consumer risk attention, media freedom, and media report authenticity. However, it is positively correlated with consumer sentiment, market noise, and media report tendency. (3) The consumer risk perception level, consumer risk attention, media freedom, and media report authenticity can effectively inhibit the spatial–temporal diffusion of the psychosocial resonance of food safety risk. On the other hand, increases in market noise, consumer sentiment, and media report tendency accelerate the spread of the psychosocial resonance of food safety risk across different regions. Full article

Applying Field Programmable Gate Array (FPGA) technology in cloud infrastructure and heterogeneous computations is of great interest today. FPGA as a Service assumes that the programmable logic device (PLD) is used as a remote (available over the Internet) service with an FPGA silicon chip on board. During the prototyping of FPGA-based projects within modern design flow, it is necessary to consider the processing delays caused by various factors, including the delay of data transfer between the kernel and host computer, limited clock frequency, and multiple parallel-running FPGA accelerator cards. To address these challenges, three techniques are proposed to reduce the required modification efforts and improve project performance. Based on the proposed models, the analytical evaluation of the functioning process of FPGA as a Service is performed to determine possibilities of improving productivity and reducing the response time. The practical experience of porting FPGA projects to new integrated environments is considered. The evaluation of the response time of FPGA as a Service using the queueing theory is proposed. It is shown that scaling and parallelization at the top level of project hierarchy, pipelining, and parameterization allow for the effective deployment of such FPGA systems for data centers and cloud infrastructures. The proposed techniques and models allow for an evaluation of the performance and response time of FPGA as a Service for formulating recommendations to improve technical characteristics. Full article

We studied decades-long (1980 to 2024) historic distributions of accumulated S&P500 returns, from daily returns to those over several weeks. The time series of the returns emphasize major upheavals in the markets—Black Monday, Tech Bubble, Financial Crisis, and the COVID pandemic—which are reflected in the tail ends of the distributions. De-trending the overall gain, we concentrated on comparing distributions of gains and losses. Specifically, we compared the tails of the distributions, which are believed to exhibit a power-law behavior and possibly contain outliers. To this end, we determined confidence intervals of the linear fits of the tails of the complementary cumulative distribution functions on a log–log scale and conducted a statistical U-test in order to detect outliers. We also studied probability density functions of the full distributions of the returns with an emphasis on their asymmetry. The key empirical observations are that the mean of de-trended distributions increases near-linearly with the number of days of accumulation while the overall skew is negative—consistent with the heavier tails of losses—and depends little on the number of days of accumulation. At the same time, the variance of the distributions exhibits near-perfect linear dependence on the number of days of accumulation; that is, it remains constant if scaled to the latter. Finally, we discuss the theoretical framework for understanding accumulated returns. Our main conclusion is that the current state of theory, which predicts symmetric or near-symmetric distributions of returns, cannot explain the aggregate of empirical results. Full article

This study examines the daily top 100 cryptocurrencies on Uniswap V3. It denoises the correlation coefficient matrix of cryptocurrencies by using sliding window techniques and random matrix theory. Further, this study constructs a time-varying correlation network of cryptocurrencies under different thresholds based on complex network methods and analyzes the Uniswap V3 network’s time-varying topological properties and risk contagion intensity of Uniswap V3. The study findings suggest the presence of random noise on the Uniswap V3 cryptocurrency market. The strength of connection relationships in cryptocurrency networks varies at different thresholds. With a low threshold, the cryptocurrency network shows high average degree and average clustering coefficient, indicating a small-world effect. Conversely, at a high threshold, the cryptocurrency network appears relatively sparse. Moreover, the Uniswap V3 cryptocurrency network demonstrates heterogeneity. Additionally, cryptocurrency networks exhibit diverse local time-varying characteristics depending on the thresholds. Notably, with a low threshold, the local time-varying characteristics of the network become more stable. Furthermore, risk contagion analysis reveals that WETH (Wrapped Ether) exhibits the highest contagion intensity, indicating its predominant role in propagating risks across the Uniswap V3 network. The novelty of this study lies in its capture of time-varying characteristics in decentralized exchange network topologies, unveiling dynamic evolution patterns in cryptocurrency correlation structures. Full article

In the context of global climate governance and the International Maritime Organization’s (IMO) stringent carbon reduction targets, the transition to green shipping fuels faces systemic challenges in supply chain coordination. This study focuses on the strategic interactions between governments and enterprises in the construction of green fuel supply chains. By constructing a multidimensional scenario framework encompassing time, technological development, social attention, policy intensity, and market competition, and using evolutionary game models and system dynamics simulations, we reveal the dynamic evolution mechanism of government–enterprise decision making. System dynamics simulations reveal that (1) short-term government intervention accelerates infrastructure development but risks subsidy inefficiency; (2) medium-term policy stability and market-driven mechanisms are critical for sustaining enterprise investments; and (3) high social awareness and mature technologies significantly reduce strategic uncertainty. This research advances the application of evolutionary game theory in sustainable supply chains and offers a decision support framework for balancing governmental roles and market forces in maritime decarbonization. Full article

This study examines the price dynamics of the UK Emission Trading Scheme (UK ETS) by integrating advanced computational methods, including deep learning and statistical modelling, to analyze and simulate carbon market behaviour. By analyzing long-memory effects and price volatility, it assesses whether UK carbon prices align with theoretical expectations from carbon pricing mechanisms and market efficiency theories. Findings indicate that UK carbon prices exhibit persistent long-memory effects, contradicting the Efficient Market Hypothesis, which assumes price movements are random and fully reflect available information. Furthermore, regulatory interventions exert significant downward pressure on prices, suggesting that policy uncertainty disrupts price equilibrium in cap-and-trade markets. Deep learning models, such as Time-series Generative Adversarial Networks (TGANs) and adjusted fractional Brownian motion, outperform traditional approaches in capturing price dependencies but are prone to overfitting, highlighting trade-offs in AI-based forecasting for carbon markets. These results underscore the need for predictable regulatory frameworks, hybrid pricing mechanisms, and data-driven approaches to enhance market efficiency. By integrating empirical findings with economic theory, this study contributes to the carbon finance literature and provides insights for policymakers on improving the stability and effectiveness of emissions trading systems. Full article

The rapid growth of digital platforms has fundamentally reshaped network and viral marketing, profoundly transforming how information spreads across social networks and influences consumer behavior. This comprehensive review synthesizes theoretical, computational, and ethical perspectives into an integrated narrative, providing novel insights into the mechanisms driving information diffusion within contemporary interactive marketing. By integrating foundational concepts from social network theory, advanced graph models, and behavioral dynamics, the paper demonstrates how the interplay between network structures, influencer behaviors, and AI-driven algorithms significantly redefines traditional marketing paradigms. A distinctive theoretical contribution of this study lies in its innovative combination of Big Data analytics with AI-based predictive modeling, explicitly revealing how real-time algorithmic personalization not only enhances marketing effectiveness but also creates new ethical tensions surrounding misinformation, algorithmic bias, and consumer vulnerability. Addressing recent calls for greater theoretical originality and narrative coherence in interactive marketing research, this review explicitly highlights how these insights resolve critical theoretical puzzles and clarify contemporary ethical dilemmas. Additionally, the paper identifies emerging trends—including Web3 marketing, decentralized platforms, and neuroscience-driven targeting—offering clear future research directions. Through its integrative, narrative-driven framework, this study significantly advances interactive marketing theory, providing essential guidance for scholars and practitioners navigating the evolving complexities of digital influence. Full article

As an essential chemical raw material and a cost-effective energy product, fluctuations in propane price has garnered significant attention in the energy market. This paper processes the original time series using a coarse-grained method and employs symbolic representation combined with the sliding window technique to represent fluctuation modes as nodes within a network. The weight and direction of the edges among the nodes are determined by the number and direction of the conversions among the modes, thereby mapping the original sequence of the propane price into the propane price oriented weighted network (PPOWN) by the symbolic patterns, which is an asymmetric network that has evolved from the symmetric network based on symmetry theory. The results indicate that the core fluctuation state of the PPOWN is concentrated in the first 0.96% of the nodes, exhibiting scale-free network characteristics and dynamic asymmetry. Nodes with greater strength are more closely interconnected, but not all early-appearing nodes possess great strength. The PPOWN demonstrates a short-range correlation ($\overline{L}=8.5405$) and a highly linear growth trend in the cumulative time interval of the new nodes. Additionally, the nodes of the PPOWN display low betweenness, clustering coefficient, and strength, which significantly differ from the random and chaotic networks. The presence of these lower-strength nodes often signifies that the market is undergoing a transformation or transition period. By identifying and analyzing these nodes, subsequent propane price fluctuations can be predicted more effectively, enhancing market responsiveness. Full article

In recent years, corporate ESG performance has been widely incorporated into investment decisions and capital allocation considerations, becoming a focal point and hot topic for research by governments and organizations worldwide. However, due to various reasons, significant discrepancies have emerged in ESG ratings for the same company across different institutions, and this growing divergence in ESG ratings has increasingly drawn the attention of scholars. Studying the differences in ESG (environmental, social, and corporate governance) ratings is of great significance. This not only helps to understand the root causes of differences, improve the objectivity, consistency, and comparability of ratings, but also helps users better understand the meaning and limitations of rating results. It is beneficial for investors to understand the focus of different ratings and develop more effective investment strategies. It can promote rated companies to improve the quality and transparency of ESG-related information disclosure. It can also provide a reference for regulatory agencies and policymakers, identify market failures and potential risks, and promote the development of more unified standards and frameworks. At the same time, this study can also promote the in-depth development of relevant academic research and theories. Based on this, this study systematically reviews the relevant literature on ESG rating divergence, focusing on its existence, causes, influencing factors, and impacts. The study finds that, in addition to the widespread existence of rating divergence in corporate ESG performance, scholars also disagree on the measurement and methods of this divergence. The reasons for rating divergence are mainly that ESG is a qualitative indicator; top-level design, intermediate calculations, and bottom-level data collection across multiple stages exacerbate divergence; and controversies in practice further deepen divergence, among others. The influencing factors and impact effects of ESG rating divergence are diverse. Given the existence of ESG rating divergence, all parties should treat ESG ratings with caution. This paper offers corresponding recommendations and looks forward to the future, providing a foundation for subsequent research. Full article