Search Results (528)

This study investigates how new R&D institutions mediate policy–market disjunctures to foster integrated innovation and entrepreneurship ecosystems. Employing a longitudinal case analysis (2013–2023) of the Jiangsu Industrial Technology Research Institute (JITRI), we delineate a three-phase evolutionary process: (1) an initial government-dominated phase, stimulating foundational capability development through contract R&D; (2) a subsequent marketization phase, enabling systemic resource integration via co-creation centers and global networks; and (3) a culminating synergy phase, where policy–market alignment facilitates ecosystem optimization through crowdsourced R&D and cross-domain collaboration. Three core mechanisms underpin this adaptation: policy–market coupling (providing external momentum), endogenous capability development (absorption to innovation), and dynamic resource orchestration (acquisition to optimization). JITRI’s hybrid governance model demonstrates that stage-contingent interventions—specifically, policy anchoring in early stages followed by market-responsive resource allocation—effectively transmute inherent tensions into productive synergies. These findings yield implementable frameworks for structuring innovative ecosystems and underscore the necessity for comparative studies to establish broader theoretical generalizability. Full article

This study introduces a novel tripartite evolutionary game model to analyze the strategic interactions among electric vehicle (EV) aggregators, local governments, and EV users in vehicle–grid interaction (VGI) markets. The core novelty lies in capturing bounded rationality and dynamic decision-making across the three stakeholders, revealing how policy incentives and market mechanisms drive the transition from disordered charging to bidirectional VGI. Key findings include the following: (1) The system exhibits five stable equilibrium points, corresponding to three distinct developmental phases of the VGI market: disordered charging (V0G), unidirectional VGI (V1G), and bidirectional VGI (V2G). (2) Peak–valley price differences are the primary driver for transitioning from V0G to V1G. (3) EV aggregators’ willingness to adopt V2G is influenced by upgrade costs, while local governments’ subsidy strategies depend on peak-shaving benefits and regulatory costs. (4) Increasing the subsidy differential between V1G and V2G accelerates market evolution toward V2G. The framework offers actionable policy insights for sustainable VGI development, while advancing evolutionary game theory applications in energy systems. Full article

The rapid evolution of SARS-CoV-2 has underscored the need for a detailed understanding of antibody binding mechanisms to combat immune evasion by emerging variants. In this study, we investigated the interactions between Class I neutralizing antibodies—BD55-1205, BD-604, OMI-42, P5S-1H1, and P5S-2B10—and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein using multiscale modeling, which combined molecular simulations with the ensemble-based mutational scanning of the binding interfaces and binding free energy computations. A central theme emerging from this work is that the unique binding strength and resilience to immune escape of the BD55-1205 antibody are determined by leveraging a broad epitope footprint and distributed hotspot architecture, additionally supported by backbone-mediated specific interactions, which are less sensitive to amino acid substitutions and together enable exceptional tolerance to mutational escape. In contrast, BD-604 and OMI-42 exhibit localized binding modes with strong dependence on side-chain interactions, rendering them particularly vulnerable to escape mutations at K417N, L455M, F456L and A475V. Similarly, P5S-1H1 and P5S-2B10 display intermediate behavior—effective in some contexts but increasingly susceptible to antigenic drift due to narrower epitope coverage and concentrated hotspots. Our computational predictions show strong agreement with experimental deep mutational scanning data, validating the accuracy of the models and reinforcing the value of binding hotspot mapping in predicting antibody vulnerability. This work highlights that neutralization breadth and durability are not solely dictated by epitope location, but also by how binding energy is distributed across the interface. The results provide atomistic insight into mechanisms driving resilience to immune escape for broadly neutralizing antibodies targeting the ACE2 binding interface—which stems from cumulative effects of structural diversity in binding contacts, redundancy in interaction patterns and reduced vulnerability to mutation-prone positions. Full article

Salt stress severely limits the growth and ornamental value of pecan (Carya illinoinensis) in salinized regions, yet the transcriptional mechanisms underlying its stress adaptation remain unclear. In this study, a comprehensive genomic analysis of the GRAS transcription factor family identified 58 CiGRAS genes in pecan. These genes were classified into 11 subfamilies and showed conserved motifs and gene structures, with variation in promoter cis-elements suggesting diverse regulatory functions. Chromosomal distribution and duplication analysis indicated that whole-genome and dispersed duplication events were the main drivers of CiGRAS expansion. Transcriptome data revealed tissue-specific expression and strong responsiveness to salt and other stresses. Under 0.6% NaCl treatment, several CiGRAS genes were significantly upregulated, especially at 48 h. Gene co-expression analysis further highlighted GRAS-enriched modules associated with redox regulation and stress signaling. qRT-PCR validation confirmed time-specific induction of seven CiGRAS genes under salt stress. These findings provide insights into the evolutionary dynamics and stress-related roles of CiGRAS genes and offer candidate regulators for improving pecan salt tolerance in ecological greening and landscape applications. Full article

AI-powered health assistants offer promising opportunities to enhance health management among older adults. However, real-world uptake remains limited, not only due to individual hesitation, but also because of complex interactions among users, platforms, and public policies. This study investigates the dynamic behavioral mechanisms behind adoption in aging populations using a tripartite evolutionary game model. Based on replicator dynamics, the model simulates the strategic behaviors of older adults, platforms, and government. It identifies evolutionarily stable strategies, examines convergence patterns, and evaluates parameter sensitivity through a Jacobian matrix analysis. Results show that when adoption costs are high, platform trust is low, and government support is limited, the system tends to converge to a low-adoption equilibrium with poor service quality. In contrast, sufficient policy incentives, platform investment, and user trust can shift the system toward a high-adoption state. Trust coefficients and incentive intensity are especially influential in shaping system dynamics. This study proposes a novel framework for understanding the co-evolution of trust, service optimization, and institutional support. It emphasizes the importance of coordinated trust-building strategies and layered policy incentives to promote sustainable engagement with AI health technologies in aging societies. Full article

The nitrogen cycle (N-cycle) is a cornerstone of global biogeochemistry, regulating nitrogen availability and affecting atmospheric chemistry, agricultural productivity, and ecological balance. Central to this cycle is the reversible interplay between nitrate (NO₃⁻) and nitrite (NO₂⁻), mediated by molybdenum-dependent enzymes—Nitrate reductases (NARs) and Nitrite oxidoreductases (NXRs). Despite catalyzing opposite reactions, these enzymes exhibit remarkable structural and mechanistic similarities. This review aims to elucidate the molecular underpinnings of nitrate reduction and nitrite oxidation by dissecting their enzymatic architectures, redox mechanisms, and evolutionary relationships. By focusing on recent structural, spectroscopic, and thermodynamic data, we explore how these two enzyme families represent “two sides of the same coin” in microbial nitrogen metabolism. Special emphasis is placed on the role of oxygen atom transfer (OAT) as a unifying mechanistic principle, the influence of environmental redox conditions, and the emerging evidence of bidirectional catalytic potential. Understanding this dynamic enzymatic interconversion provides insight into the flexibility and resilience of nitrogen-transforming pathways, with implications for environmental management, biotechnology, and synthetic biology. Full article

The digital economy presents multiple challenges to the promotion of green technologies, including behavioral uncertainty among firms, heterogeneous technological choices, and disparities in policy incentive strength. This study develops a tripartite evolutionary game model encompassing government, production enterprises, and technology suppliers to systematically explore the strategic evolution mechanisms underlying green technology adoption. A three-dimensional nonlinear dynamic system is constructed using replicator dynamics, and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is applied to identify key cost and benefit parameters for firms. Simulation results exhibit a strong match between the estimated parameters and simulated data, highlighting the model’s identifiability and explanatory capacity. In addition, the stability of eight pure strategy equilibrium points is examined through Jacobian analysis, revealing the evolutionary trajectories and local stability features across various strategic configurations. These findings offer theoretical guidance for optimizing green policy design and identifying behavioral pathways, while establishing a foundation for data-driven modeling of dynamic evolutionary processes. Full article

Urban vegetation shows significant spatial differences due to the combined effects of natural and human factors, yet fine-scale evolutionary patterns and their cross-scale feedback mechanisms remain limited. This study focuses on the Yangtze River Delta (YRD), the top economic area in China. By integrating data from multiple Landsat sensors, we built a high—resolution framework to track vegetation dynamics from 1990 to 2020. It generates annual 30-m Enhanced Vegetation Index (EVI) data and uses a new Vegetation Green—Brown Balance Index (VBI) to measure changes between greening and browning. We combined Mann-Kendall trend analysis with machine—learning based attribution analysis to look into vegetation changes across different city types and urban—rural gradients. Over 30 years, the YRD’s annual EVI increased by 0.015/10 a, with greening areas 3.07 times larger than browning. Spatially, urban centers show strong greening, while peri—urban areas experience remarkable browning. Vegetation changes showed a city-size effect: larger cities had higher browning proportions but stronger urban cores’ greening trends. Cluster analysis finds four main evolution types, showing imbalances in grey—green infrastructure allocation. Vegetation baseline in 1990 is the main factor driving the long-term trend of vegetation greenness, while socioeconomic and climate drivers have different impacts depending on city size and position on the urban—rural continuum. In areas with low urbanization levels, climate factors matter more than human factors. These multi-scale patterns challenge traditional urban greening ideas, highlighting the need for vegetation governance that adapts to specific spatial conditions and city—unique evolution paths. Full article

Credit supervision of practitioners in the water conservancy construction market, a vital pillar of national infrastructure development, significantly impacts project safety and the maintenance of order in the industry. From the perspective of indirect supervision, this study constructs a tripartite evolutionary game model involving government departments, enterprises, and practitioners to analyze the dynamic evolution mechanism of credit supervision. By examining the strategic interactions among the three parties under different regulatory scenarios, we identify key factors influencing the stable equilibrium of evolution and verify the theoretical conclusions through numerical simulations. The study yields several key insights. First, while government regulation and social supervision can substantially increase the likelihood of practitioners’ integrity, relying solely on administrative regulation has an efficiency limit. Second, the effectiveness of the reward and punishment mechanism of the direct manager plays a crucial leveraging role in credit evolution. Lastly, under differentiated regulatory strategies, high-credit practitioners respond more strongly to long-term cost optimization, while low-credit practitioners are more effectively deterred by short-term, high-intensity disciplinary actions. Based on these findings, this study proposes a systematic governance framework of “regulatory model innovation–corporate responsibility enhancement–social supervision deepening.” Unlike previous studies, this framework adopts a comprehensive approach from three dimensions: regulatory model innovation, corporate responsibility enhancement, and social supervision deepening. It offers a more holistic and systematic solution for refining the credit system in the water conservancy construction market, providing both theoretical support and practical approaches. Full article

In China, the recycling system for waste tires is characterized by high output but low standardized recovery rates. This study examines the environmental and health risks caused by non-compliant treatment by individual recyclers and explores the barriers to the large-scale adoption of Pyrolysis Technology. A Tripartite Evolutionary Game Model involving pyrolysis plants, waste tire recyclers, and government regulators is developed. The model incorporates pollutants from pretreatment and pyrolysis processes into a unified metric—Carbon Dioxide Equivalent (CO₂-eq)—based on Global Warming Potential (GWP), and designs a Differential Carbon Taxation mechanism accordingly. The strategy dynamics and stability conditions for Evolutionary Stable Strategies (ESS) are analyzed. Multi-scenario numerical simulations explore how key parameter changes influence evolutionary trajectories and equilibrium outcomes. Six typical equilibrium states are identified, along with the critical conditions for achieving environmentally friendly results. Based on theoretical analysis and simulation results, targeted policy recommendations are proposed to promote standardized waste tire pyrolysis: (1) Establish a phased dynamic carbon tax with supporting subsidies; (2) Build a green market cultivation and price stabilization system; (3) Implement performance-based differential incentives; (4) Strengthen coordination between central environmental inspections and local carbon tax enforcement. Full article

Capsicum (pepper) is an economically vital genus in the Solanaceae family, with most species possessing about 3 Gb genomes. However, the recently sequenced Capsicum rhomboideum (~1.7 Gb) represents the first reported case of an extremely compact genome in Capsicum, providing a unique and ideal model for studying genome size evolution. To elucidate the mechanisms driving this variation, we performed comparative genomic analyses between the compact Capsicum rhomboideum and the reference Capsicum annuum cv. CM334 (~2.9 Gb). Although their genome size differences initially suggested whole-genome duplication (WGD) as a potential driver, both species shared two ancient WGD events with identical timing, predating their divergence and thus ruling out WGD as a direct contributor to their size difference. Instead, transposable elements (TEs), particularly long terminal repeat retrotransposons (LTR-RTs), emerged as the dominant force shaping genome size variation. Genome size strongly correlated with LTR-RT abundance, and multiple LTR-RT burst events aligned with major phases of genome expansion. Notably, the integrity and transcriptional activity of LTR-RTs decline over evolutionary time; older insertions exhibit greater structural degradation and reduced activity, reflecting their dynamic nature. This study systematically delineated the evolutionary trajectory of LTR-RTs—from insertion and proliferation to decay–uncovering their pivotal role in driving Capsicum genome size evolution. Our findings advance the understanding of plant genome dynamics and provide a framework for studying genome size variation across diverse plant lineages. Full article

With the rapid development of new energy vehicles, the echelon utilization of power batteries has become a key pathway to promoting efficient resource recycling and environmental sustainability. To address the limitation of the existing studies that overlook the dynamic strategic interactions among multiple stakeholders, this paper constructs a tripartite evolutionary game model involving the government, battery recycling enterprises, and consumers. By incorporating consumers’ battery usage levels into the strategy space, the model captures the behavioral evolution of all these parties under bounded rationality. Numerical simulations are conducted to analyze the impact of government incentives and penalties, consumer usage behaviors, and enterprise recycling modes on system stability. The results show that a “low-subsidy, high-penalty” mechanism can more effectively guide enterprises to prioritize echelon utilization and that moderate consumer usage significantly improves battery reuse efficiency. This study enriches the application of the evolutionary game theory in the field of battery recycling and provides quantitative evidence and practical insights for policy formulation. Full article

In the operation of a power distribution system, miniature circuit breakers (MCBs) are subjected to the synergistic effect of electrical and mechanical stresses in service, and their operational performance is progressively degraded, which is prone to bring significant losses to the users after failures occur. In order to accurately predict the remaining electrical life of MCBs in service, MCB mechanical characterization and dynamic simulation are carried out, and the initial closing angle of MCBs is selected as the degradation characteristic quantity, so as to deeply analyze the evolutionary characteristics of the initial closing angle in the degradation of MCBs and to construct the electrical degradation model of the one-dimensional linear Wiener process in the present study. With the help of the Monte Carlo method, we carry out the electric life simulation analysis to investigate the intrinsic correlation between the degradation of electric performance and the initial closing angle, and we implement the electric life experiment under the 63 A working condition to analyze the dynamic change in the stiffening angle of the test samples. The parameters of the electrical performance degradation model are identified through the synergistic driving of the electrical life simulation data and the experimental data, the remaining electrical life prediction is realized based on the degradation data of the same batch of products, and the maximum prediction error of the proposed method is controlled within 15%. Full article

The complexities inherent in resource management within cross-domain digital innovation ecosystems have significantly intensified, giving rise to heightened challenges in collaborative interactions among diverse stakeholders, thereby directly impacting systemic stability. Conventional governance frameworks for innovation ecosystems are inadequate in effectively managing the uncertainties and risks inherent in these environments. To address the collaborative governance dilemma and enhance governance efficiency, this paper aims to construct an effective collaborative governance mechanism for a cross-domain digital innovation ecosystem and explore the optimal strategy choices of key governance stakeholders, including the government, digital platform enterprises, and other relevant parties. This research utilizes evolutionary game theory to construct a model comprising three governing entities: the government, digital platform enterprises, and stakeholders. It investigates the evolutionary dynamics of collaborative governance strategies among these entities and the factors that influence governance. Following this, a system dynamics methodology is employed for simulation analysis. The results reveal the following: (1) As the initial intentions of the governing entities evolve, governance decisions within the system tend to stabilize, characterized by a strategic combination of proactive regulation, active cooperative governance, and engaged participation. This equilibrium governance strategy significantly fosters the stable advancement of cross-domain digital innovation ecosystems. (2) The punitive measures enacted by the government and the internal incentive structures of the system positively influence the evolution of governance decisions towards collaborative governance. (3) The cost–benefit assessment of the primary governing entity, the digital platform enterprise, demonstrates a detrimental effect on the evolution of governance decisions towards collaborative governance. These findings are vital for refining the collaborative governance frameworks of cross-domain digital innovation ecosystems and for promoting the robust and stable progression of the system. Full article

Quantum neural networks (QNNs) represent an emerging technology that uses a quantum computer for neural network computations. The QNNs have demonstrated potential advantages over classical neural networks in certain tasks. As a core component of a QNN, the parameterized quantum circuit (PQC) plays a crucial role in determining the QNN’s overall performance. However, quantum circuit architectures designed manually based on experience or using specific hardware structures can suffer from inefficiency due to the introduction of redundant quantum gates, which amplifies the impact of noise on system performance. Recent studies have suggested that the advantages of quantum evolutionary algorithms (QEAs) in terms of precision and convergence speed can provide an effective solution to quantum circuit architecture-related problems. Currently, most QEAs adopt a fixed rotation mode in the evolution process, and a lack of an adaptive updating mode can cause the QEAs to fall into a local optimum and make it difficult for them to converge. To address these problems, this study proposes an adaptive quantum evolution algorithm (AQEA). First, an adaptive mechanism is introduced to the evolution process, and the strategy of combining two dynamic rotation angles is adopted. Second, to prevent the fluctuations of the population’s offspring, the elite retention of the parents is used to ensure the inheritance of good genes. Finally, when the population falls into a local optimum, a quantum catastrophe mechanism is employed to break the current population state. The experimental results show that compared with the QNN structure based on manual design and QEA search, the proposed AQEA can reduce the number of network parameters by up to 20% and increase the accuracy by 7.21%. Moreover, in noisy environments, the AQEA-optimized circuit outperforms traditional circuits in maintaining high fidelity, and its excellent noise resistance provides strong support for the reliability of quantum computing. Full article