Search Results (360)

Agricultural systems are complex social–ecological systems shaped by interactions among diverse stakeholders including governments, enterprises, farmers, consumers, and financial institutions. To examine policy-driven sustainability transitions, this study focuses on three principal actors—government regulatory agencies, agricultural enterprises, and farmers—whose strategic interactions critically determine transition outcomes. The aim is to drive agricultural systems toward sustainability in China. This study develops a three-party evolutionary game model involving the government, enterprises, and farmers to explore how policy-driven incentives influence sustainable development practices. The model incorporates both static and dynamic reward–punishment mechanisms, calibrated with empirical data, to examine behavioral dynamics across stakeholders. The results indicate that fluctuations in enterprise and government engagement contribute to instability in agricultural sustainability transitions. While static reward mechanisms mitigate peak fluctuations, they are insufficient to fully stabilize enterprise commitment, with actors oscillating between sustainable and conventional agricultural practices. Linear dynamic reward mechanisms offer partial stabilization but lack the capacity to maintain long-run Nash equilibrium. In contrast, nonlinear dynamic mechanisms effectively align stakeholder incentives, fostering a stable and enduring shift toward sustainable agricultural systems. This study underscores the importance of tailored dynamic strategies to build resilient agricultural systems with integrated sustainability objectives. Full article

This study is devoted to the analysis of regional collaboration in emergency supply chain reserves. To address this critical research issue, we have developed an AI-assisted tripartite evolutionary game model involving governments, manufacturers, and suppliers across different regions under demand uncertainties and resource disparities. In this study, we employ replicator dynamic equations to derive strategic evolution paths and utilize numerical simulations enhanced by AI-powered global sensitivity analysis for subsequent parameter sensitivity analysis, enabling a systematic examination of equilibrium conditions and stability strategies. Our research findings demonstrate that when government incentive mechanisms provide greater benefits than speculative gains then supply chain enterprises evolve toward collaborative strategies, with the system achieving optimal stability at the equilibrium where collaboration benefits outweigh costs. Our AI-enhanced analysis results also reveal that while higher subsidies accelerate enterprise participation, they may reduce government motivation, necessitating carefully balanced penalty scales to sustain long-term cooperation—findings validated through sensitivity analyses of key parameters. The study’s integration of game theory with AI techniques offers both theoretical innovation in multi-agent decision modeling and practical value for strengthening national emergency management frameworks. Full article

In an era of global warming, sustainable agriculture, which emphasises the conservation of biodiversity and the rational use of natural resources, is growing in importance. One of the key elements is to increase the genetic diversity of crops through the use of crop wild relatives (CWRs) and local varieties, which provide a source of genes for resistance to biotic and abiotic stresses. Modern agricultural systems are characterised by low biodiversity, which increases the susceptibility of plants to diseases and pests. Growing mixtures of varieties, both intra- and interspecific, is a practical strategy to increase plant resistance, stabilise yields and reduce pathogen pressure. This manuscript has a review character and synthesises the current literature on the use of CWRs, local varieties, and variety mixtures in sustainable agriculture. The main research question of the study is to what extent plant genetic resources, including CWRs and local varieties, as well as the cultivation of variety mixtures, can promote plant resistance, stabilise yields and contribute to sustainable agriculture under climate change. The objectives of the study are to assess the role of genetic resources and variety mixtures in maintaining biodiversity and yield stability, and to analyse the potential of CWRs and local varieties in enhancing plant resistance. Additionally, the study investigates the impact of variety mixtures in reducing disease and pest development, and identifies barriers to the use of genetic resources in breeding along with strategies to overcome them. The study takes an interdisciplinary approach including literature and gene bank data analysis (in situ and ex situ), field trials of cultivar mixtures under different environmental conditions, genetic and molecular analysis of CWRs, the use of modern genome editing techniques (CRISPR/Cas9) and assessment of ecological mechanisms of mixed crops such as barrier effect, and induced resistance and complementarity. In addition, the study considers collaboration with participatory and evolutionary breeding programmes (EPBs/PPBs) to adapt local varieties to specific environmental conditions. The results of the study indicate that the integration of plant genetic resources with the practice of cultivating variety mixtures creates a synergistic model that enhances plant resilience and stabilises yields. This approach also promotes agroecosystem conservation, contributing to sustainable agriculture under climate change. Full article

Traditional cyber attack-defense strategies have traditionally focused solely on the attacker and defender, while neglecting the role of government-led system administrators. To address strategic selection challenges in cyber warfare, this study employs an evolutionary game theory framework to construct a tripartite game model involving cyber attackers, defenders, and system administrators. The replicator dynamic equation is utilized for stability analysis of behavioral strategies across stakeholders, with Lyapunov theory applied to evaluate the equilibrium points of pure strategies within the system. MATLAB (2021a) simulations were conducted to validate theoretical findings. Experimental results demonstrate that the model achieves evolutionary stability under various scenarios, yielding optimal defense strategies that provide theoretical support for addressing cybersecurity challenges. Full article

Three-dimensional path planning in emergency logistics is a complex optimization problem, particularly challenging because it requires considering conflicting objectives such as flight time, energy consumption, and obstacle avoidance. Unlike most urban logistics research, this study examines emergency delivery path planning in mountainous environments during natural disasters. One of the most effective approaches to this problem is to employ multi-objective evolutionary algorithms. However, while multi-objective genetic algorithms can handle multiple conflicting objectives, they struggle when dealing with complex constraints. This paper proposes a multi-objective genetic optimization method, Adaptive Crossover-Mutation Multi-Objective Genetic Optimization (ACM-NSGA-II), based on the classic NSGA-II framework. Inspired by the principle of symmetry, this method dynamically adjusts the mutation and crossover rates based on population diversity to maintain a balanced exploration–exploitation trade-off. When population diversity is low, the mutation rate is increased to promote exploration of the solution space; when population diversity is high, the crossover rate is increased to promote better information exchange. The algorithm maintains symmetry by gradually adjusting the step size, balancing adaptability and stability. To address the obstacle avoidance problem, we introduced a dynamic path repair strategy that respects the symmetry of no-fly zone boundaries and terrain features, ensuring the safety and efficiency of Unmanned Aerial Vehicles. This algorithm jointly optimizes three objectives: safety cost, flight time, and energy consumption. The algorithm was tested in a mountainous environment model simulating a remote area. In experiments, ACM-NSGA-II was compared with several mainstream evolutionary algorithms. The Pareto set and hypervolume metrics of each method were recorded and statistically analyzed at a 5% significance level. The results show that ACM-NSGA-II outperforms the baseline algorithms in terms of diversity, convergence, and feasibility. Specifically, compared with the traditional NSGA-II, ACM-NSGA-II improved the average hypervolume metric by 53.39% and reduced the average flight time by 24.26%. ACM-NSGA-II also demonstrated significant advantages over other popular standard algorithms. Experimental results show that it can effectively solve the path planning challenge of emergency logistics Unmanned Aerial Vehicles in mountainous environments. Full article

The intelligent transformation of the wastewater treatment industry, as a core component of the modern environmental governance system, is of decisive significance for achieving sustainable development goals. This study focuses on the issue of multi-stakeholder collaborative governance in the intelligent transformation of the wastewater treatment industry, with differential game theory as the core framework. A tripartite game model involving the government, wastewater treatment enterprises, and digital twin platforms is developed to depict the dynamic interrelations and mutual influences of strategy choices, thereby capturing the coordination mechanisms among government regulation, enterprise technology adoption, and platform support in the transformation process. Based on the dynamic optimization properties of differential games, the Hamilton–Jacobi–Bellman (HJB) equation is employed to derive the long-term equilibrium strategies of the three parties, presenting the evolutionary paths under Nash non-cooperative games, Stackelberg games, and tripartite cooperative games. Furthermore, the Sobol global sensitivity analysis is applied to identify key parameters influencing system performance, while the response surface method (RSM) with central composite design (CCD) is used to quantify parameter interaction effects. The findings are as follows: (1) compared with Nash non-cooperative and Stackelberg games, the tripartite cooperative strategy based on the differential game model achieves global optimization of system performance, demonstrating the efficiency-enhancing effect of dynamic collaboration; (2) the most sensitive parameters are β, α, μ₃, and η₃, with β having the highest sensitivity index (ST_i = 0.459), indicating its dominant role in system performance; (3) significant synergistic enhancement effects are observed among α–β, α–μ₃, and β–μ₃, corresponding, respectively, to the “technology stability–benefit conversion” gain effect, the “technology decay–platform compensation” dynamic balance mechanism, and the “benefit conversion–platform empowerment” performance threshold rule. Full article

The mitochondrial displacement loop (D-loop) region is a non-coding control region that plays a crucial role in replication and transcription, serving as an informative marker for evolutionary and demographic studies. In this study, the complete mitochondrial D-loop sequences from NCBI public database were analyzed across nine Indian and other dromedary populations. Evolutionary and pairwise sequence analysis indicate distinct separation from foreign populations and substantive clustering of Indian breeds within a monophyletic clade. Indian breeds showed greater than 99.4% sequence identity, minimal diversity (π ≈ 0.003), and limited divergence (k = 3–4), whereas Arabian and Iranian populations exhibited more prominent variability (π ≈ 0.004–0.0044; k ≈ 5). Nucleotide composition analyses corroborated the AT-rich nature of the D-loop with conserved sequence length and enrichment of CpG motifs. This suggests selective conservation of functional elements in the D-loop sequence region. Correlation and correspondence analyses highlighted non-random nucleotide usage and repeat dynamics consistent with replication-associated mutational pressures. Demographic structural diversity showed that nearly all genetic variation was distributed among populations (~99.9%), with minimal variation within breeds. Pairwise differentiation values indicated substantial divergence between Indian and foreign breeds, with Indian desert breeds displaying restricted differentiation, possibly due to shared maternal ancestry. Neutrality test results for the sequence dataset interpreted ongoing demographic expansion or bottleneck recovery for the Arabian, Iranian, Sindhi, and Kharai populations. In contrast, for other Indian desert breeds, the neutrality test values that were closing towards zero may express current population shrinkage. Conserved transcription factor binding motifs further support the role of purifying selection on sequence functional constraints. These findings highlight that Indian dromedaries bear highly conserved mitochondrial D-loop sequences, which are influenced by purifying selection and demographic stability. This low mitochondrial diversity in non-coding sequence can mirror the declining population size and emphasizes the urgent need for targeted conservation strategies. Full article

In response to the issues of premature convergence and insufficient parameter control in Particle Swarm Optimization (PSO) for high-dimensional complex optimization problems, this paper proposes a Multi-Strategy Topological Particle Swarm Optimization algorithm (MSTPSO). The method builds upon a reinforcement learning-driven topological switching framework, where Q-learning dynamically selects among fully informed topology, small-world topology, and exemplar-set topology to achieve an adaptive balance between global exploration and local exploitation. Furthermore, the algorithm integrates differential evolution perturbations and a global optimal restart strategy based on stagnation detection, together with a dual-layer experience replay mechanism to enhance population diversity at multiple levels and strengthen the ability to escape local optima. Experimental results on 29 CEC2017 benchmark functions, compared against various PSO variants and other advanced evolutionary algorithms, show that MSTPSO achieves superior fitness performance and exhibits stronger stability on high-dimensional and complex functions. Ablation studies further validate the critical contribution of the Q-learning-based multi-topology control and stagnation detection mechanisms to performance improvement. Overall, MSTPSO demonstrates significant advantages in convergence accuracy and global search capability. Full article

In the current geopolitical context, sustainable energy development has become one of the pillars of global economic growth. This issue is well recognized in the European Union, which has undertaken a number of measures to achieve sustainable development goals. For these measures to be effective, it is essential to conduct a reliable, multi-variant diagnosis of the state of energy development in the EU-27 countries. This paper addresses this highly topical and important issue. It presents a new proprietary method—the Entropy–Evolutionary Evaluation of Sustainability (E³)—based on a multidimensional approach to researching and evaluating the state of sustainable energy development in the EU-27 countries between 2014 and 2023. Through the integration of 19 indicators representing the adopted dimensions of the study (energy, economic, environmental, and social), the method enabled both a static assessment and a dynamic analysis of energy transition processes across space and time. To determine the weights of the indicators for each dimension of sustainable energy development, the CRITIC, Entropy, and equal weight methods, along with the Laplace criterion, were applied. The Analytic Hierarchy Process method was used to establish the weights of the dimensions themselves. An important component of the approach was the inclusion of scenario studies, which made it possible to assess sustainable energy development under five variants: baseline, level, equilibrium, transformational, and neutral. These scenarios were based on different weight values assigned to three factors: the level of energy development (L), its stability (S), and the trajectory of change ($\tilde{T}$). The results, expressed in the form of a total index value and dimensional indices, reveal significant diversity among the EU-27 countries in terms of sustainable energy development. Sweden, Finland, Denmark, Latvia, and Austria achieved the best results, while Cyprus, Malta, Ireland, and Luxembourg—countries heavily dependent on energy imports, with limited diversification of their energy mix and high energy costs—performed the worst. The developed method and the results obtained should serve as a valuable source of knowledge to support decision-making and the formulation of strategies concerning the pace and direction of actions related to the energy transition. Full article

Deoxynivalenol (DON) is a trichothecene mycotoxin produced by Fusarium species that frequently contaminates cereal crops, representing a major threat to food safety, public health, and agricultural productivity. Its remarkable chemical stability during food processing presents significant challenges for effective detoxification. Among the available mitigation strategies, biological approaches have emerged as particularly promising, as they exploit enzymatic systems capable of converting DON into metabolites with substantially reduced toxicity. In this study, we provide a comprehensive analysis of the structural and evolutionary mechanisms underlying DON detoxification across three kingdoms of life. We investigated the fungal glutathione S-transferase Fhb7, the bacterial DepA/DepB epimerization pathway, and the plant SPG glyoxalase using integrative bioinformatics, phylogenetics, molecular modeling, and docking simulations. The selected enzymatic systems employ distinct yet complementary strategies: Fhb7 conjugates DON with glutathione and disrupts its epoxide ring, DepA/DepB converts it into the less toxic 3-epi-DON through stereospecific epimerization, and SPG glyoxalase mediates DON isomerization. Despite their mechanistic differences, these enzymes share key adaptive features that enable efficient DON recognition and detoxification. This work provides an integrative view of cross-kingdom enzymatic strategies for DON degradation, offering insights into their evolution and functional diversity. These findings open avenues for biotechnological applications, including the development of DON-resistant crops and innovative solutions to reduce mycotoxin contamination in the food chain. Full article

The decision-making regarding the development of intelligent construction in construction enterprises is crucial for the transformation and upgrading of the construction industry. This paper constructs an evolutionary game model among construction enterprises and applies system dynamics for simulation analysis of the game model. It explores the impact of key factors on the strategy choices of the game participants. The research findings indicate that the initial state of construction enterprises’ willingness to transition to intelligent construction in the evolutionary game model influences the final stable strategy. Direct benefits, the strength of government incentives, penalty intensity, and reduced costs through joint transition positively affect the probability of construction enterprises implementing intelligent construction, while incremental transition costs and positive spillover effect are negatively correlated. When the direct benefit rate exceeds 5%, costs are jointly reduced by more than 2%, and transition costs are below 35 CNY/m², it can significantly motivate enterprises to adopt intelligent construction. A certain level of government incentives (at least greater than 5 CNY/m²) has a positive effect on the transformation process; however, once the incentives exceed 10 CNY/m², their impact stabilizes. Penalties only affect the speed at which the system evolves toward a stable point. Current policy incentives do not require further enhancement. Meanwhile, reducing incremental transition costs is more effective than increasing the intensity of government incentives. The research conclusions contribute to the quantitative analysis of how changes in different key factors affect the dynamic evolution of strategy adjustments by construction enterprises over time, thereby providing corresponding recommendations for transformation and upgrading. Full article

This study examines the governance of building carbon markets in the context of China’s “dual-carbon strategy”, focusing specifically on the integration of Monitoring, Reporting, and Verification (MRV) systems. The study identifies critical challenges in China’s emissions-trading scheme (ETS), such as weak corporate compliance incentives, high regulatory costs, and concerns about third-party verification independence, which hinder the effectiveness of carbon pricing and technology adoption. Using a three-player evolutionary game model involving the government, carbon-emitting firms, and third-party verifiers, the study finds that moderate government supervision, performance-based incentives, and stronger penalties lead to long-term stability and optimal governance. Based on these findings, policy recommendations are made, including tiered penalties, targeted incentives for green technology adoption, and the strengthening of third-party verification mechanisms to enhance market governance and support China’s carbon-reduction goals in the building sector. Full article

This study presents detailed microstructural observations of the mouthparts and sensory organs of adult cicadomorphan species, obtained using scanning electron microscopy (SEM). Despite microstructural variation, the overall morphology of the mouthparts, comprising a three-segmented labium and a bundle of interlocking stylets (maxillae and mandibles), is highly conserved across species, supporting its evolutionary significance in sap feeding from floem, xylem, or epidermis cells. Variations in the number and shape of mandibular stylet barbs likely reflect adaptations to different host plant tissues. The presence of an identical dual interlocking system between the maxillary stylets, which is found consistently across taxa, enhances functional stability during feeding and indicates a conserved mechanism among cicadomorphans. The species studied exhibit two distinct types of salivary canal closure: hooked and T-shaped. The latter potentially represents a state linked to specialised feeding strategies, such as sap xylem feeding. On the labial tip, there are different shapes of the anterior sensory fields. This area hosts a complex array of sensilla of different numbers, including gustatory (sensilla peg, PS1 and PS2, basiconica, BS3, double basiconica, DB), olfactory (finger–like, FLS) and thermo-hygroreceptive (sensillum dome-shaped, DS, and coeloconicum, CS) types, which facilitate host detection and feeding site selection. In the posterior sensory field, sensilla contact-chemosensory (sensilla basiconica, BS1 and BS2, and sensillum trichoideum, TS) are present. Mechanosensilla chaetica (CH1–CH3) are widely distributed on the last labial segment and may contribute to labium positioning. These findings emphasise the presence of both conserved and specialised morphological traits reflecting evolutionary and ecological diversification within Cicadomorpha. Full article

Biomimetic structures inspired by evolutionary optimized biological systems offer promising solutions to overcome current limitations in passive daytime radiative cooling (PDRC) technology, which efficiently scatters solar radiation through atmospheric windows and radiates surface heat into space without additional energy consumption. While structural biomimicry provides excellent optical performance and feasibility, its complex manufacturing and high costs limit scalability due to micro–nano fabrication constraints. Material-based biomimicry, utilizing environmentally friendly and abundant raw materials, offers greater scalability but requires improvements in mechanical durability. Adaptive biomimicry enables intelligent regulation with high responsiveness but faces challenges in system complexity, stability, and large-scale integration. These biologically derived strategies provide valuable insights for advancing radiative cooling devices. This review systematically summarizes recent progress, elucidates mechanisms of key biological structures for photothermal regulation, and explores their application potential across various fields. It also discusses current challenges and future research directions, aiming to promote deeper investigation and breakthroughs in biomimetic radiative cooling technologies. Full article

Given the great proportion of CO₂ emissions from electricity generation in total energy-related CO₂ emissions, this article constructs a tripartite evolutionary game model consisting of vertical governments and power generation groups (PGGs), where the vertical governments include the central government (CG) and local governments (LGs), considering the externalities of different power generation modes on energy security and the environment. This article analyzes the stable strategies of the three players through replicator dynamics equations, draws the evolutionary phase diagrams, and analyzes the asymptotic stability of equilibrium points by using Jacobian matrices. To validate and broaden the results, this article also provides a numerical simulation. This article concludes that (1) a reduction in the supervision, enforcement, or low-carbonization costs of the CG, LGs, or PGGs motivates it or them to choose “supervision”, “enforcement”, or “low-carbonization” strategies; (2) an increase in penalty incomes or expenses encourages the CG or LGs to choose the “supervision” or “enforcement” strategies; (3) a rise in extra tax expenses motivates PGGs to choose the “low-carbonization” strategy; (4) a change in the externalities of energy security or the environment has no impact on the CG’s strategy. The above conclusions offer the CG and LGs with references for making effective low-carbon policies and provide PGGs with references for choosing an appropriate power generation mode. Full article