Achieving an effective energy transition requires carbon policies that adapt to firm behavior and reward performance rather than penalize uniformly. While existing rebate schemes often overlook firm-level heterogeneity, this study hypothesizes that aligning rebates with efficiency, workforce, and R&D performance can deliver stronger environmental and economic outcomes. To test this, we propose the Efficiency-Enhanced Carbon Tax Rebate Allocation (EECRA) framework, a firm-sensitive system that integrates policy design with stakeholder dynamics. In the first stage, EECRA applies a translog production function to estimate firm-level efficiency, deriving workforce- and R&D-oriented efficiency scores that guide conditional rebate allocation. In the second stage, an evolutionary game framework models stakeholder adaptation through interconnected dynamics of replication, workforce expansion, and R&D investment. Evidence from a Canadian case study utilizing five years of firm-level data, alongside a Norwegian case study employing three years of data, indicates that EECRA generates stable evolutionary equilibria, enhances energy output, reduces emission intensity, promotes green employment, and boosts wage-based GDP and social welfare. By aligning fiscal signals with firm-specific performance, EECRA has the potential to transform rising uniform carbon taxes into scalable drivers of cleaner production, innovation, and competitiveness, while strengthening economic resilience and offering policymakers a robust tool for accelerating low-carbon transitions across diverse economies. • EECRA, a new carbon tax policy, is introduced in the energy network. • EECRA links carbon tax rebates to firm efficiency for cleaner energy transitions. • Dual-efficiency scores drive strategic competition among energy stakeholders. • Evolutionary game reveals stable, adaptive policy equilibria under EECRA dynamics. • Canada and Norway evidence highlight advances in low-carbon transition and growth.

The development of tourism destinations is traditionally interpreted using linear and cyclical models, such as the Tourism Area Life Cycle (TALC). However, growing uncertainty, environmental constraints and crisis events highlight the limitations of these deterministic approaches. This study addresses the growing need for alternative conceptual frameworks by applying evolutionary and path dependence perspectives to examine ecotourism development in Sikkim (India). This Himalayan state's tourism trajectory has been significantly influenced by geopolitical, institutional and environmental factors. The study's primary objective is to compare the TALC model's explanatory power with that of a path dependence approach in interpreting Sikkim's long-term tourism development trajectory, and to identify the pivotal historical decisions and institutional mechanisms that have shaped its current ecotourism-oriented pathway. The research also assesses future challenges related to sustainability and resilience in the context of continued growth and external shocks. The study is based on a mixed-methods approach, combining a quantitative analysis of tourist arrivals (1993–2024) - including compound annual growth rates and trend projections - with a qualitative analysis of policy documents, regulatory frameworks and key political and institutional turning points in tourism development. Sikkim's tourism development is interpreted through two analytical frameworks: the linear-cyclical Tourism Area Life Cycle (TALC) model, and a nonlinear evolutionary path dependence perspective. The results show that, although Sikkim currently corresponds to the 'development' stage of the TALC model, its tourism trajectory cannot be adequately explained as a universal life cycle. Rather, it represents a historically conditioned, path-dependent process, shaped by early access restrictions, environmental policies and the institutionalisation of ecotourism as a dominant development strategy. Key critical junctures, such as the transition to organic farming, the 2011 Ecotourism Policy, and the declaration of Sikkim as India’s first fully eco-friendly state in 2016, created a stable but increasingly rigid development path based on regulated, low-impact tourism. However, rapid post-pandemic growth reveals emerging risks of environmental pressure, infrastructure overload and potential path lock-in. This study shows that evolutionary and path-dependent approaches offer a more reliable framework for understanding the non-linear development of tourism than classical life-cycle models do.

Bi-level optimization research area has become increasingly popular, largely due to its effectiveness in modeling and solving real-world problems. This framework provides a hierarchical structure involving two decision-makers (i.e., upper and lower levels) that govern together to find an optimal solution to complex optimization problems. Most resolution methods proposed in the literature adhere to this hierarchical structure, which limit their applicability only to small-scale instances of the problem. Among these resolution strategies, we highlight an interesting evolutionary algorithm known as CODBA, which focuses on decomposing the lower-level search space into several parts that evolve in parallel to address the high complexity of the nested structure. In this paper, we enhance the searching capabilities of CODBA by proposing a novel evolutionary reinforcement learning approach that integrates the core CODBA scheme with a Q-learning strategy, presenting a promising method for training intelligent search algorithms for bi-level optimization problems. The computational statistical experiments are performed on bi-level multi-depot vehicle routing problem, demonstrated the effectiveness of our solution approach in terms of computation time and solution quality compared to existing algorithms.

“Only One, Two at the Most”: George Davis' Insights into Adaptive Radiations of Freshwater Gastropods as an Early Evolutionary Systematics Approach

Solar Wind Heating near the Sun: A Radial Evolution Approach

The choroid of the eye is a rare site for metastatic tumor spread, and as small lesions on the periphery of brain MRI studies, these choroidal metastases are often missed. To improve their detection, we aimed to use artificial intelligence to distinguish between brain MRI scans containing normal orbits and choroidal metastases. We present a novel hierarchical deep learning framework for sequential cropping and classification on brain MR images to detect choroidal metastases. The key innovation of this approach lies in training an orbit localization network based on a YOLOv5 architecture to focus on the orbits, isolating the structures of interest and eliminating irrelevant background information. The initial subtask of localization ensures that the input to the subsequent classification network is restricted to the precise anatomic region where choroidal metastases are likely to occur. In step 1, we trained a localization network on 386 T2-weighted brain MRI axial slices from 97 patients. Using the localized orbit images from step 1, in step 2 we trained a binary classifier network with 33 normal and 33 choroidal metastasis-containing brain MRIs. To address the challenges posed by the small data set, we used a data-efficient evolutionary strategies approach, which has been shown to avoid both overfitting and underfitting in small training sets. Our orbit localization model identified globes with 100% accuracy and a mean average precision (mAP) of intersection over union thresholds of 0.5-0.95 [mAP(0.5:0.95)] of 0.47 on held-out testing data. Similarly, the model generalized well to our step 2 data set, which included orbits demonstrating pathologies, achieving 100% accuracy and mAP(0.5:0.95) of 0.44. mAP(0.5:0.95) appeared low because the model could not distinguish left and right orbits. Using the cropped orbits as inputs, our evolutionary strategies-trained convolutional neural network achieved a testing set area under the curve of 0.93 (95% CI, 0.83-1.03), with 100% sensitivity and 87% specificity at the optimal Youden index. The semiautomated pipeline from brain MRI slices to choroidal metastasis classification demonstrates the utility of a sequential localization and classification approach, and clinical relevance for identifying small, "corner-of-the-image," easily overlooked lesions. Artificial Intelligence Level of Evidence: 5B.

Cognitive Radio Networks (CRNs) are essential for improving spectrum efficiency in 5G and beyond; however, their open and adaptive nature makes them highly vulnerable to jamming attacks. The purpose of this work is to develop an anti-jamming framework that jointly addresses jammer detection and frequency selection under adversarial conditions. The novelty of this work lies in jointly addressing strategic anti-jamming decision-making and data-driven jammer detection within a single unified framework. Motivated by the limitations of standalone game-theoretic, evolutionary, and deep learning approaches in dynamic adversarial environments, we propose a hybrid anti-jamming framework that integrates game theory, deep learning, and particle swarm optimization (PSO). Game theory is employed to model the strategic interaction between secondary users and jammers, enabling utility-aware frequency hopping (FH) decisions, while a PSO-driven deep neural network (DNN), termed DeepSwarm, is designed for accurate and robust jammer detection. The strength lies in leveraging PSO to enhance the convergence speed and robustness of the DNN in dynamic jamming environments. Simulation results demonstrate that DeepSwarm achieves 98.10% accuracy, 98.30% recall, 98.10% precision, and a 98.05% F1-score, outperforming SVM, linear regression, and stacking baselines. Furthermore, FH guided by the proposed detection framework improves channel utilization and increases normalized throughput by up to 32% compared to static selection under varying jamming probabilities. These findings confirm the scalability and effectiveness of the proposed framework for securing CRNs in adversarial environments.

Decision support for airfare forecasting using an evolutionary approach: Evidence on search engine data from Touristic agency

A mesoscale simulation approach of microstructural evolution and anisotropic shrinkage during sintering of binder jet printed 17-4PH stainless steel

Green technology innovation (GTI) is crucial for enhancing the resource utilization of construction and demolition waste (CDW). However, consumer resistance to products made from recycled CDW significantly constrains their advancement. Existing studies largely emphasize government policies and technological progress, while the pivotal influence of consumer innovation resistance remains underexplored. This study applies evolutionary game theory, grounded in innovation resistance theory, to examine the long-term interactive dynamics between consumers and building material manufacturers (BMMs). The results reveal that (1) both consumers’ stronger willingness to purchase green innovative products and BMMs’ higher initial intention to adopt GTI foster sustainable green development; and (2) the degree of consumer innovation resistance exerts heterogeneous effects on BMMs’ GTI decisions, whereas consumer green preferences consistently stimulate GTI adoption. Overall, this study elucidates the evolutionary mechanisms through which consumer innovation resistance and green preferences affect BMMs’ GTI behavior, addressing a key gap in the CDW domain. It further offers theoretical guidance for enterprises to mitigate innovation resistance, refine GTI strategies, and advance sustainable innovation in the construction industry.

Exploring air-rail cooperation in China under asymmetric dependency: An evolutionary game approach

ABSTRACT In an increasingly complex digital economy, platform ecosystems have emerged as a critical organizational form for promoting collaborative innovation and value co‐creation. Ecological embeddedness not only facilitates deep integration between platform enterprises and complementary firms in terms of technological and resource dimensions but also provides essential support for the sustained improvement of overall ecosystem value. Against this backdrop, this study aims to examine the logic of strategic choices and the governance mechanisms of platform enterprises, technology‐based startups, and incumbent firms in facilitating collaborative innovation within platform ecosystems. From an ecological embeddedness perspective, this study develops an evolutionary game model for a platform ecosystem encompassing multiple heterogeneous actors. It systematically analyzes the conditions under which evolutionary stability emerges when different participants adopt active collaborative innovation strategies. By employing numerical simulations, it further explores the mechanisms through which key factors—such as initial cooperative willingness, resource‐sharing levels, benefit distribution mechanisms, strength of organizational relationships, and regulatory and guiding mechanisms—influence the evolutionary trajectories of collaborative innovation. On this basis, the study proposes governance strategies to facilitate the implementation pathways for collaborative innovation within platform ecosystems. The results indicate that a high initial cooperative willingness among actors facilitates the initiation of collaborative innovation and promotes the evolution of cooperative relationships toward a stable equilibrium. Achieving multi‐actor ecological embedded collaborative innovation requires three critical conditions: (1) Efficient ecological empowerment and knowledge diffusion mechanisms must be established to enhance resource‐sharing levels; (2) benefit distribution should be equitable and designed to optimize overall ecological gains; and (3) the platform‐centric ecological innovation network must be strengthened to consolidate the organizational foundation for cross‐actor collaboration. The absence of any of these conditions may disrupt the evolutionary path of collaborative innovation or even result in failure of cooperation. Meanwhile, further analysis reveals that regulatory mechanisms significantly limit passive strategic choices by increasing penalties for breaches of agreement and reducing opportunistic benefits, whereas guiding mechanisms enhance the motivation of innovation actors to participate through appropriately calibrated incentives. However, when incentives exceed a certain threshold, innovation activities may fail. Overall, scientifically designed governance mechanisms contribute to the improvement of collaboration efficiency among platform ecosystem members and enhance system performance. The findings of this study not only enrich the theoretical framework of embedded innovation and collaborative innovation in platform ecosystems but also provide valuable insights for designing governance mechanisms in the embedded innovation practices of technology‐based startups and platform enterprises.

DPCND: A dual-population based evolutionary approach for critical node detection problem in complex networks

Adaptive differential evolution approaches in real-time optimization of co-generation systems for enhanced energy minimization

Advanced shelf space allocation in brick-and-mortar stores: A multi-population differential evolution approach for high-impact planogram design

Neutron stars (NS), with their extreme gravitational and magnetic fields, provide an exceptional astrophysical laboratory for studying axion dark matter (DM). Through the Primakoff effect, axions can convert into photons within the magnetospheres of NS, a process that may produce observable radio and X-ray signals. In this work, we investigate axion-photon conversion using a novel, time-dependent state evolution formalism, moving beyond the commonly used stationary-path approximations. We derive a generic analytical expression for the conversion probability and calculate the associated radiated power. Our analysis demonstrates that this approach allows NS to strongly constrain the axion-photon coupling constant, reaching sensitivities of gaγγ ≃ 10−14 −10−15 GeV−1 for axion masses of ma ≃ 10−3 −10−10 eV. These results establish a new pathway to constrain gaγ via NS observations. Future campaigns using powerful observatories like the James Webb Space Telescope (JWST), Green Bank Telescope (GBT), and More Karoo Array Telescope (MeerKAT) array will be ideally suited to probe the distinct spectral signatures predicted by our model across multiple frequency domains.

Abstract This paper revisits the work of Richard R. Nelson (1930–2025), focusing on his critique of equilibrium theory, to explore persistent methodological debates within the field of economics. It examines the tension between theoretical abstraction and empirical realism, arguing that Nelson’s evolutionary approach offers a compelling alternative to the dominant general equilibrium framework. By foregrounding processes of change, uncertainty, and adaptive behaviour, Nelson (alongside Sidney G. Winter) challenges the discipline’s tendency to model economies as static, fully understood systems. While this paper does not aim to provide a comprehensive review of their contributions, it highlights how their critique remains strikingly relevant today. Despite methodological refinements, shifts in research topics and policy engagement, mainstream economics continues to struggle with dynamic, out-of-equilibrium analysis. Revisiting Nelson’s legacy is not only timely, but also essential for those seeking a more realistic and responsive economics.

To address the complexity of multi-constraint multi-objective optimization problems (mCMOPs), this paper proposes a novel multi-population evolutionary algorithm (MOEA). Multi-objective optimization problems (MOPs) are ubiquitous in scientific and engineering fields, while the introduction of multiple complex constraints significantly increases the difficulty of finding solutions. To tackle this challenge, this work systematically analyzes the intrinsic relationships among the Single-Constraint Pareto Front (SCPF), Sub-Constraint Pareto Front (SSCPF), Unconstrained Pareto Front (UPF), and the Final Constrained Pareto Front (FCPF), and it investigates how these relationships can be leveraged to effectively enhance optimization performance. Based on this analysis, a Hierarchical Multi-Population Cooperative Evolutionary Approach (HMP-CE) is proposed. The approach constructs C+2 populations (where C represents the number of constraints) to search the UPF, SCPF, and SSCPF at appropriate stages, thereby driving the final solution approximation in a hierarchical manner. Meanwhile, HMP-CE introduces the following two key mechanisms: (1) the Population Activation–Dormancy Regulation (PADR) mechanism, which adaptively regulates the activation and dormancy of populations to reduce computational cost and accelerate convergence; (2) the Constraint Combination Timing Identification (CCTI) mechanism, which identifies suitable moments to jointly solve selected constraints in SSCPF, thereby enhancing cooperative efficiency among populations. Experimental results on 37 benchmark mCMOPs, and six real-world engineering problems demonstrate that the proposed algorithm exhibits superior performance in terms of convergence, feasibility, and solution diversity, providing a competitive approach for solving complex multi-constraint optimization problems.

Customs administrations play a crucial role in facilitating international trade, ensuring compliance with laws and regulations, and safeguarding national interests. In recent years, the landscape of global trade has become increasingly complex, driven by technological advancements, evolving trade agreements, and shifting geopolitical dynamics. In this context, the effective governance of customs administrations has emerged as a critical priority for governments worldwide. Cameroon, like many other countries, faces significant challenges in managing its customs operations amidst these evolving dynamics. This paper, therefore, explored the significance of performance measurement as a governance tool within the context of Cameroon Customs Operations. The paper examined the role of performance measurement in enhancing governance practices within Cameroon Customs, focusing on key indicators, methodologies, and their impact on organisational effectiveness. In essence, performance measurement entails that specific goals are set alongside evaluating and monitoring tools within the administration for specific timeframes. In a nutshell, a system that the administration uses to enhance its services and keep its officers accountable. Through a comprehensive review of existing literature and case studies, the paper highlighted best practices and challenges in implementing performance measurement systems. Furthermore, it discussed the potential implications for policy-making and institutional reforms aimed at strengthening customs governance in Cameroon. The findings are that performance measurement has served as a strategic tool for enhancing transparency, accountability, and efficiency within the Cameroon Customs administration. The study recommends that the administration should not sleep on its laurels but brace itself further to ensure sustainability in the context of fast-evolving trade dynamics.

All species evolve under selective pressures that emerge from their interactions, often antagonistic, with other species. Phenotypes mediating species interactions manifest as the combined products of the genomes of interacting species; understanding the evolutionary processes acting in one lineage therefore cannot be attained without bridging the genomes of interacting species. Venoms have arisen independently more than 100 times in animals and serve diverse roles in species interactions, including predation and defense. Each venom is evolutionarily entwined with reciprocal phenotypes, such as venom resistance, in often diverse recipient species. Despite extensive work on venoms, the full genetic basis for resistance to whole venoms is largely unknown. Using the venom of the Florida blue centipede (Scolopendra viridis) comprised of 35 toxins and Drosophila melanogaster as model prey, we investigated the genetics of venom resistance for a naive prey through experimental evolution and genetic-mapping approaches. We identified 12 consensus genes across techniques associated with venom resistance, yet individual experiments suggested a genome-wide basis for resistance involving hundreds to thousands of genes, despite the relative simplicity of the venom of S. viridis. We found no evidence for fitness trade-offs associated with the evolution of resistance and revealed a stark contrast in the nature of venom resistance between prey sexes. The disparity in resistance genetics between prey sexes as well as the relative genetic complexity of venom versus resistance may ultimately give venomous predators a coevolutionary advantage over their prey.