Search Results (4,349)

Sliding failure is a common form of instability for reverse rock slopes. The determination of the failure plane for such unstable slopes is currently a key and challenging issue in research. To pinpoint the position of the failure surface with higher accuracy, this paper proposes a key equilibrium model based on the symmetric bidirectional evolutionary structural optimization (BESO) method. This model is based on the basic parameters of the slope, the BESO method, and the limit equilibrium theory. It uses its own algorithm program to search and determine the key failure plane of the slope. At the same time, two rock slope model tests were conducted to verify the effectiveness of this method in slope stability analysis. The calculated results exhibit a good consistency with the experimental outcomes, which confirms the feasibility of using the key equilibrium-bidirectional evolutionary structural optimization (CE-BESO) method for stability evaluation of this type of slopes. Full article

Cotton spider mites pose a significant threat to cotton production, while traditional manual investigation and blanket pesticide application are inefficient for precision pest management in large-scale cotton fields. To address this challenge, this study developed an integrated UAV multispectral remote sensing system for spider mite monitoring and precision spraying. Multispectral imagery was acquired from cotton fields in Shaya County, Xinjiang using UAV-mounted cameras, and vegetation indices including RDVI, MSAVI, SAVI, and OSAVI were selected through feature optimization. Comparative evaluation of three machine learning models (Logistic Regression, Random Forest, and Support Vector Machine) and two deep learning models (1D-CNN and MobileNetV2) was conducted. Considering classification performance and computational efficiency for real-time UAV deployment, Random Forest was identified as optimal, achieving 85.47% accuracy, an 85.24% F1-score, and an AUC of 0.912. The model generated centimeter-level spatial distribution maps for precise spray zone delineation. An improved NSGA-III multi-objective path optimization algorithm was proposed, incorporating PCA-based heuristic initialization, differential evolution operators, and co-evolutionary dual population strategies to optimize deadheading distance, energy consumption, operation time, turning frequency, and load balancing. Ablation study validated the effectiveness of each component, with the fully improved algorithm reducing IGD by 59.94% and increasing HV by 5.90% compared to standard NSGA-III. Field validation showed 98.5% coverage of infested areas with only 3.6% path repetition, effectively minimizing pesticide waste and phytotoxicity risks. This study established a complete technical pipeline from monitoring to application, providing a valuable reference for precision pest control in large-scale cotton production systems. The framework demonstrated robust performance across multiple field sites, though its generalization is currently limited to one geographic region and growth stage. Future work will extend its application to additional cotton varieties, growth stages, and geographic regions. Full article

Background: From the perspective of product liability, this study explores how agricultural product e-commerce enterprises can enhance the quality of the agricultural product supply chain through quality incentive strategies. Methods: Based on a tripartite evolutionary game model, the strategic interactions among farmers, agricultural product e-commerce enterprises, and the government are analyzed. Results: The research finds that whether the system converges to the ideal equilibrium of “high-quality production—ex-ante quality cost-sharing—collaborative governance” depends on the combined effects of revenue distribution, liability costs, and external incentives or penalties. Among these, government-led collaborative governance plays a key guiding role in incentivizing enterprises and influencing farmers’ behaviors. The incentive measures implemented by e-commerce enterprises and government penalties can effectively curb farmers’ low-quality production behaviors. Conclusions: The study further reveals how factors such as ex-ante cost-sharing, liability allocation, and farmers’ conformity psychology affect the stability of agricultural product supply chain quality, thereby providing theoretical support for constructing a “policy-platform-farmer” collaborative governance framework. Full article

Accurate state estimation is essential for the performance of uncalibrated visual servoing systems, yet it is frequently undermined by non-Gaussian disturbances—such as impulse noise, motion blur, and occlusions—whose heavy-tailed statistical characteristics are not adequately represented by conventional Gaussian models. To address this issue, this paper presents an evolutionary neural-enhanced intelligent controller designed for robotic visual servoing under such noise conditions. The controller architecture incorporates a hybrid estimation core that integrates $\alpha$-stable distribution modeling for principled noise characterization with an Interacting Multiple Model Kalman filter (IMM-KF) to address system dynamics and uncertainties. A multi-layer perceptron (MLP), optimized globally via the Stochastic Fractal Search (SFS) algorithm, is embedded to provide adaptive compensation for residual estimation errors. This integration of statistical modeling, adaptive filtering, and evolutionary optimization constitutes a coherent learning-based control framework. Simulations and physical experiments reveal that the proposed method enhances improvements in estimation accuracy and tracking performance relative to conventional approaches. The outcomes indicate that the framework offers a functional solution for vision-based robotic systems operating under realistic conditions where non-Gaussian sensor noise is present. Full article

Air pollution has become a pressing global challenge that threatens ecological security, public health, and sustainable socioeconomic development, prompting extensive academic and policy attention on air pollution control and environmental governance. To systematically clarify the knowledge structure, evolutionary trends, and interdisciplinary characteristics of this field, this study employs bibliometric methods combined with CiteSpace, VOSviewer, and Tableau tools for in-depth analysis of the global literature published in the last 25 years. Key dimensions including keyword clustering, co-occurrence networks, national cooperation patterns, journal co-citation relationships, and policy evaluation methodology evolution are explored. The results reveal that research output in this field has maintained sustained rapid growth, with distinct interdisciplinary integration across environmental science, economics, energy engineering, and public health. Notably, the evolutionary path of research themes presents a clear transformation: shifting from early emphasis on “emission standards” and “end-of-pipe treatment” to market-oriented policy instruments such as “carbon tax” and “carbon emission trading”, and further expanding toward systematic solutions including “green finance” and “collaborative environmental governance”. In terms of policy evaluation methodologies, there is a developmental trend from single-indicator monitoring to integrated assessment frameworks combining quasi-experimental approaches (e.g., difference-in-differences, regression discontinuity design) and multi-model coupling. Furthermore, national collaboration analysis identifies China as a core hub in the global research network, while European and American countries maintain advantages in research impact. While this observation is based on absolute metrics, a data normalization approach (e.g., by population) reveals more distinct relative differences and a complementary global dynamic: China’s scale-driven output aligns with large-scale, engineering-intensive governance challenges, whereas the markedly higher per capita research impact of Western nations reflects a deeper focus on policy innovation and systemic mechanisms. Burst term detection highlights emerging frontiers such as the “Porter hypothesis”, reflecting growing focus on the synergistic relationship between environmental regulation, green innovation, and economic development. This study also identifies critical research gaps, including insufficient attention on cross-regional pollution transport policy coordination and emergency policy evaluation under extreme weather conditions. The findings provide a comprehensive academic map of global air pollution control and environmental governance research, offering valuable insights for optimizing environmental policy design, promoting interdisciplinary collaboration, and guiding future research directions in this field. Full article

Background/Objectives: ASXL1 is a chromatin-associated gene implicated in both hematologic malignancies and neurodevelopmental disorders, including Bohring–Opitz syndrome (BOS). Although many ASXL1 variants are well classified, a substantial proportion remain variants of uncertain significance (VUS), complicating molecular diagnosis and genetic counseling. The objective of this study was to evaluate whether structural context can inform the interpretation of selected ASXL1 missense variants in a clinical setting. Methods: We describe a 17-year-old female with clinical features consistent with BOS carrying the heterozygous ASXL1 variant p.Q1448R, currently classified as benign under ACMG/AMP guidelines. Three-dimensional in silico structural modeling was performed using AlphaFold3 and available crystallographic data. Three additional ASXL1 missense variants classified as VUS in ClinVar (p.R265H, p.T297M, and p.Y358C) were also analyzed. Evolutionary conservation, domain localization, and residue-level interactions were assessed. Results: Structural modeling indicated that the p.Q1448R substitution alters polar interactions and introduces a steric constraint near a conserved PHD-type zinc finger domain. Variants p.R265H and p.T297M affected stabilizing interactions within the DEUBAD, which is involved in BAP1 activation, while p.Y358C altered a polar microenvironment adjacent to a chromatin-interacting region. All analyzed variants, except p.T297M, localized to evolutionarily conserved regions. Conclusions: This study demonstrates that in silico structural analysis can provide complementary, domain-level insights for the interpretation of ASXL1 missense variants that remain classified as benign, likely benign or VUS under current frameworks. Such approaches may assist in prioritizing variants for further functional evaluation and refining molecular interpretation when experimental data are limited. Full article

The Kuramoto–Sivashinsky (KS) equation and its fractional form (FKS) are widely used across scientific fields, including fluid dynamics, plasma physics, and chemical processes, to model nonlinear phenomena such as shock waves. It is worth emphasizing that this contribution is part (II) of a larger, systematic research program aimed at modeling, for the first time, completely nonintegrable, nonplanar, and fractional nonplanar evolutionary wave equations. This work focuses on the nonplanar KS framework and its applications to dust–acoustic shock waves in a complex plasma composed of inertial dust grains and inertialess nonextensive ions. This study analyzes both the nonplanar integer KS and nonplanar FKS equations, accounting for geometric effects. This is because the nonplanar model is most suitable for analyzing various nonlinear phenomena (e.g., shock waves) that arise and propagate in plasma physics, fluids, and other physical and engineering systems. Since the nonplanar KS equation is a fully non-integrable problem, its analysis poses a significant challenge for studying the properties of nonplanar shock waves in plasma physics. Therefore, the primary objective of this study is to analyze the nonplanar KS equation using the Ansatz method, thereby deriving semi-analytical solutions that simulate the propagation mechanism of nonplanar shock waves in various physical systems. Following this, we investigate the effect of the fractional factor on the profiles of nonplanar dust–acoustic shock waves to elucidate their propagation mechanism and assess the impact of the memory factor on their behavior. To achieve the second goal, we face a significant challenge because the model under study does not support exact solutions and is more complex than simpler physical models. Thus, the Tantawy technique is employed to overcome this challenge and to analyze this model for generating highly accurate analytical approximations suitable for modeling nonplanar fractional shock waves in various plasma models and in other physical and engineering systems. Full article

This comprehensive review investigates how biomimetic mechanisms inform engineered systems that adapt to the user and environment during use, marking a shift from aesthetic imitation to functional compliance. By synthesizing a curated evidence base of 52 key studies, this work identifies four investigation domains: (i) biomorphic structures, (ii) compliant material systems, (iii) computational modelling via AI and digital twins, and (iv) integrated ergonomic-sustainability evaluations. Our analysis reveals a technical continuum dominated by Passive Compliance (59.6%), while identifying significant translational bottlenecks in closed-loop adaptive verification. To address these gaps, the study introduces a functional taxonomy and the Nautilus Model as a maturity framework for iterative, knowledge-preserving design. Furthermore, a set of benchmark tasks (e.g., 100 Hz adaptation, 500,000-cycle durability) is established to support the validation of future co-evolutionary, eco-centric products. This synthesis establishes a new research agenda that integrates biological self-organization with rigorous ergonomic verification. Full article

A hybrid quill shaft for a multifunctional machine tool centre combines a conventional steel body with a wound composite insert that significantly enhances structural stiffness and dynamic properties. This paper presents a methodologically rigorous approach to the design and validation of a hybrid quill shaft, encompassing material optimisation through the NSGA-II evolutionary algorithm, experimental modal analysis, and verification of the influence of an active pre-tensioning anchor system on the compensation of elastic deformations. A finite element model was coupled with an optimisation tool evaluating eight fibre types across 786 iterations. Results unequivocally demonstrated the superiority of M55J fibre with ±88° orientation as the optimal compromise between stiffness (13.2% reduction in deflection), weight (3% reduction), and cost (4.2% cost increase). Composite safety was ensured through the three-dimensional Tsai-Wu strength criterion applied as a constraint. Experimental validation on an assembly with a hydraulic pre-tensioning system demonstrated symmetrical quill shaft behaviour (±0.07 mm/m) and agreement with finite element analysis (9.5% deviation). Numerical modal analysis revealed a pronounced decrease in natural frequencies with increasing overhang (from 308 Hz to 58 Hz). The resulting design incorporating M55J fibres, 2345 mm length, and epoxy resin in a 60:40 fibre-to-matrix ratio represents a practically implementable solution for enhanced precision and productivity in modern machine tool centres. Full article

The development of the new energy vehicle (NEV) industry has become a key driver of the global low-carbon transition. Understanding the policy effect on NEV diffusion is essential to promote sustainable growth. In this study, we propose a new approach that combines a two-layer small-world network involving consumers and enterprises and evolutionary game theory to study the diffusion effect of industrial and trade policies on enterprises’ low-carbon production strategies and consumer preferences. Different from existing diffusion models, we integrate reinforcement learning (RL) into the decision-making process of enterprises and use SHapley Additive exPlanations (SHAP) to decode the micro-level decision logic of enterprises. In terms of the decision-making mechanism, the simulation results show that the Q-learning algorithm better fits the real market diffusion trend of NEVs compared with traditional algorithms; in terms of policy effects, industrial policies and trade policies exhibit a synergistic effect. SHAP analysis reveals that enterprises are more concerned about NEV market maturity than the impact of policy parameters on decision-making; Sobol sensitivity analysis indicates that consumer subsidies have a greater impact on the market diffusion of NEVs than trade policies. Full article

In deregulated electricity markets, Generation Companies (GENCOs) are exposed to substantial financial risk due to volatile and uncertain electricity prices. Traditional generation asset valuation approaches, which rely primarily on expected profit, fail to adequately capture downside risk under market uncertainty. This study proposes an integrated risk-aware framework for generation asset valuation by embedding Value-at-Risk (VaR) into a Price-Based Unit Commitment (PBUC) model. VaR is employed to quantify potential profit losses at different confidence levels, enabling GENCOs to explicitly assess downside exposure associated with electricity price fluctuations. Spot price uncertainty is modeled using the Delta-Normal approach based on historical PJM market data. The resulting nonlinear mixed-integer optimization problem is solved using an Improved Immune Algorithm (IIA) enhanced with the Taguchi Method to improve convergence stability and solution diversity. Case studies on the IEEE 15-unit system demonstrate that the proposed IIA consistently outperforms conventional evolutionary algorithms in terms of profitability, robustness, and convergence reliability. The VaR analysis further reveals pronounced left-tail risk in profit distributions, particularly during peak-load periods, highlighting the importance of risk-adjusted commitment strategies. The proposed framework provides a practical decision-support tool for GENCOs to balance profitability and downside risk in competitive electricity markets. Full article

Variant emergence continues to pose a threat to public health, despite the widespread use of vaccination. To quantify how vaccine strain compositions shape evolutionary and epidemiological outcomes, we extend a previous genotype-structured transmission model with vaccination and study the impact of different vaccination formulations on variant emergence. It consists of a set of partial differential equations coupled with an integro-differential one. We begin by showing that the model reproduces variant emergence followed by a period of co-circulation in the absence of vaccination. Then, we introduce vaccination and show important trade-offs shaped by the breadth and cross-protection of vaccine-induced immunity. In our simulations, narrow-spectrum vaccines substantially reduce the immediate infection burden but inadvertently promote the emergence of non-targeted variants. After that, we study the effects of more complex shapes such as triangular and M-shaped configurations. We show that M-triangular distributions outperform triangular ones by limiting secondary variant expansion for vaccines with narrow cross-protection. In contrast, triangular compositions are more protective when considering broader cross-protection. We also show that targeting the genetic area between co-circulating variants is more beneficial than focusing on specific variants when using vaccines with a broad cross-protection. Together, these results highlight how vaccine breadth and antigenic targeting influence both epidemic size and the trajectory of variant emergence, offering quantitative guidance for monovalent and multivalent vaccine design. Full article

Evolutionary changes can significantly impact interactions among populations and disrupt ecosystems by driving extinctions or collapsing population oscillations, posing substantial challenges to biodiversity conservation. This study addresses the ecological rescue of a predator population threatened by a mutant prey population using the optimal control method. To study this, we study a model that incorporates a genotypically structured prey population comprising wild-type, heterozygous, and mutant prey types, as well as the predator population. We prove that this model has both local and global existence and uniqueness of solutions, ensuring the model’s robustness. Then, we applied the optimal control method, incorporating Pontryagin’s Maximum Principle, to introduce a control input into the model and minimize the mutant population, thereby stabilizing the ecosystem. We utilize a reproduction number and a control efficacy measure to numerically demonstrate that the undesired dynamics of the model can be controlled, leading to the suppression of the mutant and the restoration of the oscillatory dynamics of the system. These findings demonstrate the applicability of optimal control strategies and provide a mathematical framework for managing such ecological disruptions. Full article

The Japanese frog Glandirana rugosa, endemic to Japan, exhibits both XY and ZW sex determination systems in different populations, representing a rare example of sex chromosome turnover within a single species. To explore the genetic basis of this phenomenon, we analyzed the X, Y, Z, and W chromosomes using microdissection followed by next-generation sequencing. All chromosomes originated from chromosome 7, and the sex chromosomal sequences were homologous. Comparative analyses revealed a high degree of sequence similarity between the Y and Z chromosomes. This suggests that the Y and Z chromosomes may have originated from the same ancestral chromosome and remained highly homologous at the genomic sequence level. This relationship supports the idea that transitions between the XY and ZW systems can occur through the reuse of homologous chromosomes. Our findings indicate that G. rugosa provides an informative model for studying the early stages of sex chromosome differentiation and turnover. Understanding these processes in G. rugosa may enhance our understanding of the evolutionary dynamics of sex chromosomes across vertebrates. Full article

To address collaborative decision-making challenges in the pharmaceutical logistics supply chain amid public health emergencies, this study integrates disappointment aversion, delay effects, and pharmaceutical value attenuation, constructing a three-echelon system. It adopts a “differential game-system dynamics (SD)” two-layer dynamic research method for in-depth synergy. The differential game model focuses on multi-agent dynamic strategic interactions, deriving time-series equilibrium solutions for the optimal effort levels, transportation efficiency, and profits under four decision modes (decentralized, government subsidy, cost-sharing, centralized) to clarify the dynamic impact laws of the core parameters. Compensating for its limitations in complex environmental coupling and practical variable integration, the SD model incorporates the patient consumption rate, inventory fluctuations, weather disturbances and other real factors to build a dynamic feedback system. It not only verifies the practical adaptability of the differential game equilibrium solutions but also reveals the evolutionary laws of supply chain performance and the amplified inter-mode performance differences under multi-factor superposition. This study finds that centralized decision-making performs the best in terms of transportation efficiency peaking, profit stability, and attenuation control. Pharmaceutical stability and enterprise effort levels positively drive benefits, while disappointment aversion and excessive delays exert inhibitory effects. Government subsidies need to be paired with collaborative mechanisms to avoid policy dependence. Management implications suggest that enterprises should prioritize the collaborative centralized-decision-making mode, establish risk-sharing and benefit-sharing mechanisms, precisely regulate key variables, and implement gradient subsidies with exit mechanisms to enhance the supply chain’s dynamic adaptability and achieve the triple optimization of “efficiency–profit–stability”. Full article