Search Results (4,308)

Driven by the new wave of technological revolution and industrial transformation, firms are accelerating strategic change to gain new competitive advantages. Situated within a complex adaptive system, firms must adapt to highly dynamic and uncertain external environments by adjusting executive cognitive structures, reconfiguring resources and capabilities, and strengthening collaboration with industrial ecosystem elements; hence, digital strategic change is characterized by continuous evolution. Using a sample of Chinese A-share listed firms from 2015 to 2023, this study develops a “cognition–capability–strategy” pathway model grounded in upper echelons theory and dynamic capabilities theory to examine how executive cognitive styles, i.e., cognitive flexibility and cognitive complexity, drive digital strategic change via absorptive capacity and how environmental dynamism moderates these relationships. The findings show that executive cognition, as a decision node in strategic change, can dynamically adjust firms’ strategic paths by activating absorptive capacity in rapidly changing external information environments; environmental dynamism differentially affects the two cognitive styles. Heterogeneity tests further indicate that the role of executive cognition varies significantly with regional digital economy development levels, firm life cycle, and industry factor intensities. The study reveals how firms can respond to high environmental uncertainty through cognition–strategy alignment and resource capability reconfiguration in a complex adaptive system, providing theoretical references and practical insights for emerging economies to advance digital transformation and enhance competitiveness. Full article

Exploring the spatiotemporal characteristics and spatial correlation structure of the coupling and coordination relationship between urban economic development and ecological resilience is of great significance for optimizing the regional coordinated development strategies of urban agglomerations and building high-quality economic development regions. Taking 33 counties (cities, districts) in the Qianzhong Urban Agglomeration as the research objects, this study adopts the analytical paradigm of “mechanism exploration—level measurement—relationship evolution—spatial correlation”, expands and constructs a four-dimensional ecological resilience evaluation index system based on the “risk resistance—adaptation—recovery” framework, and systematically analyzes the spatiotemporal dynamics and spatial correlation characteristics of the coupling and coordination between economic development and ecological resilience from 2005 to 2020 by combining the coupling coordination model, trend surface analysis, and spatial gravity model. The research results show that the overall coupling coordination degree between economic development and ecological resilience in the Qianzhong Urban Agglomeration presents an upward trend, and the key to optimizing the coupling coordination lies in improving the level of urban economic development. The spatial correlation of regional coupling coordination degree is increasingly close, and its spatial connection structure shows the characteristics of “core polarization, edge collapse and multi-center germination”. The research results provide important enlightenment for formulating differentiated sustainable development strategies for urban agglomerations in ecologically fragile areas. Full article

Sulfur dioxide not only affects the ecological environment and endangers health but also restricts economic development. The reasonable prediction of sulfur dioxide emissions is beneficial for formulating more comprehensive energy use strategies and guiding social policies. To this end, this article uses a multiparameter combination optimization gray prediction model (PGM(1, N)), which not only defines the difference between the sequences represented by variables but also optimizes the order of all variables. To this end, this article proposes an improved algorithm for the Dung Beetle Optimization (DBO) algorithm, namely, CSLDDBO, to optimize two important parameters in the model, namely, the smoothing generation coefficient and the order of the gray generation operators. In order to overcome the shortcomings of DBO, four improvement strategies have been introduced. Firstly, the use of a chain foraging strategy is introduced to guide the ball-rolling beetle to update its position. Secondly, the rolling foraging strategy is adopted to fully conduct adaptive searches in the search space. Then, learning strategies are adopted to improve the global search capabilities. Finally, based on the idea of differential evolution, the convergence speed of the algorithm was improved, and the ability to escape from local optima was enhanced. The superiority of CSLDDBO was verified on the CEC2022 test set. Finally, the optimized PGM(1, N) model was used to predict China’s sulfur dioxide emissions. From the results, it can be seen that the error of the PGM(1, N) model is the smallest at 0.1117%, and the prediction accuracy is significantly higher than that of other prediction models. Full article

This paper proposes a formal descriptive model for understanding the evolution of characters in detective fiction from the 19th to the 20th century, using methodologies and technologies from the Semantic Web. The integration of Digital Humanities within the theory of comparative literature opens new paths of study that allow for a digital approach to the understanding of intertextuality through close reading techniques and ontological modelling. In this research area, the variety of possible textual relationships, the levels of analysis required to classify these connections, and the inherently referential nature of certain literary genres demand a structured taxonomy. This taxonomy should account for stylistic elements, narrative structures, and cultural recursiveness that are unique to literary texts. The detective figure, central to modern literature, provides an ideal lens for examining narrative intertextuality across the 19th and 20th centuries. The analysis concentrates on character traits and narrative functions, addressing various methods of rewriting within the evolving cultural and creative context of authorship. Through a comparative examination of a representative sample of detective fiction from the period under scrutiny, the research identifies mechanisms of (meta)narrative recurrence, transformation, and reworking within the canon. The outcome is a formal model for describing narrative structures and techniques, with a specific focus on character development, aimed at uncovering patterns of continuity and variation in diegetic content over time and across different works, adaptable to analogous cases of traditional reworking and narrative fluidity. Full article

Wind turbine blades feature complex geometries and operate under harsh conditions, including high curvature gradients, nonlinear deformations, elevated humidity, and particulate contamination. This study presents the design and kinematic analysis of a novel climbing robot based on a 10R folding metamorphic mechanism. The robot employs a hybrid wheel-leg drive and adaptively reconfigures between rectangular and hexagonal topologies to ensure precise adhesion and efficient locomotion along blade leading edges and windward surfaces. A high-order kinematic model, derived from a modified Grubler–Kutzbach criterion augmented by rotor theory, captures the mechanism’s intricate motion characteristics. We analyze the degrees of freedom (DOF) and motion branch transitions for three representative singular configurations, elucidating their evolution and constraint conditions. A scaled-down prototype, integrating servo actuators, vacuum adhesion, and multi-modal sensing on an MDOF control platform, was fabricated and tested. Experimental results demonstrate a configuration switching time of 6.3 s, a single joint response time of 0.4 s, and a maximum crawling speed of 125 mm/s, thereby validating stable adhesion and surface tracking performance. This work provides both theoretical insights and practical validation for the intelligent maintenance of wind turbine blades. Full article

As protected areas (PAs) expand globally at an accelerating rate, reconciling biodiversity conservation with socioeconomic development in adjacent communities has become a critical challenge for landscape sustainability. This systematic review synthesizes literature (1990–2025) to trace three interconnected transitions: (1) the conceptual evolution from exclusionary to inclusive PA–community paradigms, grounded in shifting perceptions of cultural landscapes; (2) the governance transformation from tokenistic participation to power-sharing co-management frameworks; and (3) the spatial planning progression from fragmented “island” models to integrated protected area networks (PANs) leveraging ecological corridors. Our analysis reveals that disconnected PA–community relationships exacerbate conservation–development conflicts, particularly where cultural landscapes are undervalued. A key finding is that cultural–natural synergies act as pivotal mediators for conservation efficacy, necessitating context-adaptive governance approaches. This study advances landscape planning theory by proposing a rural landscape network framework that integrates settlement patches, biocultural corridors, and PA matrices to optimize ecological connectivity while empowering communities. Empirical insights from China highlight pathways to harmonize stringent protection with rural revitalization, underscoring the capacity of PANs to bridge spatial and socio-institutional divides. This synthesis provides a transformative lens for policymakers to scale locally grounded solutions across global conservation landscapes. Full article

Understanding the evolutionary and geographic trajectories of crop wild relatives is vital for enhancing agro-biodiversity and advancing climate-resilient agriculture. This study focuses on ten Vicia L. taxa—comprising five species, four varieties, and one subspecies—of significant agricultural importance in Türkiye. An integrative molecular framework was applied, incorporating nuclear ITS sequence data, ITS2 secondary structure modeling, phylogenetic network analysis, and time-calibrated biogeographic reconstruction. This approach revealed well-supported clades, conserved secondary structural elements, and signatures of reticulate evolution, particularly within the Vicia sativa L. and V. villosa Roth. complexes, where high genetic similarity suggests recent divergence and possible hybridization. Anatolia was identified as both a center of origin and a dispersal corridor, with divergence events estimated to have occurred during the Late Miocene–Pliocene epochs. Inferred migration routes extended toward the Balkans, the Caucasus, and Central Asia, corresponding to paleoenvironmental events such as the uplift of the Anatolian Plateau and the Messinian Salinity Crisis. Phylogeographic patterns indicated genetic affiliations between Turkish taxa and drought-adapted Irano-Turanian lineages, offering valuable potential for climate-resilient breeding strategies. The results establish a molecularly informed foundation for conservation and varietal development, supporting sustainability-oriented innovation in forage crop systems and contributing to regional food security. Full article

To explore the spatiotemporal evolution of extreme temperature events in Guangxi (1940–2023), reveal regional response mechanisms, and assess future trends of persistence under climate warming, a multi-scale analysis was conducted using ERA5 reanalysis data. Methodologies included RH tests for homogeneity correction, collaborative kriging for data optimisation, Mann–Kendall tests for trend and abrupt change detection, Morlet wavelet analysis for cyclic pattern identification, Exploratory Spatio-Temporal Data Analysis (ESTDA) for spatial heterogeneity quantification, and Rescaled Range (R/S) analysis to calculate Hurst indices for future persistence assessment. Results showed the following: (1) The ERA5 dataset exhibited high applicability in Guangxi (R = 0.9989, RMSE = 1.9492 °C), supporting robust evidence of continuous warming—warm indices (e.g., SU25, TX90p) increased significantly (SU25 at 0.2044 d/10a), while cold indices (e.g., TN10p, FD0) declined (TN10p at −0.0519 d/10a); abrupt changes of cold indices were concentrated in 1942–1950, with warm indices accelerating post-2000 and TXx exhibited the highest warming rate (0.23 °C/decade). (2) Extreme temperature indices displayed a primary 19–21-year oscillation cycle (dominant in warm indices) and a secondary 13-year cycle (prominent in cold indices). (3) Spatial heterogeneity featured northwest–southeast cold–heat inversion, coastal–inland intensity gradients, and latitudinal zonation of extreme indices; ESTDA revealed intensified polarisation, with warm indices clustering in low-latitude regions (e.g., Baise) and cold indices declining homogeneously in mountainous areas (e.g., Guilin), indicating an irreversible transition to a warming steady state. (4) R/S analysis indicated all indices had Hurst indices of 0.65–0.92, reflecting persistent future trends consistent with historical evolution, with warm indices (e.g., TNn, SU25) showing stronger persistence (H > 0.85). This work clarifies the spatial polarisation mechanism and future persistence of extreme temperature dynamics in Guangxi, providing a multi-scale scientific basis for disaster early warning and adaptation planning in climate-sensitive karst-monsoon regions. Full article

The emergence of mass personalized production has increased the adaptability and intelligence requirements of welding robots. To address the challenges associated with mass personalized production, this paper proposes a novel knowledge-driven framework for intelligent welding process planning in cloud robotics systems. This framework integrates cloud-edge-end collaborative computing with ontology-based knowledge representation to enable efficient welding process optimization. A hierarchical knowledge-based architecture was developed using the SQLite 3.38.0, Redis 5.0.4, and HBase 2.1.0 tools. The ontology models formally define the welding tasks, resources, processes, and results, thereby enabling semantic interoperability across heterogeneous systems. A hybrid knowledge evolution method that combines cloud-based welding simulation and transfer learning is presented as a means of achieving inexpensive, efficient, and intelligent evolution of welding process knowledge. Experiments demonstrated that, with respect to pure cloud-based solutions, edge-based knowledge bases can reduce the average response time by 86%. The WeldNet-152 model achieved a welding parameter prediction accuracy of 95.1%, while the knowledge evolution method exhibited a simulation-to-reality transfer accuracy of 78%. The proposed method serves as a foundation for significant enhancements in the adaptability of welding robots to Industry 5.0 manufacturing environments. Full article

With the rapid evolution of technology and the continuous deepening of digital transformation in education, personalized and adaptive learning have emerged as inevitable trends in the educational landscape. This study focuses on a Computerized Adaptive Learning System Based on Cognitive Diagnosis (CAL-CDS)—an integrated platform that incorporates multiple technologies for assessment and learning. The study is organized around two dimensions: (1) constructing a foundational cognitive diagnostic assessment framework, and (2) investigating the operational mechanisms of the cognitive diagnosis-based computerized adaptive system. It comprehensively incorporates core components including cognitive modeling, Q-matrix generation, and diagnostic test development. On this basis, this study dissects the system’s operational logic from four aspects: the adaptive testing system, diagnostic system, recommendation system, and empirical case studies. This study effectively addresses two core questions: how to construct a cognitive diagnostic assessment framework that alignes with China’s mathematics knowledge structure, and how to facilitate personalized student learning via cognitive diagnosis. Overall, this study offers a systematic solution for developing mathematics-specific cognitive diagnosis-driven adaptive learning systems. Full article

The rapid proliferation of Chat Generative Pre-trained Transformer (ChatGPT) marks a pivotal moment in artificial intelligence, eliciting responses from academic shock to industrial awe. As these technologies advance from passive tools toward proactive, agentic systems, their transformative potential and inherent risks are magnified globally. This paper presents a comprehensive, critical review of ChatGPT’s impact across five key domains: natural language understanding (NLU), content generation, knowledge discovery, education, and engineering. While ChatGPT demonstrates profound capabilities, significant challenges remain in factual accuracy, bias, and the inherent opacity of its reasoning—a core issue termed the “Black Box Conundrum”. To analyze these evolving dynamics and the implications of this shift toward autonomous agency, this review introduces a series of conceptual frameworks, each specifically designed to illuminate the complex interactions and trade-offs within these domains: the “Specialization vs. Generalization” tension in NLU; the “Quality–Scalability–Ethics Trilemma” in content creation; the “Pedagogical Adaptation Imperative” in education; and the emergence of “Human–LLM Cognitive Symbiosis” in engineering. The analysis reveals an urgent need for proactive adaptation across sectors. Educational paradigms must shift to cultivate higher-order cognitive skills, while professional practices (including practices within education sector) must evolve to treat AI as a cognitive partner, leveraging techniques like Retrieval-Augmented Generation (RAG) and sophisticated prompt engineering. Ultimately, this paper argues for an overarching “Ethical–Technical Co-evolution Imperative”, charting a forward-looking research agenda that intertwines technological innovation with vigorous ethical and methodological standards to ensure responsible AI development and integration. Ultimately, the analysis reveals that the challenges of factual accuracy, bias, and opacity are interconnected and acutely magnified by the emergence of agentic systems, demanding a unified, proactive approach to adaptation across all sectors. Full article

Sex-biased gene expression is a fundamental driver of sexual dimorphism and plays an important role in evolutionary divergence across species. Here, we conducted a comparative evolutionary analysis of sex-biased genes (SBGs) in 13 insect species spanning four major orders: Hemiptera, Diptera, Lepidoptera, and Coleoptera. In total, 42,488 SBGs were identified, with low interspecific conservation—86.5% of orthogroups were shared by six or fewer species. Phylogenetic analyses showed that SBGs generally follow evolutionary trajectories similar to other protein-coding genes but show branch length differences suggestive of accelerated evolution in Drosophila virilis and Tribolium castaneum. Male-biased genes were more conserved than female-biased genes, which often showed reversed expression patterns, particularly in T. castaneum. Functional annotation of three rapidly evolving orthogroups in T. castaneum indicates possible associations with sex-specific traits such as lifespan, muscle development, and immunity, which should be considered hypotheses for future functional validation. These findings highlight the complex evolutionary dynamics of sex-biased genes and their contributions to phenotypic diversity and adaptive evolution in insects, providing a basis for future studies on the molecular mechanisms of sexual dimorphism and lineage-specific adaptations. Full article

This paper proposes an optimal-sliding-mode-based adaptive dynamic programming (ADP) master–slave synchronous control strategy for the actuator saturation and performance constraints that AUVs face in dynamic recovery. First, by introducing the sliding-mode function into the value function to optimize the state error and its derivative simultaneously, the convergence speed is significantly improved. Second, by designing the performance constraint function to directly map the sliding-mode function, the evolution trajectory of the sliding-mode function is constrained, ensuring the steady-state and transient characteristics. In addition, the hyperbolic tangent function (tanh) is introduced into the value function to project the control inputs into an unconstrained policy domain, thereby eliminating the phase lag inherent in conventional saturation compensation schemes. Finally, the requirement for initial stability is relaxed by constructing a single-critic network to approximate the optimal control policy. The simulation results show that the proposed method has significant advantages in terms of the position and attitude synchronization error convergence rate, steady-state accuracy, and control signal continuity compared with the conventional ADP method. Full article

Gerronema lapidescens (Lei Wan), a valued medicinal basidiomycete traditionally employed for antiparasitic and digestive ailments, faces severe conservation threats due to unsustainable wild harvesting and the absence of reliable cultivation protocols. To address this crisis and unlock its pharmacotherapeutic potential, we present the first chromosome-scale genome assembly and comprehensive methylome profile for the wild strain G. lapidescens QL01, domesticated from the Qinling Mountains. A multi-platform sequencing strategy (Illumina and PacBio HiFi) yielded a high-quality 82.23 Mb assembly anchored to 11 chromosomes, exhibiting high completeness (98.4% BUSCO) and 46.03% GC content. Annotation predicted 15,847 protein-coding genes, with 81.12% functionally assigned. Genome-wide analysis identified 8.46 million high-confidence single-nucleotide polymorphisms (SNPs). Notably, methylation profiling revealed 3.25 million methylation events, with elevated densities on chromosomes 4, 9, and 10, suggesting roles in gene silencing and environmental adaptation. Phylogenomic analyses clarified the evolutionary status of G. lapidescens, whilst gene family evolution indicated moderate dynamics reflecting niche adaptation. Carbohydrate-Active enzymes (CAZymes) analysis identified 521 enzymes, including 211 Glycoside Hydrolases (GHs), consistent with organic matter degradation. Additionally, 3279 SSRs were catalogued as molecular markers. This foundational resource elucidates G. lapidescens’s genetic architecture, epigenetic regulation, evolutionary history, and enzymatic toolkit, underpinning future research into medicinal compound biosynthesis, environmental adaptation, germplasm conservation, and sustainable cultivation. Full article

The Anthropocene, marked by rapid and extensive environmental changes, poses distinct evolutionary pressures and opportunities for species adaptation. Insects, among the most diverse and resilient taxa, exhibit notable dietary plasticity and the ability to convert low-value biomass—such as agro-industrial and urban waste—into usable nutrients. This review explores how these traits serve as adaptive strategies, enabling insects to thrive and expand into novel, human-altered habitats. We examine the evolution of insect nutritional requirements and how alternative diets influence physiological, behavioral, and reproductive traits, ultimately enhancing resilience to anthropogenic stressors. The capacity of insects to metabolize diverse substrates not only supports their role in food security and circular economy initiatives but also provides valuable insights into detoxification pathways and metabolic flexibility in environments rich in xenobiotics. By synthesizing key studies, we highlight the pivotal role insects play in redefining ecosystem functions under human influence. This review underscores the intersection of nutritional and evolutionary biology in understanding insect success in the Anthropocene, emphasizing the importance of nutritional knowledge for both ecological research and applied insect farming systems. Full article