Search Results (12,272)

Driven by the rapid proliferation of generative artificial intelligence, the computing power industry is undergoing a paradigm shift from traditional linear supply chains toward complex, interdependent innovation ecosystems. This study investigates the evolutionary dynamics of the computing power ecosystem, specifically examining the strategic interplay between antitrust regulation and vertical integration. We construct a tripartite evolutionary game framework involving the government regulators, leading computing power incumbents, and downstream AI innovators. By deriving evolutionarily stable strategies, we analyze the underlying mechanisms of system transitions and employ numerical simulations to explore key parametric sensitivities. The theoretical analysis suggests that the evolution of the AI computing power innovation ecosystem manifests distinct stage-based progressions and threshold-driven bifurcation characteristics—potentially transitioning from an initial efficiency-based state of “natural monopoly and passive dependence” during the industry’s emergence, through transitionary states such as the “comfort zone trap” or “regulatory stalemate” during the expansion phase, and ultimately converging toward a mature configuration of “co-opetition and endogenous growth.” The model suggests that downstream AI firms may benefit from advancing vertical integration, achieving hardware–software co-optimization through self-developed domain-specific architectures, The analysis further implies that the leading computing power firm could strengthen its ecological niche by opening its underlying interfaces and software stacks to maintain its ecological niche as the industry cornerstone in integrated form. For the government, it is necessary to establish precise dynamic intervention and orderly exit mechanisms. Full article

Cold stress (CS) represents a major environmental factor that adversely affects plant growth, development, and productivity. To cope with low-temperature conditions, plants have evolved sophisticated mechanisms for CS perception and response, mediated through complex cellular signaling networks and physiological processes. Central to these adaptive responses are phytohormones, which function either independently or through synergistic and antagonistic interactions to fine-tune CS tolerance. This review synthesizes current knowledge on the roles of major classical phytohormones and signaling metabolites in regulating CS tolerance in plants. We first outline the molecular mechanisms involved in CS sensing and signal transduction, highlighting the roles of membrane-associated sensors, calcium signaling, and downstream transcriptional networks. Then, we discuss the contributions of key classical phytohormones, including auxin, abscisic acid, ethylene, salicylic acid, cytokinin, jasmonic acid, brassinosteroids, gibberellic acid, strigolactones, and signaling metabolites, including melatonin and gamma-aminobutyric acid, to CS tolerance, highlighting their individual and interacting roles in modulating gene expression regulation, antioxidant defense and physiological adaptations. We also discuss the crosstalk between these hormones, emphasizing the dynamic and often context-dependent nature of their interactions in response to CS. Furthermore, the review highlights recent advances in CRISPR/Cas9-based genome editing strategies targeting phytohormone biosynthesis, signaling, and response pathways to improve CS tolerance in plants. By integrating hormonal signaling, molecular regulation, and modern biotechnological tools, this review provides a comprehensive framework for understanding phytohormone-mediated CS adaptation and offers perspectives for developing climate-resilient crops through genetic and agronomic approaches. Full article

Despite the rapid financial expansion over the past two decades, South Africa’s economic growth has remained sluggish, raising concerns about the disconnect between financial sector development and overall economic performance. This study aims to investigate the relationship between financialization and economic growth in South Africa using three proxy variables, finance, insurance, real estate, and business services as a percentage of GDP; money supply (M3) as a percentage of GDP; and credit to the private sector as a percentage of GDP, alongside a composite financialization indicator. Using quarterly time-series data from 1994Q1 to 2025Q2, this study employs the autoregressive distributed lag (ARDL) approach to examine both short- and long-term dynamics and cointegration between financialization and economic growth. The empirical findings reveal that financialization exerts a positive and statistically significant influence on South Africa’s economic growth. Meanwhile, the estimation results reveal that financialization has a positive and highly significant impact on economic growth in South Africa, demonstrating the need for policies that promote and enhance its effects. Full article

In recent years, plant stress biology has moved beyond single-pathway descriptions toward an integrated framework in which stress perception, hormonal control, and gene regulation are tightly interconnected. Early events such as membrane-associated sensing, calcium influx, reactive oxygen species (ROS) generation, and kinase activation converge with phytohormonal networks to shape context-dependent responses. Within this framework, abscisic acid, salicylic acid, jasmonates, ethylene, auxin, cytokinins, gibberellins, brassinosteroids, and strigolactones function not as isolated regulators but as components of a dynamic signaling matrix that balances survival, defense, growth restraint, and recovery. These hormonal signals are ultimately translated into adaptive outcomes through extensive transcriptional and post-transcriptional reprogramming mediated by transcription factors, RNA-based regulators, chromatin remodeling, and stress memory mechanisms. This review synthesizes current understanding of how plants integrate stress perception, phytohormonal crosstalk, and transcriptional regulation to establish stress tolerance. We first examine the molecular basis of stress sensing and early signaling. We then discuss the central functions of major phytohormones and the logic of hormone–hormone interaction networks in coordinating stress adaptation. Next, we analyze transcriptional, post-transcriptional, and epigenetic mechanisms that determine response specificity, intensity, and persistence. We further highlight points of convergence between abiotic and biotic stress responses and discuss how combined stresses challenge traditional single-stress models. Finally, we consider the roles of omics, systems biology, and translational technologies in decoding and engineering stress-resilient phenotypes. By integrating these perspectives, this review presents plant stress tolerance as a multilevel systems property and outlines key priorities for future research aimed at developing climate-resilient crops. Full article

Interleukin-5 (IL-5) has long been positioned as a lineage-restricted cytokine primarily responsible for eosinophil differentiation and survival. However, emerging mechanistic and clinical evidence supports a broader conceptual shift: IL-5 should no longer be viewed solely as an eosinophil growth factor, but as a context-dependent regulator embedded within dynamic airway immune networks. Drawing on advances in eosinophil subset biology, receptor signaling, and tissue-level immune crosstalk, this review reframes IL-5 biology through the lens of systems-level inflammatory regulation across airway and systemic eosinophilic diseases. Recent data reveal functional heterogeneity between resident and inflammatory eosinophil subsets, challenging the assumption that blood eosinophilia uniformly reflects pathogenic activity. In parallel, functional IL-5 receptor expression has been identified on multiple structural and immune cell populations—including epithelial cells, mast cells, plasma cells, basophils, neutrophils, and fibroblasts—supporting a broader tissue-signaling paradigm. Experimental and translational studies further link IL-5 to epithelial integrity, airway remodeling, and mucus pathology, suggesting structural and network-level effects beyond simple eosinophil depletion. Comparative analyses across asthma, chronic rhinosinusitis with nasal polyps, and COPD demonstrate that eosinophilic inflammation is biologically heterogeneous and context-dependent. While IL-5-targeted therapies yield consistent benefit in severe asthma, therapeutic responses in other airway diseases appear to be shaped by local tissue architecture and mixed inflammatory programs. Together, these observations illustrate a paradigm shift from pathway-specific inhibition toward network-informed disease control and highlight key areas for future mechanistic investigation. Full article

Rapid expansion in urbanisation, along with the rising demand for energy consumption, has deepened environmental apprehensions among developing economies and intensified their concerns about long-run environmental sustainability. This article examines how urban expansion and rising energy consumption impact environmental sustainability, and whether environmental taxes moderate this relationship, by using a panel of 110 developing countries over the period of 2010 to 2024. To capture both static and dynamic relationships among the variables, we have applied complementary econometric methodologies that allow for cross-country heterogeneity and persistence in emissions. The estimated outcomes show that urban expansion and energy consumption are significantly increasing gas emissions, and this outcome is consistent with the idea that environmental costs of urban-led growth and energy-intensive development. But as we have added environmental taxes as a moderating policy instrument, the positive impact of energy consumption and urbanisation on emissions becomes negative in most specifications. The significant impact of both interaction terms, i.e., environmental taxes and urbanisation, and environmental taxes and energy consumption, across different estimation strategies, suggests that environmental taxation weakens emissions and encourages structural change with rising energy use. Renewable energy consumption and foreign direct investment have significant influences on emissions, emphasising the role of energy structure and investment composition in shaping environmental outcomes, whereas the income effect varies across models. The outcomes of dynamic models also confirm emissions persistence, but over time, environmental taxes reduce the degree of emissions persistence. The estimated outcomes imply that environmental taxes can support a decoupling of urbanisation and energy-driven growth from environmental degradation. Thus, developing countries should balance urban development, energy demand, and environmental sustainability through credible market-based regulations. Full article

Eastern Thailand serves as a critical case study for the escalating tension between agricultural preservation and urban expansion, a dynamic recently intensified by the COVID-19 pandemic. This study addresses a pivotal research question: To what extent do emerging socio-economic realities, such as policy shifts, labor fluctuations, and climatic extremes, alter the spatiotemporal continuity of urban expansion? Employing a mixed-methods approach, we integrated multi-stakeholder insights with quantitative spatial modeling to simulate context-specific land use futures through 2030. Qualitative findings indicate that while COVID-19 accelerated agricultural modernization, evidenced by increased mechanization and e-commerce integration, these shifts have limited long-term impact on land use patterns. Instead, regional policy, climate change, and technological innovation emerged as the primary drivers of landscape transformation. Quantitative simulations reveal that urban growth will concentrate in the western provinces bordering Bangkok and the southern coastal corridors of Chon Buri and Rayong. Crucially, across all scenarios, approximately 60% of new urban land is projected to be converted from existing croplands, followed by significant losses in natural forest cover. These results demonstrate that current growth-oriented policies may undermine regional food security and ecosystem services. This study provides a framework for balancing agricultural modernization with ecological preservation, offering essential evidence for developing the integrated, sustainability-focused land use frameworks required to meet 2030 development goals. Full article

Accurate forecasting of sediment accumulation under extreme hydrodynamic forcing is essential for coastal engineering design and harbor management. This study evaluates the performance of Dynamic Mode Decomposition (DMD), optimized DMD (optDMD), and optimized DMD with stability constraints (optDMDs) for reconstructing and forecasting sediment accumulation height fields at the Dilderesi River mouth under a 50-year return period flood scenario. Sediment height fields generated using Delft3D are represented through reduced-order modal decompositions and the truncation rank is determined based on reconstruction-error analysis. Although all formulations reproduce the training data with negligible error, their predictive behavior differs during temporal extrapolation. Standard DMD exhibits rapid error growth at longer lead times. The optDMD formulation improves short- and intermediate-horizon performance but shows gradual degradation at extended lead times. Optimized DMD with stability constraints provides the most consistent long-horizon forecasts, maintaining high Nash–Sutcliffe efficiency and low RMSE across the full 9 h prediction interval. Examination of the continuous-time eigenvalue distributions and modal dynamics indicates that spectral characteristics of the reduced-order representation govern forecast robustness. The results demonstrate that enforcing spectral stability within reduced-order frameworks substantially enhances morphodynamic forecasting reliability under extreme flood conditions. The proposed approach provides a computationally efficient and physically consistent tool for sediment dynamics prediction in coastal engineering applications. Full article

This paper develops a tractable theoretical framework to study how network participation shapes the boom–bust dynamics of non-fungible token (NFT) prices. We model NFT pricing under network effects and heterogeneous consumers, and show that prices and participation are jointly determined in equilibrium. The model implies a critical participation threshold that separates expansion from contraction regimes: above this threshold, positive feedback between participation and valuation generates self-reinforcing growth, while below it, weakening network benefits lead to contraction. We provide empirical evidence using data from the aggregate NFT market and prominent collections including Bored Ape Yacht Club (BAYC) and CryptoPunks. Reduced-form regressions show a positive association between prices and network participation, with stronger effects at the collection level than in the aggregate market. Threshold estimation further provides evidence consistent with regime-dependent dynamics, with clearer tipping behaviour in well-defined NFT communities than in the aggregate market. These findings suggest that NFT valuation is closely tied to network structure and participation dynamics. More broadly, this paper contributes a unified framework that links participation, price formation, and threshold behaviour in NFT markets. Full article

Background: Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality despite major advances in diagnostics and therapies. The prognosis remains poor, mostly due to late-stage presentation and molecular heterogeneity. Epidermal growth factor receptor (EGFR) mutations are common drivers of NSCLC. The development of EGFR tyrosine kinase inhibitors (TKIs) has significantly improved outcomes in patients with EGFR mutations. Variant allele frequency (VAF) is a quantitative genomic measure representing the proportion of sequencing reads harboring a given mutation. In NSCLC tissue, the EGFR mutation VAF reflects tumor clonality and intratumoral heterogeneity, and accumulating evidence suggests an association between EGFR VAF and response to EGFR-targeted TKIs. Methods: To address the limited synthesis of data on the relevance of EGFR mutation VAF in NSCLC, we conducted a narrative review of the literature using PubMed/MEDLINE and Embase databases and current clinical guidelines, synthesizing available evidence on EGFR VAF, including its biological, molecular, and therapeutic implications in EGFR-mutated disease. The review was structured in accordance with the SANRA (Scale for the Assessment of Narrative Review Articles) checklist. Results: EGFR VAF and on-treatment VAF dynamics are consistently associated with treatment response, progression-free survival, and overall survival in osimertinib-treated NSCLC. Baseline VAF enables risk stratification, early clearance kinetics predict durable benefit, and longitudinal VAF monitoring facilitates early detection of resistance. Importantly, the prognostic implications of VAF differ fundamentally between tissue-based and plasma-based measurements: high tissue VAF reflects clonal homogeneity and predicts favorable TKI response, whereas high plasma VAF indicates elevated tumor burden and is associated with inferior outcomes. In the second-line setting, the T790M/activating mutation ratio serves as a surrogate for resistance clonality and independently predicts osimertinib efficacy. Conclusions: EGFR VAF represents a promising dynamic molecular biomarker for treatment monitoring and precision decision-making in EGFR-mutated NSCLC. Full article

This study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture. Data were collected using a GNSS antenna placed within an experimental meadow located in NW Italy. GNSS-IR exploits the interference between direct and ground-reflected signals to derive physical parameters such as the vegetation phase center height and soil moisture. In this work, by analyzing and modeling the oscillations in SNR time series, the sensitivity to crop growth dynamics was assessed. Vegetation height and dielectric parameters were compared against corresponding ground-surveyed values collected using a ruler and buried soil moisture sensors. Results suggest that GNSS-IR can detect canopy height with a high degree of consistency (Pearson’s r = 0.89, MAPE = 18%). Results also show that changes in the amplitude and phase of the interference pattern are sensitive to biomass density and dielectric properties of the reflecting surface (r = −0.81 and r = 0.86 respectively). GNSS-IR observables were analyzed across four representative measurement campaigns capturing distinct seasonal stages of meadow development. Despite the limited temporal sampling (n = 4), the selected observations correspond to contrasting vegetation and soil moisture conditions, allowing the identification of systematic variations in crop biophysical properties. These findings open promising perspectives for the development of innovative monitoring strategies in precision agriculture, leveraging existing GNSS infrastructure to obtain key biophysical parameters with minimal additional equipment and operational complexity. Full article

Miang, a traditional fermented tea produced from Camellia sinensis var. assamica, is of notable cultural and socio-economic relevance in Northern Thailand. Traditionally, the non-filamentous fungi-based process (NFP) in western Lanna uses only young tea leaves, resulting in substantial amounts of mature leaves being discarded as agricultural waste. This study aimed to utilize the mature tea leaves by adapting the filamentous fungi growth-based process (FFP) of eastern Lanna using selected tannin-tolerant microorganisms, including Aspergillus niger MLF3, Cyberlindera rhodanensis P3, and Lactiplantibacillus pentosus A14-6. Study on fermentation dynamics and bioactive compound formation based on a 2-step fermentation process: 3-day solid-state fermentation with A. niger MLF3, followed by 7-day submerged fermentation by co-culture of C. rhodaninsis P3, and L. pentosus A14-6 in 500 mL sterile distilled water at 30 °C. Increased activities of polysaccharide-degrading enzymes and organic acids were clearly observed during solid-state fermentation, while the significant changes in polyphenol, antioxidant, and reducing sugar content in cell-free supernatant (CFS) were found after submerged fermentation. The obtained CFS shows inhibitory effects of 90 ± 2.5% and 95 ± 1.8% on α-glucosidase and α-amylase, respectively. Analysis of CFS by E-tongue and E-nose clearly indicated the influence of microbial mixture on the taste and aroma of the fermented products. These results demonstrate not only a high-yielding strategy for the effective biotransformation of mature tea leaves into functional drink products but also significant implications for reducing agricultural waste. Full article

Whether finance leads growth or growth leads finance remains a century-long debate. We argue that the direction and strength of the GDP–credit nexus are context-dependent and can be systematically uncovered by conditioning causal analysis on macro-structural heterogeneity across countries. We implement a two-stage pipeline: (i) unsupervised clustering of 30 economies (2005–2022, five annual macro indicators) via Agglomerative Clustering to form homogeneous macro-structural groups; and (ii) within-cluster dynamic causal analysis using lagged correlations, Granger causality and explanatory models (quarterly GDP and private credit, year-on-year growth, 1Q2005–3Q2024). Results show non-universality of causality: (a) in “developed and in transition, economically stable” economies, credit → GDP is predominant; (b) in “highly developed, competitive and stable” economies, bidirectionality is predominant; however, the results are not economically intuitive; (c) in “emerging/intermediate with macro risk”, bidirectional links are common, and feedback between both variables is interpretable across distinct scenarios. Post hoc Lasso and XGBoost confirm effect magnitudes and non-linear thresholds. We contribute a macro-segmented causal discovery framework that reconciles conflicting findings in the literature and provides policy-relevant differentiation by economic context. Full article

Intracranial aneurysms are common vascular lesions with a highly variable natural history. While most unruptured intracranial aneurysms remain stable throughout life, a biologically aggressive subset progresses to growth and rupture, resulting in aneurysmal subarachnoid hemorrhage with substantial morbidity and mortality. Contemporary evidence demonstrates that aneurysm behavior is dynamic rather than static and reflects the interaction of hemodynamic forces, inflammatory vascular remodeling, genetic susceptibility, and environmental risk factors. Rupture risk is not constant over time and may be highest early after aneurysm formation, followed by a period of relative quiescence in selected lesions. Traditional population-based risk estimates have therefore evolved toward individualized risk stratification incorporating aneurysm size, location, morphology, growth, patient-specific factors, and emerging imaging and computational biomarkers. This chapter reviews the epidemiology, pathobiology, growth patterns, and rupture risk of intracranial aneurysms, integrating foundational observational studies with recent advances in genetics, vessel wall imaging, and predictive modeling. Understanding the natural history of brain aneurysms is essential for balancing the risks of observation against intervention and for guiding future innovations in aneurysm management. Full article

Urbanization, together with shifts in rainfall patterns, has become an increasingly important driver of hydrological extremes in many rapidly developing tropical regions. In the Cimanceuri River Basin, Tangerang Regency, Indonesia, these processes have intensified over the last decade, raising concerns regarding flood risk. This study examines the combined influence of urban expansion and rainfall variability on flood dynamics over 2013–2025. Multi temporal land use classification based on Landsat imagery indicates a pronounced growth of impervious surfaces, primarily driven by rapid urban development and the conversion of agricultural land. To assess the hydrological consequences of these changes, rainfall–runoff processes and flood inundation were simulated using the Soil Conservation Service Curve Number (SCS–CN) method within a coupled HEC-HMS and HEC-RAS 2D modelling framework. Simulations were performed for multiple temporal conditions and design rainfall scenarios. Model calibration relied on observed flood events recorded in March 2025 in the Mustika Residential Area, Tangerang. The results suggest that urbanization has contributed to measurable increases in both peak discharge and inundation extent. Between 2013 and 2025, impervious surface coverage expanded by approximately 67%, accompanied by a rise in the composite Curve Number from 85.86 to 86.63 and an estimated 5.2% increase in flood extent. Also, the design rainfall increased from 85.01 to 90.95 with an average increase of 7.34%. Comparison between simulated inundation patterns and aerial imagery shows satisfactory agreement, with an average deviation of less than 10%, indicating acceptable model performance. Hydrologic analyses generated two discharge scenarios, consisting of event-based flow from the 5 March 2025 rainfall data and return-period flows derived from design rainfall under different rainfall-shift periods. The rainfall-shift analysis quantified changes in design rainfall and corresponding discharge using progressively updated rainfall records. Together, the results emphasize the combined effects of urban expansion and shifting rainfall patterns on flood dynamics, underscoring the need for adaptive land-use planning and climate-responsive water management in rapidly urbanizing catchments. Full article